1
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Liu J, Maximov K, Kanold PO. Automated head-fixation training system with high levels of animal participation in psychoacoustic tasks. PLoS One 2025; 20:e0323114. [PMID: 40392886 PMCID: PMC12091756 DOI: 10.1371/journal.pone.0323114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2024] [Accepted: 04/02/2025] [Indexed: 05/22/2025] Open
Abstract
Many animal training paradigms rely on head-fixation. Head-fixation training is typically laborious and can benefit from automation to relieve the workload and reduce the variability in the training outcome. Several groups have reported successful implementations of such systems, but throughput varied greatly across groups. In addition, most studies relied on brief periods of head-fixation sessions (≤ 1 minute) to reduce the potential stress on the animal. Here, we report the design of a new system that could achieve head-fixation sessions on the order of minutes with a high participation rate from the animal (100%). Throughout the training period, each mouse performed a total of close to 40 minutes of head-fixation training on average each day and learned common psychoacoustic tasks, i.e., tone detection and tone discrimination. Our system can achieve highly efficient training with minimum idling time, allowing combinations with high-end neural recording equipment to achieve maximum training and data collection efficiency.
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Affiliation(s)
- Ji Liu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Biology, University of Maryland, College Park, Maryland, United States of America
| | - Kate Maximov
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Biology, University of Maryland, College Park, Maryland, United States of America
| | - Patrick O. Kanold
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Biology, University of Maryland, College Park, Maryland, United States of America
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, Maryland, United States of America
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2
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Jung F, Cao X, Heymans L, Carlén M. The DMC-Behavior Platform: An Open-Source Framework for Auditory-Guided Perceptual Decision-Making in Head-Fixed Mice. eNeuro 2025; 12:ENEURO.0457-24.2025. [PMID: 40210486 PMCID: PMC11984799 DOI: 10.1523/eneuro.0457-24.2025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2024] [Revised: 02/23/2025] [Accepted: 02/26/2025] [Indexed: 04/12/2025] Open
Abstract
Perceptual decision-making describes the process of selecting an appropriate action based on the sensory information present in the immediate environment and is hence an omnipresent factor in the life of animals. In preclinical research, a widespread approach to study the neuronal correlates of perceptual decision-making is to record (and manipulate) neuronal activity in head-fixed mice performing behavioral tasks. In contrast to the technologies used to record/manipulate neuronal activity, standardization of the behavioral training of mice is generally neglected, a circumstance that is particularly true for behavioral tasks involving auditory stimuli. Here, we present the DMC-Behavior Platform, an open-source, cost-efficient framework for training head-fixed mice in perceptual decision-making tasks involving auditory stimuli. Combining the DMC-Behavior Platform with strategies to record and manipulate neuronal activity offers many opportunities to test hypotheses on the neuronal underpinnings of cognitive processes. We demonstrate the utility of the platform by training mice on auditory variants of three behavioral tasks commonly used to study perceptual decision-making: a detection, a Go/NoGo, and a two-alternative forced choice task. The presented work establishes the seamless integration and synchronization of external devices to record neuronal activity, task-related, and nontask-related behavioral variables. Our platform provides a valuable tool for more standardized and reproducible investigations into the neuronal circuits underlying auditory-guided decision-making.
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Affiliation(s)
- Felix Jung
- Department of Neuroscience, Karolinska Institutet, Stockholm 171 77, Sweden
| | - Xiao Cao
- Department of Neuroscience, Karolinska Institutet, Stockholm 171 77, Sweden
| | - Loran Heymans
- Department of Neuroscience, Karolinska Institutet, Stockholm 171 77, Sweden
| | - Marie Carlén
- Department of Neuroscience, Karolinska Institutet, Stockholm 171 77, Sweden
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3
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Gilmer JI, Coltman SK, Cuenu G, Hutchinson JR, Huber D, Person AL, Al Borno M. A novel biomechanical model of the proximal mouse forelimb predicts muscle activity in optimal control simulations of reaching movements. J Neurophysiol 2025; 133:1266-1278. [PMID: 40098414 DOI: 10.1152/jn.00499.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2024] [Revised: 11/19/2024] [Accepted: 03/04/2025] [Indexed: 03/19/2025] Open
Abstract
Mice are key model organisms in neuroscience and motor systems physiology. Fine motor control tasks performed by mice have become widely used in assaying neural and biophysical motor system mechanisms. Although fine motor tasks provide useful insights into behaviors that require complex multi-joint motor control, there is no previously developed physiological biomechanical model of the adult mouse forelimb available for estimating kinematics, muscle activity, or kinetics during behaviors. Here, we developed a musculoskeletal model based on high-resolution imaging of the mouse forelimb that includes muscles spanning the neck, trunk, shoulder, and limbs. Physics-based optimal control simulations of the forelimb model were used to estimate in vivo muscle activity present when constrained to the tracked kinematics during reaching movements. The activity of a subset of muscles was recorded and used to assess the accuracy of the muscle patterning in simulation. We found that the synthesized muscle patterning in the forelimb model had a strong resemblance to empirical muscle patterning, suggesting that our model has utility in providing a realistic set of estimated muscle excitations over time when given a kinematic template. The strength of the similarity between empirical muscle activity and optimal control predictions increases as mice performance improves throughout learning of the reaching task. Our computational tools are available as open-source in the OpenSim physics and modeling platform. Our model can enhance research into limb control across broad research topics and can inform analyses of motor learning, muscle synergies, neural patterning, and behavioral research that would otherwise be inaccessible.NEW & NOTEWORTHY Investigations into motor planning and execution lack an accurate and complete model of the forelimb, which could bolster or expand on findings. We sought to construct such a model using high-detail scans of murine anatomy and prior research into muscle physiology. We then used the model to predict muscle excitations in a set of reaching movements and found that it provided accurate estimations and provided insight into an optimal-control framework of motor learning.
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Affiliation(s)
- Jesse I Gilmer
- Department of Computer Science and Engineering, Computational Bioscience Program, University of Colorado Denver | Anschutz Medical Campus, Denver, Colorado, United States
| | - Susan K Coltman
- Department of Kinesiology, The Pennsylvania State University, University Park, Pennsylvania, United States
| | - Geraldine Cuenu
- Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
| | - John R Hutchinson
- Department of Comparative Biomedical Sciences, Royal Veterinary College, London, United Kingdom
| | - Daniel Huber
- Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
| | - Abigail L Person
- Department of Physiology and Biophysics, University of Colorado Denver | Anschutz Medical Campus, Denver, Colorado, United States
| | - Mazen Al Borno
- Department of Computer Science and Engineering, Computational Bioscience Program, University of Colorado Denver | Anschutz Medical Campus, Denver, Colorado, United States
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4
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Linares-García I, Iliakis EA, Juliani SE, Ramirez AN, Woolley J, Díaz-Hernández E, Fuccillo MV, Margolis DJ. An Open-Source Joystick Platform for Investigating Forelimb Motor Control, Auditory-Motor Integration, and Value-Based Decision-Making in Head-Fixed Mice. eNeuro 2025; 12:ENEURO.0038-25.2025. [PMID: 40295100 PMCID: PMC12037168 DOI: 10.1523/eneuro.0038-25.2025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2025] [Revised: 03/18/2025] [Accepted: 03/28/2025] [Indexed: 04/30/2025] Open
Abstract
Investigation of neural processes underlying motor control requires behavioral readouts that capture the richness of actions, including both categorical (choice-based) information and motor execution (kinematics). We present an open-source platform for behavioral training of head-fixed mice that combines a stationary or retractable forelimb-based joystick, sound-presentation system, capacitive lick sensor, and water reward dispenser. The setup allows for the creation of multiple behavioral paradigms, two of which are highlighted here: a two-alternative forced-choice auditory-motor discrimination paradigm and a two-armed bandit value-based decision-making task. In the auditory-motor paradigm, mice learn to report high- or low-frequency tones by pushing or pulling the joystick. In the value-based paradigm, mice learn to push or pull the joystick based on the history of rewarded trials. In addition to reporting categorical choices, this setup provides a rich dataset of motor parameters that reflect components of the underlying learning and decision processes in both of these tasks. These kinematic parameters (including joystick speed and displacement, Fréchet similarity of trajectories, tortuosity, angular standard deviation, and movement vigor) provide key additional insights into the motor execution of choices that are not as readily assessed in other paradigms. The system's flexibility of task design, joystick readout, and ease of construction represent an advance compared with currently available manipulandum tasks in mice. We provide detailed schematics for constructing the setup and protocols for behavioral training using both paradigms, with the hope that this open-source resource is readily adopted by neuroscientists interested in mechanisms of sensorimotor integration, motor control, and choice behavior.
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Affiliation(s)
- Ivan Linares-García
- Department of Cell Biology and Neuroscience, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854
| | - Evan A Iliakis
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Sofia E Juliani
- Department of Cell Biology and Neuroscience, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854
| | - Alexandra N Ramirez
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Joel Woolley
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Edgar Díaz-Hernández
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Marc V Fuccillo
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - David J Margolis
- Department of Cell Biology and Neuroscience, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854
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5
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Amir A, Headley DB, Herzallah MM, Karki A, Kim IT, Paré D. Studying decision making in rats using a contextual visual discrimination task: Detection and prevention of alternative behavioral strategies. J Neurosci Methods 2025; 415:110346. [PMID: 39667672 DOI: 10.1016/j.jneumeth.2024.110346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2024] [Revised: 12/04/2024] [Accepted: 12/08/2024] [Indexed: 12/14/2024]
Abstract
BACKGROUND The neural bases of decision-making and contextual sensory discriminations have traditionally been studied in primates, highlighting the role of the prefrontal cortex in cognitive control and flexibility. With the advent of molecular tools to manipulate and monitor neuronal activity, these processes have increasingly been studied in rodents. However, rodent tasks typically consist of two-alternative forced choice paradigms that usually feature coarse sensory discriminations and no contextual dependence, limiting prefrontal involvement in task performance. NEW METHOD To circumvent these limitations, we developed a novel contextual visual discrimination task that lends itself to rigorous psychophysical analyses. In this task, rats learn to detect left-right differences in one dimension (e.g. luminance or speed) depending on context while ignoring another (e.g. speed or luminance, respectively). Depending on trials, speed and luminance can be greater on the same side (congruent trials) or on opposite sides (incongruent trials). RESULTS Rats learned the task in four phases: nose-poking and lever-pressing (∼7 days), discriminating left-right differences in one dimension (∼20 days), discriminating left-right differences in a second dimension (∼10 days), and discriminating left-right differences in one of the two dimensions depending on context (∼2.5 months). A 20:80 ratio of congruent to incongruent trials is used to prevent rats from adopting alternative strategies. COMPARISON WITH EXISTING METHODS This task is comparable to contextual sensory discrimination tasks used in monkeys. Few equivalent tasks exist in rodents. CONCLUSIONS This task will allow investigators to use the full neuroscientific armamentarium to study contextual neural coding in the rat prefrontal cortex.
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Affiliation(s)
- Alon Amir
- Center for Molecular and Behavioral Neuroscience, Rutgers University - Newark, 197 University Ave, Newark, NJ 07102, USA
| | - Drew B Headley
- Center for Molecular and Behavioral Neuroscience, Rutgers University - Newark, 197 University Ave, Newark, NJ 07102, USA
| | - Mohammad M Herzallah
- Center for Molecular and Behavioral Neuroscience, Rutgers University - Newark, 197 University Ave, Newark, NJ 07102, USA; Palestinian Neuroscience Initiative, Al-Quds University, Jerusalem, Palestine
| | - Asriya Karki
- Center for Molecular and Behavioral Neuroscience, Rutgers University - Newark, 197 University Ave, Newark, NJ 07102, USA
| | - Ian T Kim
- Center for Molecular and Behavioral Neuroscience, Rutgers University - Newark, 197 University Ave, Newark, NJ 07102, USA
| | - Denis Paré
- Center for Molecular and Behavioral Neuroscience, Rutgers University - Newark, 197 University Ave, Newark, NJ 07102, USA.
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6
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Failor SW, Carandini M, Harris KD. Visual experience orthogonalizes visual cortical stimulus responses via population code transformation. Cell Rep 2025; 44:115235. [PMID: 39888718 DOI: 10.1016/j.celrep.2025.115235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Revised: 09/26/2024] [Accepted: 01/06/2025] [Indexed: 02/02/2025] Open
Abstract
Sensory and behavioral experience can alter visual cortical stimulus coding, but the precise form of this plasticity is unclear. We measured orientation tuning in 4,000-neuron populations of mouse V1 before and after training on a visuomotor task. Changes to single-cell tuning curves appeared complex, including development of asymmetries and of multiple peaks. Nevertheless, these complex tuning curve transformations can be explained by a simple equation: a convex transformation suppressing responses to task stimuli specifically in cells responding at intermediate levels. The strength of the transformation varies across trials, suggesting a dynamic circuit mechanism rather than static synaptic plasticity. The transformation results in sparsening and orthogonalization of population codes for task stimuli. It cannot improve the performance of an optimal stimulus decoder, which is already perfect even for naive codes, but it improves the performance of a suboptimal decoder model with inductive bias as might be found in downstream readout circuits.
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Affiliation(s)
- Samuel W Failor
- UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK.
| | - Matteo Carandini
- UCL Institute of Ophthalmology, University College London, London EC1V 9EL, UK
| | - Kenneth D Harris
- UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK.
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7
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Bissen D, Cary BA, Zhang A, Sailor KA, Van Hooser SD, Turrigiano GG. Prey capture learning drives critical period-specific plasticity in mouse binocular visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.01.28.635373. [PMID: 39975102 PMCID: PMC11838381 DOI: 10.1101/2025.01.28.635373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/21/2025]
Abstract
Critical periods are developmental windows of high experience-dependent plasticity essential for the correct refinement of neuronal circuitry and function. While the consequences for the visual system of sensory deprivation during the critical period have been well-characterized, far less is known about the effects of enhanced sensory experience. Here, we use prey capture learning to assess structural and functional plasticity mediating visual learning in the primary visual cortex of critical period mice. We show that prey capture learning improves temporal frequency discrimination and drives a profound remodeling of visual circuitry through an increase in excitatory connectivity and spine turnover. This global and persistent rewiring is not observed in adult hunters and is mediated by TNFα-dependent mechanisms. Our findings demonstrate that enhanced visual experience in a naturalistic paradigm during the critical period can drive structural plasticity to improve visual function, and promotes a long-lasting increase in spine dynamics that could enhance subsequent plasticity.
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Affiliation(s)
- Diane Bissen
- Department of Biology, Brandeis University, Waltham, MA 02453, USA
| | - Brian A Cary
- Department of Biology, Brandeis University, Waltham, MA 02453, USA
| | - Amanda Zhang
- Department of Biology, Brandeis University, Waltham, MA 02453, USA
| | - Kurt A Sailor
- Institut Pasteur, CNRS UMR 3571, Perception and Action Unit, F-75015, Paris, France
| | | | - Gina G Turrigiano
- Department of Biology, Brandeis University, Waltham, MA 02453, USA
- Lead Contact
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8
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Linares-García I, Iliakis EA, Juliani SE, Ramirez AN, Woolley J, Díaz-Hernández E, Fuccillo MV, Margolis DJ. An Open-Source Joystick Platform for Investigating Forelimb Motor Control, Auditory-Motor Integration, and Value-Based Decision-Making in Head-Fixed Mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.01.23.634598. [PMID: 39896607 PMCID: PMC11785236 DOI: 10.1101/2025.01.23.634598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/04/2025]
Abstract
Investigation of neural processes underlying motor control requires behavioral readouts that capture the richness of actions, including both categorical (choice-based) information and motor execution (kinematics). We present an open-source platform for behavioral training of head-fixed mice that combines a stationary or retractable forelimb-based joystick, sound-presentation system, capacitive lick sensor, and water reward dispenser. The setup allows for the creation of multiple behavioral paradigms, two of which are highlighted here: a two-alternative forced-choice auditory-motor discrimination paradigm, and a two-armed bandit value-based decision-making task. In the auditory-motor paradigm, mice learn to report high or low frequency tones by pushing or pulling the joystick. In the value-based paradigm, mice learn to push or pull the joystick based on the history of rewarded trials. In addition to reporting categorical choices, this setup provides a rich dataset of motor parameters that reflect components of the underlying learning and decision processes in both of these tasks. These kinematic parameters (including joystick speed and displacement, Fréchet similarity of trajectories, tortuosity, angular standard deviation, and movement vigor) provide key additional insights into the motor execution of choices that are not as readily assessed in other paradigms. The system's flexibility of task design, joystick readout, and ease of construction represent an advance compared to currently available manipulandum tasks in mice. We provide detailed schematics for constructing the setup and protocols for behavioral training using both paradigms, with the hope that this open-source resource is readily adopted by neuroscientists interested in mechanisms of sensorimotor integration, motor control, and choice behavior. Significance Statement Behavioral paradigms for experiments in head-restrained mice are important for investigating the relationship between neural activity and behavior. However, behavioral setups are often constrained by high cost, design complexity, and implementation challenges. Here, we present an open-source platform for behavioral training of head-fixed mice using a joystick manipulandum. The setup allows for the creation of multiple behavioral paradigms, including an auditory-motor discrimination paradigm, and a value-based decision-making task. We include detailed instructions for construction and implementation of the entire open-source behavioral platform.
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Affiliation(s)
- Ivan Linares-García
- Department of Cell Biology and Neuroscience, Rutgers, The State University of New Jersey, 604 Allison Road, Piscataway, NJ, 08854, USA
| | - Evan A. Iliakis
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, USA
| | - Sofia E. Juliani
- Department of Cell Biology and Neuroscience, Rutgers, The State University of New Jersey, 604 Allison Road, Piscataway, NJ, 08854, USA
| | - Alexandra N. Ramirez
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, USA
| | - Joel Woolley
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, USA
| | - Edgar Díaz-Hernández
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, USA
| | - Marc V. Fuccillo
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, USA
| | - David J. Margolis
- Department of Cell Biology and Neuroscience, Rutgers, The State University of New Jersey, 604 Allison Road, Piscataway, NJ, 08854, USA
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9
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Collina JS, Erdil G, Xia M, Angeloni CF, Wood KC, Sheth J, Kording KP, Cohen YE, Geffen MN. Individual-specific strategies inform category learning. Sci Rep 2025; 15:2984. [PMID: 39848949 PMCID: PMC11758382 DOI: 10.1038/s41598-024-82219-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2024] [Accepted: 12/03/2024] [Indexed: 01/25/2025] Open
Abstract
Categorization is an essential task for sensory perception. Individuals learn category labels using a variety of strategies to ensure that sensory signals, such as sounds or images, can be assigned to proper categories. Categories are often learned on the basis of extreme examples, and the boundary between categories can differ among individuals. The trajectories for learning also differ among individuals, as different individuals rely on different strategies, such as repeating or alternating choices. However, little is understood about the relationship between individual learning trajectories and learned categorization. To study this relationship, we trained mice to categorize auditory stimuli into two categories using a two-alternative forced choice task. Because the mice took several weeks to learn the task, we were able to quantify the time course of individual strategies and how they relate to how mice categorize stimuli around the categorization boundary. Different mice exhibited different trajectories while learning the task. Mice displayed preferences for a specific category, manifested by a choice bias in their responses, but this bias drifted with learning. We found that this drift in choice bias correlated with variability in the category boundary for sounds with ambiguous category membership. Next, we asked how stimulus-independent, individual-specific strategies informed learning. We found that the tendency to repeat choices, which is a form of perseveration, contributed to long-term learning. These results indicate that long-term trends in individual strategies during category learning affect learned category boundaries.
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Affiliation(s)
- Jared S Collina
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
| | - Gozde Erdil
- Department of Otorhinolaryngology, University of Pennsylvania, Philadelphia, PA, USA
| | - Mingyi Xia
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Janaki Sheth
- Department of Otorhinolaryngology, University of Pennsylvania, Philadelphia, PA, USA
| | - Konrad P Kording
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Yale E Cohen
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
- Department of Otorhinolaryngology, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Maria N Geffen
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Otorhinolaryngology, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
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10
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Zheng J, Meister M. The unbearable slowness of being: Why do we live at 10 bits/s? Neuron 2025; 113:192-204. [PMID: 39694032 PMCID: PMC11758279 DOI: 10.1016/j.neuron.2024.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2024] [Revised: 10/31/2024] [Accepted: 11/12/2024] [Indexed: 12/20/2024]
Abstract
This article is about the neural conundrum behind the slowness of human behavior. The information throughput of a human being is about 10 bits/s. In comparison, our sensory systems gather data at ∼109 bits/s. The stark contrast between these numbers remains unexplained and touches on fundamental aspects of brain function: what neural substrate sets this speed limit on the pace of our existence? Why does the brain need billions of neurons to process 10 bits/s? Why can we only think about one thing at a time? The brain seems to operate in two distinct modes: the "outer" brain handles fast high-dimensional sensory and motor signals, whereas the "inner" brain processes the reduced few bits needed to control behavior. Plausible explanations exist for the large neuron numbers in the outer brain, but not for the inner brain, and we propose new research directions to remedy this.
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Affiliation(s)
- Jieyu Zheng
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
| | - Markus Meister
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
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11
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Peelman K, Haider B. Environmental context sculpts spatial and temporal visual processing in thalamus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2024.07.26.605345. [PMID: 39091887 PMCID: PMC11291113 DOI: 10.1101/2024.07.26.605345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Behavioral state modulates neural activity throughout the visual system1-3. This is largely due to changes in arousal that alter internal brain state4-10. Much is known about how these internal factors influence visual processing7-11, but comparatively less is known about the role of external environmental contexts12. Environmental contexts can promote or prevent certain actions13, and it remains unclear if and how this affects visual processing. Here, we addressed this question in the thalamus of awake head-fixed mice while they viewed stimuli but remained stationary in two different environmental contexts: either a cylindrical tube, or a circular running wheel that enabled locomotion. We made silicon probe recordings in the dorsal lateral geniculate nucleus (dLGN) while simultaneously measuring multiple metrics of arousal changes, so that we could control for them across contexts. We found surprising differences in spatial and temporal processing in dLGN across contexts. The wheel context (versus tube) showed elevated baseline activity, and faster but less spatially selective visual responses; however, these visual processing differences disappeared if the wheel no longer enabled locomotion. Our results reveal an unexpected influence of the physical environmental context on fundamental aspects of early visual processing, even in otherwise identical states of alertness and stillness.
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Affiliation(s)
- Kayla Peelman
- Dept of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA, USA
| | - Bilal Haider
- Dept of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA, USA
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12
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Collina JS, Erdil G, Xia M, Angeloni CF, Wood KC, Sheth J, Kording KP, Cohen YE, Geffen MN. Individual-specific strategies inform category learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2024.09.26.615062. [PMID: 39829779 PMCID: PMC11741237 DOI: 10.1101/2024.09.26.615062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/22/2025]
Abstract
Categorization is an essential task for sensory perception. Individuals learn category labels using a variety of strategies to ensure that sensory signals, such as sounds or images, can be assigned to proper categories. Categories are often learned on the basis of extreme examples, and the boundary between categories can differ among individuals. The trajectories for learning also differ among individuals, as different individuals rely on different strategies, such as repeating or alternating choices. However, little is understood about the relationship between individual learning trajectories and learned categorization. To study this relationship, we trained mice to categorize auditory stimuli into two categories using a two-alternative forced choice task. Because the mice took several weeks to learn the task, we were able to quantify the time course of individual strategies and how they relate to how mice categorize stimuli around the categorization boundary. Different mice exhibited different trajectories while learning the task. Mice displayed preferences for a specific category, manifested by a choice bias in their responses, but this bias drifted with learning. We found that this drift in choice bias correlated with variability in the category boundary for sounds with ambiguous category membership. Next, we asked how stimulus-independent, individual-specific strategies informed learning. We found that the tendency to repeat choices, which is a form of perseveration, contributed to long-term learning. These results indicate that long-term trends in individual strategies during category learning affect learned category boundaries.
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Affiliation(s)
- Jared S. Collina
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA
| | - Gozde Erdil
- Department of Otorhinolaryngology, University of Pennsylvania, Philadelphia, PA
| | - Mingyi Xia
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA
| | | | | | - Janaki Sheth
- Department of Otorhinolaryngology, University of Pennsylvania, Philadelphia, PA
| | - Konrad P. Kording
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA
| | - Yale E. Cohen
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA
- Department of Otorhinolaryngology, University of Pennsylvania, Philadelphia, PA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA
| | - Maria N. Geffen
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA
- Department of Otorhinolaryngology, University of Pennsylvania, Philadelphia, PA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA
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13
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Cuturela LI, Pillow JW. Internal states emerge early during learning of a perceptual decision-making task. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.11.30.626182. [PMID: 39651276 PMCID: PMC11623682 DOI: 10.1101/2024.11.30.626182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2024]
Abstract
Recent work has shown that during perceptual decision-making tasks, animals frequently alternate between different internal states or strategies. However, the question of how or when these emerge during learning remains an important open problem. Does an animal alternate between multiple strategies from the very start of training, or only after extensive exposure to a task? Here we address this question by developing a dynamic latent state model, which we applied to training data from mice learning to perform a visual decision-making task. Remarkably, we found that mice exhibited distinct "engaged" and "biased" states even during early training, with multiple states apparent from the second training session onward. Moreover, our model revealed that the gradual improvement in task performance over the course of training arose from a combination of two factors: (1) increased sensitivity to stimuli across all states; and (2) increased proportion of time spent in a higher-accuracy "engaged" state relative to biased or disengaged states. These findings highlight the power of our approach for characterizing the temporal evolution of multiple strategies across learning.
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14
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Pan-Vazquez A, Sanchez Araujo Y, McMannon B, Louka M, Bandi A, Haetzel L, Faulkner M, Pillow JW, Daw ND, Witten IB. Pre-existing visual responses in a projection-defined dopamine population explain individual learning trajectories. Curr Biol 2024; 34:5349-5358.e6. [PMID: 39413788 PMCID: PMC11579926 DOI: 10.1016/j.cub.2024.09.045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 06/11/2024] [Accepted: 09/17/2024] [Indexed: 10/18/2024]
Abstract
A key challenge of learning a new task is that the environment is high dimensional-there are many different sensory features and possible actions, with typically only a small reward-relevant subset. Although animals can learn to perform complex tasks that involve arbitrary associations between stimuli, actions, and rewards,1,2,3,4,5,6 a consistent and striking result across varied experimental paradigms is that in initially acquiring such tasks, large differences between individuals are apparent in the learning process.7,8,9,10,11,12 What neural mechanisms contribute to initial task acquisition, and why do some individuals learn a new task much more quickly than others? To address these questions, we recorded longitudinally from dopaminergic (DA) axon terminals in mice learning a visual decision-making task.7 Across striatum, DA responses tracked idiosyncratic and side-specific learning trajectories, consistent with widespread reward prediction error coding across DA terminals. However, even before any rewards were delivered, contralateral-side-specific visual responses were present in DA terminals, primarily in the dorsomedial striatum (DMS). These pre-existing responses predicted the extent of learning for contralateral stimuli. Moreover, activation of these terminals improved contralateral performance. Thus, the initial conditions of a projection-specific and feature-specific DA signal help explain individual learning trajectories. More broadly, this work suggests that functional heterogeneity across DA projections may serve to bias target regions toward learning about different subsets of task features, providing a potential mechanism to address the dimensionality of the initial task learning problem.
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Affiliation(s)
- Alejandro Pan-Vazquez
- Princeton Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ 08540, USA
| | - Yoel Sanchez Araujo
- Princeton Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ 08540, USA
| | - Brenna McMannon
- Princeton Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ 08540, USA
| | - Miranta Louka
- Princeton Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ 08540, USA
| | - Akhil Bandi
- Princeton Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ 08540, USA
| | - Laura Haetzel
- Princeton Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ 08540, USA
| | - Mayo Faulkner
- UCL Institute of Ophthalmology, University College London, 11-43 Bath Street, London EC1V 9EL, UK
| | - Jonathan W Pillow
- Princeton Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ 08540, USA
| | - Nathaniel D Daw
- Princeton Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ 08540, USA; Department of Psychology, Princeton University, Washington Road, Princeton, NJ 08540, USA.
| | - Ilana B Witten
- Princeton Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ 08540, USA; Howard Hughes Medical Institute, Princeton University, Washington Road, Princeton, NJ 08540, USA.
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15
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Gilmer JI, Coltman SK, Cuenu G, Hutchinson JR, Huber D, Person AL, Al Borno M. A novel biomechanical model of the mouse forelimb predicts muscle activity in optimal control simulations of reaching movements. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.05.611289. [PMID: 39314302 PMCID: PMC11418950 DOI: 10.1101/2024.09.05.611289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
Mice are key model organisms in neuroscience and motor systems physiology. Fine motor control tasks performed by mice have become widely used in assaying neural and biophysical motor system mechanisms. Although fine motor tasks provide useful insights into behaviors which require complex multi-joint motor control, there is no previously developed physiological biomechanical model of the adult mouse forelimb available for estimating kinematics nor muscle activity or kinetics during behaviors. Here, we developed a musculoskeletal model based on high-resolution imaging of the mouse forelimb that includes muscles spanning the neck, trunk, shoulder, and limbs. Physics-based optimal control simulations of the forelimb model were used to estimate in vivo muscle activity present when constrained to the tracked kinematics during reaching movements. The activity of a subset of muscles was recorded and used to assess the accuracy of the muscle patterning in simulation. We found that the synthesized muscle patterning in the forelimb model had a strong resemblance to empirical muscle patterning, suggesting that our model has utility in providing a realistic set of estimated muscle excitations over time when given a kinematic template. The strength of the similarity between empirical muscle activity and optimal control predictions increases as mice performance improves throughout learning of the reaching task. Our computational tools are available as open-source in the OpenSim physics and modeling platform. Our model can enhance research into limb control across broad research topics and can inform analyses of motor learning, muscle synergies, neural patterning, and behavioral research that would otherwise be inaccessible.
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Affiliation(s)
- Jesse I Gilmer
- University of Colorado Denver ∣ Anschutz Medical Campus, Department of Computer Science and Engineering, Computational Bioscience Program
| | - Susan K Coltman
- The Pennsylvania State University, Department of Kinesiology
| | | | - John R Hutchinson
- Royal Veterinary College, Department of Comparative Biomedical Sciences
| | - Daniel Huber
- University of Geneva, Department of Basic Neuroscience
| | - Abigail L Person
- University of Colorado Denver ∣ Anschutz Medical Campus, Department of Physiology and Biophysics
| | - Mazen Al Borno
- University of Colorado Denver ∣ Anschutz Medical Campus, Department of Computer Science and Engineering, Computational Bioscience Program
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16
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Lyamzin DR, Alamia A, Abdolrahmani M, Aoki R, Benucci A. Regularizing hyperparameters of interacting neural signals in the mouse cortex reflect states of arousal. PLoS Comput Biol 2024; 20:e1012478. [PMID: 39405361 PMCID: PMC11527387 DOI: 10.1371/journal.pcbi.1012478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 10/31/2024] [Accepted: 09/11/2024] [Indexed: 11/02/2024] Open
Abstract
In natural behaviors, multiple neural signals simultaneously drive activation across overlapping brain networks. Due to limitations in the amount of data that can be acquired in common experimental designs, the determination of these interactions is commonly inferred via modeling approaches, which reduce overfitting by finding appropriate regularizing hyperparameters. However, it is unclear whether these hyperparameters can also be related to any aspect of the underlying biological phenomena and help interpret them. We applied a state-of-the-art regularization procedure-automatic locality determination-to interacting neural activations in the mouse posterior cortex associated with movements of the body and eyes. As expected, regularization significantly improved the determination and interpretability of the response interactions. However, regularizing hyperparameters also changed considerably, and seemingly unpredictably, from animal to animal. We found that these variations were not random; rather, they correlated with the variability in visually evoked responses and with the variability in the state of arousal of the animals measured by pupillometry-both pieces of information that were not included in the modeling framework. These observations could be generalized to another commonly used-but potentially less informative-regularization method, ridge regression. Our findings demonstrate that optimal model hyperparameters can be discovery tools that are informative of factors not a priori included in the model's design.
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Affiliation(s)
| | - Andrea Alamia
- Centre de Recherche Cerveau et Cognition, CNRS, Université de Toulouse, Toulouse, France
| | | | - Ryo Aoki
- RIKEN Center for Brain Science, Wako-shi, Saitama, Japan
| | - Andrea Benucci
- Queen Mary University of London, School of Biological and Behavioural Sciences, London, United Kingdom
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17
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Deveau CE, Zhou Z, LaFosse PK, Deng Y, Mirbagheri S, Steinmetz N, Histed MH. Recurrent cortical networks encode natural sensory statistics via sequence filtering. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.24.581890. [PMID: 38903066 PMCID: PMC11188075 DOI: 10.1101/2024.02.24.581890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
Recurrent neural networks can generate dynamics, but in sensory cortex it has been unclear if any dynamic processing is supported by the dense recurrent excitatory-excitatory network. Here we show a new role for recurrent connections in mouse visual cortex: they support powerful dynamical computations, but by filtering sequences of input instead of generating sequences. Using two-photon optogenetics, we measure neural responses to natural images and play them back, finding inputs are amplified when played back during the correct movie dynamic context- when the preceding sequence corresponds to natural vision. This sequence selectivity depends on a network mechanism: earlier input patterns produce responses in other local neurons, which interact with later input patterns. We confirm this mechanism by designing sequences of inputs that are amplified or suppressed by the network. These data suggest recurrent cortical connections perform predictive processing, encoding the statistics of the natural world in input-output transformations.
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Affiliation(s)
- Ciana E Deveau
- Intramural Program, National Institute of Mental Health, National Institutes of Health; Bethesda, MD USA
- NIH Graduate Partnership Program, Bethesda, MD USA
- Department of Neuroscience, Brown University, Providence RI USA
| | - Zhishang Zhou
- Intramural Program, National Institute of Mental Health, National Institutes of Health; Bethesda, MD USA
| | - Paul K LaFosse
- Intramural Program, National Institute of Mental Health, National Institutes of Health; Bethesda, MD USA
- NIH Graduate Partnership Program, Bethesda, MD USA
- Neuroscience and Cognitive Science Program, University of Maryland, College Park MD USA
| | - Yanting Deng
- Intramural Program, National Institute of Mental Health, National Institutes of Health; Bethesda, MD USA
| | - Saghar Mirbagheri
- Department of Biological Structure, University of Washington, Seattle, WA USA
| | - Nicholas Steinmetz
- Department of Biological Structure, University of Washington, Seattle, WA USA
| | - Mark H Histed
- Intramural Program, National Institute of Mental Health, National Institutes of Health; Bethesda, MD USA
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18
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Carandini M. Sensory choices as logistic classification. Neuron 2024; 112:2854-2868.e1. [PMID: 39013468 PMCID: PMC11377159 DOI: 10.1016/j.neuron.2024.06.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 05/13/2024] [Accepted: 06/19/2024] [Indexed: 07/18/2024]
Abstract
Logistic classification is a simple way to make choices based on a set of factors: give each factor a weight, sum the results, and use the sum to set the log odds of a random draw. This operation is known to describe human and animal choices based on value (economic decisions). There is increasing evidence that it also describes choices based on sensory inputs (perceptual decisions), presented across sensory modalities (multisensory integration) and combined with non-sensory factors such as prior probability, expected value, overall motivation, and recent actions. Logistic classification can also capture the effects of brain manipulations such as local inactivations. The brain may implement it by thresholding stochastic inputs (as in signal detection theory) acquired over time (as in the drift diffusion model). It is the optimal strategy under certain conditions, and the brain appears to use it as a heuristic in a wider set of conditions.
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Affiliation(s)
- Matteo Carandini
- UCL Institute of Ophthalmology, University College London, London WC1 6BT, UK.
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19
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Fritsche M, Majumdar A, Strickland L, Liebana Garcia S, Bogacz R, Lak A. Temporal regularities shape perceptual decisions and striatal dopamine signals. Nat Commun 2024; 15:7093. [PMID: 39154025 PMCID: PMC11330509 DOI: 10.1038/s41467-024-51393-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 08/05/2024] [Indexed: 08/19/2024] Open
Abstract
Perceptual decisions should depend on sensory evidence. However, such decisions are also influenced by past choices and outcomes. These choice history biases may reflect advantageous strategies to exploit temporal regularities of natural environments. However, it is unclear whether and how observers can adapt their choice history biases to different temporal regularities, to exploit the multitude of temporal correlations that exist in nature. Here, we show that male mice adapt their perceptual choice history biases to different temporal regularities of visual stimuli. This adaptation was slow, evolving over hundreds of trials across several days. It occurred alongside a fast non-adaptive choice history bias, limited to a few trials. Both fast and slow trial history effects are well captured by a normative reinforcement learning algorithm with multi-trial belief states, comprising both current trial sensory and previous trial memory states. We demonstrate that dorsal striatal dopamine tracks predictions of the model and behavior, suggesting that striatal dopamine reports reward predictions associated with adaptive choice history biases. Our results reveal the adaptive nature of perceptual choice history biases and shed light on their underlying computational principles and neural correlates.
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Affiliation(s)
- Matthias Fritsche
- Department of Physiology, Anatomy & Genetics, University of Oxford, Oxford, UK.
| | - Antara Majumdar
- Department of Physiology, Anatomy & Genetics, University of Oxford, Oxford, UK
| | - Lauren Strickland
- Department of Physiology, Anatomy & Genetics, University of Oxford, Oxford, UK
- Institute of Behavioral Neuroscience, University College London, London, UK
| | | | - Rafal Bogacz
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
| | - Armin Lak
- Department of Physiology, Anatomy & Genetics, University of Oxford, Oxford, UK.
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20
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Del Rosario J, Coletta S, Kim SH, Mobille Z, Peelman K, Williams B, Otsuki AJ, Del Castillo Valerio A, Worden K, Blanpain LT, Lovell L, Choi H, Haider B. Lateral inhibition in V1 controls neural & perceptual contrast sensitivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.10.566605. [PMID: 38014014 PMCID: PMC10680635 DOI: 10.1101/2023.11.10.566605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Lateral inhibition is a central principle for sensory system function. It is thought to operate by the activation of inhibitory neurons that restrict the spatial spread of sensory excitation. Much work on the role of inhibition in sensory systems has focused on visual cortex; however, the neurons, computations, and mechanisms underlying cortical lateral inhibition remain debated, and its importance for visual perception remains unknown. Here, we tested how lateral inhibition from PV or SST neurons in mouse primary visual cortex (V1) modulates neural and perceptual sensitivity to stimulus contrast. Lateral inhibition from PV neurons reduced neural and perceptual sensitivity to visual contrast in a uniform subtractive manner, whereas lateral inhibition from SST neurons more effectively changed the slope (or gain) of neural and perceptual contrast sensitivity. A neural circuit model identified spatially extensive lateral projections from SST neurons as the key factor, and we confirmed this with anatomy and direct subthreshold measurements of a larger spatial footprint for SST versus PV lateral inhibition. Together, these results define cell-type specific computational roles for lateral inhibition in V1, and establish their unique consequences on sensitivity to contrast, a fundamental aspect of the visual world.
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21
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Gauld OM, Packer AM, Russell LE, Dalgleish HWP, Iuga M, Sacadura F, Roth A, Clark BA, Häusser M. A latent pool of neurons silenced by sensory-evoked inhibition can be recruited to enhance perception. Neuron 2024; 112:2386-2403.e6. [PMID: 38729150 PMCID: PMC7616379 DOI: 10.1016/j.neuron.2024.04.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 02/12/2024] [Accepted: 04/12/2024] [Indexed: 05/12/2024]
Abstract
To investigate which activity patterns in sensory cortex are relevant for perceptual decision-making, we combined two-photon calcium imaging and targeted two-photon optogenetics to interrogate barrel cortex activity during perceptual discrimination. We trained mice to discriminate bilateral whisker deflections and report decisions by licking left or right. Two-photon calcium imaging revealed sparse coding of contralateral and ipsilateral whisker input in layer 2/3, with most neurons remaining silent during the task. Activating pyramidal neurons using two-photon holographic photostimulation evoked a perceptual bias that scaled with the number of neurons photostimulated. This effect was dominated by optogenetic activation of non-coding neurons, which did not show sensory or motor-related activity during task performance. Photostimulation also revealed potent recruitment of cortical inhibition during sensory processing, which strongly and preferentially suppressed non-coding neurons. Our results suggest that a pool of non-coding neurons, selectively suppressed by network inhibition during sensory processing, can be recruited to enhance perception.
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Affiliation(s)
- Oliver M Gauld
- Wolfson Institute for Biomedical Research, University College London, London WC1E 6BT, UK; Sainsbury Wellcome Centre, University College London, London W1T 4JG, UK.
| | - Adam M Packer
- Wolfson Institute for Biomedical Research, University College London, London WC1E 6BT, UK; Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, UK
| | - Lloyd E Russell
- Wolfson Institute for Biomedical Research, University College London, London WC1E 6BT, UK
| | - Henry W P Dalgleish
- Wolfson Institute for Biomedical Research, University College London, London WC1E 6BT, UK
| | - Maya Iuga
- Wolfson Institute for Biomedical Research, University College London, London WC1E 6BT, UK
| | - Francisco Sacadura
- Wolfson Institute for Biomedical Research, University College London, London WC1E 6BT, UK
| | - Arnd Roth
- Wolfson Institute for Biomedical Research, University College London, London WC1E 6BT, UK
| | - Beverley A Clark
- Wolfson Institute for Biomedical Research, University College London, London WC1E 6BT, UK
| | - Michael Häusser
- Wolfson Institute for Biomedical Research, University College London, London WC1E 6BT, UK.
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22
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Carandini M. Sensory choices as logistic classification. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.17.576029. [PMID: 38979189 PMCID: PMC11230223 DOI: 10.1101/2024.01.17.576029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Logistic classification is a simple way to make choices based on a set of factors: give each factor a weight, sum the results, and use the sum to set the log odds of a random draw. This operation is known to describe human and animal choices based on value (economic decisions). There is increasing evidence that it also describes choices based on sensory inputs (perceptual decisions), presented across sensory modalities (multisensory integration) and combined with non-sensory factors such as prior probability, expected value, overall motivation, and recent actions. Logistic classification can also capture the effects of brain manipulations such as local inactivations. The brain may implement by thresholding stochastic inputs (as in signal detection theory) acquired over time (as in the drift diffusion model). It is the optimal strategy under certain conditions, and the brain appears to use it as a heuristic in a wider set of conditions.
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Affiliation(s)
- Matteo Carandini
- UCL Institute of Ophthalmology, University College London, London WC1 6BT, UK
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23
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Oesch LT, Ryan MB, Churchland AK. From innate to instructed: A new look at perceptual decision-making. Curr Opin Neurobiol 2024; 86:102871. [PMID: 38569230 PMCID: PMC11162954 DOI: 10.1016/j.conb.2024.102871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 03/07/2024] [Accepted: 03/08/2024] [Indexed: 04/05/2024]
Abstract
Understanding how subjects perceive sensory stimuli in their environment and use this information to guide appropriate actions is a major challenge in neuroscience. To study perceptual decision-making in animals, researchers use tasks that either probe spontaneous responses to stimuli (often described as "naturalistic") or train animals to associate stimuli with experimenter-defined responses. Spontaneous decisions rely on animals' pre-existing knowledge, while trained tasks offer greater versatility, albeit often at the cost of extensive training. Here, we review emerging approaches to investigate perceptual decision-making using both spontaneous and trained behaviors, highlighting their strengths and limitations. Additionally, we propose how trained decision-making tasks could be improved to achieve faster learning and a more generalizable understanding of task rules.
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Affiliation(s)
- Lukas T Oesch
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States
| | - Michael B Ryan
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States. https://twitter.com/NeuroMikeRyan
| | - Anne K Churchland
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States.
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24
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Levi A, Aviv N, Stark E. Learning to learn: Single session acquisition of new rules by freely moving mice. PNAS NEXUS 2024; 3:pgae203. [PMID: 38818240 PMCID: PMC11138122 DOI: 10.1093/pnasnexus/pgae203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 05/14/2024] [Indexed: 06/01/2024]
Abstract
Learning from examples and adapting to new circumstances are fundamental attributes of human cognition. However, it is unclear what conditions allow for fast and successful learning, especially in nonhuman subjects. To determine how rapidly freely moving mice can learn a new discrimination criterion (DC), we design a two-alternative forced-choice visual discrimination paradigm in which the DCs governing the task can change between sessions. We find that experienced animals can learn a new DC after being exposed to only five training and three testing trials. The propensity for single session learning improves over time and is accurately predicted based on animal experience and criterion difficulty. After establishing the procedural learning of a paradigm, mice continuously improve their performance in new circumstances. Thus, mice learn to learn.
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Affiliation(s)
- Amir Levi
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Noam Aviv
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Eran Stark
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol Department of Neurobiology, Haifa University, Haifa 3103301, Israel
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25
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Zhou M, Wu B, Jeong H, Burke DA, Namboodiri VMK. An open-source behavior controller for associative learning and memory (B-CALM). Behav Res Methods 2024; 56:2695-2710. [PMID: 37464151 PMCID: PMC10898869 DOI: 10.3758/s13428-023-02182-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/23/2023] [Indexed: 07/20/2023]
Abstract
Associative learning and memory, i.e., learning and remembering the associations between environmental stimuli, self-generated actions, and outcomes such as rewards or punishments, are critical for the well-being of animals. Hence, the neural mechanisms underlying these processes are extensively studied using behavioral tasks in laboratory animals. Traditionally, these tasks have been controlled using commercial hardware and software, which limits scalability and accessibility due to their cost. More recently, due to the revolution in microcontrollers or microcomputers, several general-purpose and open-source solutions have been advanced for controlling neuroscientific behavioral tasks. While these solutions have great strength due to their flexibility and general-purpose nature, for the same reasons, they suffer from some disadvantages including the need for considerable programming expertise, limited online visualization, or slower than optimal response latencies for any specific task. Here, to mitigate these concerns, we present an open-source behavior controller for associative learning and memory (B-CALM). B-CALM provides an integrated suite that can control a host of associative learning and memory behaviors. As proof of principle for its applicability, we show data from head-fixed mice learning Pavlovian conditioning, operant conditioning, discrimination learning, as well as a timing task and a choice task. These can be run directly from a user-friendly graphical user interface (GUI) written in MATLAB that controls many independently running Arduino Mega microcontrollers in parallel (one per behavior box). In sum, B-CALM will enable researchers to execute a wide variety of associative learning and memory tasks in a scalable, accurate, and user-friendly manner.
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Affiliation(s)
- Mingkang Zhou
- Department of Neurology, University of California, San Francisco, CA, USA
- Neuroscience Graduate Program, University of California, San Francisco, CA, USA
| | - Brenda Wu
- Department of Neurology, University of California, San Francisco, CA, USA
| | - Huijeong Jeong
- Department of Neurology, University of California, San Francisco, CA, USA
| | - Dennis A Burke
- Department of Neurology, University of California, San Francisco, CA, USA
| | - Vijay Mohan K Namboodiri
- Department of Neurology, University of California, San Francisco, CA, USA.
- Neuroscience Graduate Program, University of California, San Francisco, CA, USA.
- Weill Institute for Neuroscience, Kavli Institute for Fundamental Neuroscience, Center for Integrative Neuroscience, University of California, San Francisco, CA, USA.
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26
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Bolaños F, Orlandi JG, Aoki R, Jagadeesh AV, Gardner JL, Benucci A. Efficient coding of natural images in the mouse visual cortex. Nat Commun 2024; 15:2466. [PMID: 38503746 PMCID: PMC10951403 DOI: 10.1038/s41467-024-45919-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 02/06/2024] [Indexed: 03/21/2024] Open
Abstract
How the activity of neurons gives rise to natural vision remains a matter of intense investigation. The mid-level visual areas along the ventral stream are selective to a common class of natural images-textures-but a circuit-level understanding of this selectivity and its link to perception remains unclear. We addressed these questions in mice, first showing that they can perceptually discriminate between textures and statistically simpler spectrally matched stimuli, and between texture types. Then, at the neural level, we found that the secondary visual area (LM) exhibited a higher degree of selectivity for textures compared to the primary visual area (V1). Furthermore, textures were represented in distinct neural activity subspaces whose relative distances were found to correlate with the statistical similarity of the images and the mice's ability to discriminate between them. Notably, these dependencies were more pronounced in LM, where the texture-related subspaces were smaller than in V1, resulting in superior stimulus decoding capabilities. Together, our results demonstrate texture vision in mice, finding a linking framework between stimulus statistics, neural representations, and perceptual sensitivity-a distinct hallmark of efficient coding computations.
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Affiliation(s)
- Federico Bolaños
- University of British Columbia, Neuroimaging and NeuroComputation Centre, Vancouver, BC, V6T, Canada
| | - Javier G Orlandi
- University of Calgary, Department of Physics and Astronomy, Calgary, AB, T2N 1N4, Canada.
| | - Ryo Aoki
- RIKEN Center for Brain Science, Laboratory for Neural Circuits and Behavior, Wakoshi, Japan
| | | | - Justin L Gardner
- Stanford University, Wu Tsai Neurosciences Institute, Stanford, CA, USA
| | - Andrea Benucci
- RIKEN Center for Brain Science, Laboratory for Neural Circuits and Behavior, Wakoshi, Japan.
- Queen Mary, University of London, School of Biological and Behavioral Science, London, E1 4NS, UK.
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27
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White SR, Preston MW, Swanson K, Laubach M. Learning to Choose: Behavioral Dynamics Underlying the Initial Acquisition of Decision Making. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.28.582581. [PMID: 38464283 PMCID: PMC10925347 DOI: 10.1101/2024.02.28.582581] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Current theories of decision making propose that decisions arise through competition between choice options. Computational models of the decision process estimate how quickly information about choice options is integrated and how much information is needed to trigger a choice. Experiments using this approach typically report data from well-trained participants. As such, we do not know how the decision process evolves as a decision-making task is learned for the first time. To address this gap, we used a behavioral design separating learning the value of choice options from learning to make choices. We trained male rats to respond to single visual stimuli with different reward values. Then, we trained them to make choices between pairs of stimuli. Initially, the rats responded more slowly when presented with choices. However, as they gained experience in making choices, this slowing reduced. Response slowing on choice trials persisted throughout the testing period. We found that it was specifically associated with increased exponential variability when the rats chose the higher value stimulus. Additionally, our analysis using drift diffusion modeling revealed that the rats required less information to make choices over time. Surprisingly, we observed reductions in the decision threshold after just a single session of choice learning. These findings provide new insights into the learning process of decision-making tasks. They suggest that the value of choice options and the ability to make choices are learned separately, and that experience plays a crucial role in improving decision-making performance.
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Affiliation(s)
- Samantha R White
- Department of Neuroscience, American University, Washington, DC, USA
| | - Michael W Preston
- Department of Neuroscience, American University, Washington, DC, USA
| | - Kyra Swanson
- Department of Neuroscience, American University, Washington, DC, USA
| | - Mark Laubach
- Department of Neuroscience, American University, Washington, DC, USA
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28
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Reinartz S, Fassihi A, Ravera M, Paz L, Pulecchi F, Gigante M, Diamond ME. Direct contribution of the sensory cortex to the judgment of stimulus duration. Nat Commun 2024; 15:1712. [PMID: 38402290 PMCID: PMC10894222 DOI: 10.1038/s41467-024-45970-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 02/06/2024] [Indexed: 02/26/2024] Open
Abstract
Decision making frequently depends on monitoring the duration of sensory events. To determine whether, and how, the perception of elapsed time derives from the neuronal representation of the stimulus itself, we recorded and optogenetically modulated vibrissal somatosensory cortical activity as male rats judged vibration duration. Perceived duration was dilated by optogenetic excitation. A second set of rats judged vibration intensity; here, optogenetic excitation amplified the intensity percept, demonstrating sensory cortex to be the common gateway both to time and to stimulus feature processing. A model beginning with the membrane currents evoked by vibrissal and optogenetic drive and culminating in the representation of perceived time successfully replicated rats' choices. Time perception is thus as deeply intermeshed within the sensory processing pathway as is the sense of touch itself, suggesting that the experience of time may be further investigated with the toolbox of sensory coding.
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Affiliation(s)
- Sebastian Reinartz
- SENSEx Lab, International School for Advanced Studies (SISSA), 34136, Trieste, Italy
- Brain & Sound Lab, Department of Biomedicine, Basel University, 4056, Basel, Switzerland
| | - Arash Fassihi
- SENSEx Lab, International School for Advanced Studies (SISSA), 34136, Trieste, Italy
- Department of Physics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Maria Ravera
- SENSEx Lab, International School for Advanced Studies (SISSA), 34136, Trieste, Italy
| | - Luciano Paz
- SENSEx Lab, International School for Advanced Studies (SISSA), 34136, Trieste, Italy
| | - Francesca Pulecchi
- SENSEx Lab, International School for Advanced Studies (SISSA), 34136, Trieste, Italy
| | - Marco Gigante
- SENSEx Lab, International School for Advanced Studies (SISSA), 34136, Trieste, Italy
| | - Mathew E Diamond
- SENSEx Lab, International School for Advanced Studies (SISSA), 34136, Trieste, Italy.
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29
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Londei F, Arena G, Ferrucci L, Russo E, Ceccarelli F, Genovesio A. Connecting the dots in the zona incerta: A study of neural assemblies and motifs of inter-area coordination in mice. iScience 2024; 27:108761. [PMID: 38274403 PMCID: PMC10808920 DOI: 10.1016/j.isci.2023.108761] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 10/23/2023] [Accepted: 11/11/2023] [Indexed: 01/27/2024] Open
Abstract
The zona incerta (ZI), a subthalamic area connected to numerous brain regions, has raised clinical interest because its stimulation alleviates the motor symptoms of Parkinson's disease. To explore its coordinative nature, we studied the assembly formation in a dataset of neural recordings in mice and quantified the degree of functional coordination of ZI with other 24 brain areas. We found that the ZI is a highly integrative area. The analysis in terms of "loop-like" motifs, directional assemblies composed of three neurons spanning two areas, has revealed reciprocal functional interactions with reentrant signals that, in most cases, start and end with the activation of ZI units. In support of its proposed integrative role, we found that almost one-third of the ZI's neurons formed assemblies with more than half of the other recorded areas and that loop-like assemblies may stand out as hyper-integrative motifs compared to other types of activation patterns.
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Affiliation(s)
- Fabrizio Londei
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
- PhD Program in Behavioral Neuroscience, Sapienza University of Rome, Rome, Italy
| | - Giulia Arena
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
- PhD Program in Behavioral Neuroscience, Sapienza University of Rome, Rome, Italy
| | - Lorenzo Ferrucci
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Eleonora Russo
- The BioRobotics Institute, Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
| | - Francesco Ceccarelli
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Aldo Genovesio
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
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30
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Gale SD, Strawder C, Bennett C, Mihalas S, Koch C, Olsen SR. Backward masking in mice requires visual cortex. Nat Neurosci 2024; 27:129-136. [PMID: 37957319 DOI: 10.1038/s41593-023-01488-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 10/10/2023] [Indexed: 11/15/2023]
Abstract
Visual masking can reveal the timescale of perception, but the underlying circuit mechanisms are not understood. Here we describe a backward masking task in mice and humans in which the location of a stimulus is potently masked. Humans report reduced subjective visibility that tracks behavioral deficits. In mice, both masking and optogenetic silencing of visual cortex (V1) reduce performance over a similar timecourse but have distinct effects on response rates and accuracy. Activity in V1 is consistent with masked behavior when quantified over long, but not short, time windows. A dual accumulator model recapitulates both mouse and human behavior. The model and subjects' performance imply that the initial spikes in V1 can trigger a correct response, but subsequent V1 activity degrades performance. Supporting this hypothesis, optogenetically suppressing mask-evoked activity in V1 fully restores accurate behavior. Together, these results demonstrate that mice, like humans, are susceptible to masking and that target and mask information is first confounded downstream of V1.
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Affiliation(s)
- Samuel D Gale
- MindScope Program, Allen Institute, Seattle, WA, USA
| | | | | | | | - Christof Koch
- MindScope Program, Allen Institute, Seattle, WA, USA.
| | - Shawn R Olsen
- MindScope Program, Allen Institute, Seattle, WA, USA.
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31
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Timme NM, Ardinger CE, Weir SDC, Zelaya-Escobar R, Kruger R, Lapish CC. Non-consummatory behavior signals predict aversion-resistant alcohol drinking in head-fixed mice. Neuropharmacology 2024; 242:109762. [PMID: 37871677 PMCID: PMC10872650 DOI: 10.1016/j.neuropharm.2023.109762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 10/05/2023] [Accepted: 10/12/2023] [Indexed: 10/25/2023]
Abstract
A key facet of alcohol use disorder is continuing to drink alcohol despite negative consequences (so called "aversion-resistant drinking"). In this study, we sought to assess the degree to which head-fixed mice exhibit aversion-resistant drinking and to leverage behavioral analysis techniques available in head-fixture to relate non-consummatory behaviors to aversion-resistant drinking. We assessed aversion-resistant drinking in head-fixed female and male C57BL/6 J mice. We adulterated 20% (v/v) alcohol with varying concentrations of the bitter tastant quinine to measure the degree to which mice would continue to drink despite this aversive stimulus. We recorded high-resolution video of the mice during head-fixed drinking, tracked body parts with machine vision tools, and analyzed body movements in relation to consumption. Female and male head-fixed mice exhibited heterogenous levels of aversion-resistant drinking. Additionally, non-consummatory behaviors, such as paw movement and snout movement, were related to the intensity of aversion-resistant drinking. These studies demonstrate that head-fixed mice exhibit aversion-resistant drinking and that non-consummatory behaviors can be used to assess perceived aversiveness in this paradigm. Furthermore, these studies lay the groundwork for future experiments that will utilize advanced electrophysiological techniques to record from large populations of neurons during aversion-resistant drinking to understand the neurocomputational processes that drive this clinically relevant behavior. This article is part of the Special Issue on "PFC circuit function in psychiatric disease and relevant models".
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Affiliation(s)
- Nicholas M Timme
- Department of Psychology, Indiana University - Purdue University Indianapolis, 402 N. Blackford St, LD 124, Indianapolis, IN, 46202, USA.
| | - Cherish E Ardinger
- Department of Psychology, Indiana University - Purdue University Indianapolis, 402 N. Blackford St, LD 124, Indianapolis, IN, 46202, USA
| | - Seth D C Weir
- Department of Psychology, Indiana University - Purdue University Indianapolis, 402 N. Blackford St, LD 124, Indianapolis, IN, 46202, USA
| | - Rachel Zelaya-Escobar
- Department of Psychology, Indiana University - Purdue University Indianapolis, 402 N. Blackford St, LD 124, Indianapolis, IN, 46202, USA
| | - Rachel Kruger
- Department of Psychology, Indiana University - Purdue University Indianapolis, 402 N. Blackford St, LD 124, Indianapolis, IN, 46202, USA
| | - Christopher C Lapish
- Department of Anatomy, Cell Biology, and Physiology, Indiana University School of Medicine, 635 Barnhill Drive, MSB 5035, Indianapolis, IN, 46202, USA; Stark Neuroscience Institute, Indiana University School of Medicine, 320 W. 15th St, NB 414, Indianapolis, IN, 46202, USA
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32
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Do J, Jung MW, Lee D. Automating licking bias correction in a two-choice delayed match-to-sample task to accelerate learning. Sci Rep 2023; 13:22768. [PMID: 38123637 PMCID: PMC10733387 DOI: 10.1038/s41598-023-49862-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023] Open
Abstract
Animals often display choice bias, or a preference for one option over the others, which can significantly impede learning new tasks. Delayed match-to-sample (DMS) tasks with two-alternative choices of lickports on the left and right have been widely used to study sensory processing, working memory, and associative memory in head-fixed animals. However, extensive training time, primarily due to the animals' biased licking responses, limits their practical utility. Here, we present the implementation of an automated side bias correction system in an olfactory DMS task, where the lickport positions and the ratio of left- and right-rewarded trials are dynamically adjusted to counterbalance mouse's biased licking responses during training. The correction algorithm moves the preferred lickport farther away from the mouse's mouth and the non-preferred lickport closer, while also increasing the proportion of non-preferred side trials when biased licking occurs. We found that adjusting lickport distances and the proportions of left- versus right-rewarded trials effectively reduces the mouse's side bias. Further analyses reveal that these adjustments also correlate with subsequent improvements in behavioral performance. Our findings suggest that the automated side bias correction system is a valuable tool for enhancing the applicability of behavioral tasks involving two-alternative lickport choices.
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Affiliation(s)
- Jongrok Do
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
- Center for Cognition and Sociality, Institute for Basic Science, Daejeon, 34126, Republic of Korea
| | - Min Whan Jung
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea.
- Center for Synaptic Brain Dysfunctions, Institute for Basic Science, Daejeon, 34141, Republic of Korea.
| | - Doyun Lee
- Center for Cognition and Sociality, Institute for Basic Science, Daejeon, 34126, Republic of Korea.
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33
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Baruchin LJ, Alleman M, Schröder S. Reward Modulates Visual Responses in the Superficial Superior Colliculus of Mice. J Neurosci 2023; 43:8663-8680. [PMID: 37879894 PMCID: PMC7615379 DOI: 10.1523/jneurosci.0089-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 10/06/2023] [Accepted: 10/10/2023] [Indexed: 10/27/2023] Open
Abstract
The processing of sensory input is constantly adapting to behavioral demands and internal states. The drive to obtain reward, e.g., searching for water when thirsty, is a strong behavioral demand and associating the reward with its source, a certain environment or action, is paramount for survival. Here, we show that water reward increases subsequent visual activity in the superficial layers of the superior colliculus (SC), which receive direct input from the retina and belong to the earliest stages of visual processing. We trained mice of either sex to perform a visual decision task and recorded the activity of neurons in the SC using two-photon calcium imaging and high-density electrophysiological recordings. Responses to visual stimuli in around 20% of visually responsive neurons in the superficial SC were affected by reward delivered in the previous trial. Reward mostly increased visual responses independent from modulations due to pupil size changes. The modulation of visual responses by reward could not be explained by movements like licking. It was specific to responses to the following visual stimulus, independent of slow fluctuations in neural activity and independent of how often the stimulus was previously rewarded. Electrophysiological recordings confirmed these results and revealed that reward affected the early phase of the visual response around 80 ms after stimulus onset. Modulation of visual responses by reward, but not pupil size, significantly improved the performance of a population decoder to detect visual stimuli, indicating the relevance of reward modulation for the visual performance of the animal.SIGNIFICANCE STATEMENT To learn which actions lead to food, water, or safety, it is necessary to integrate the receiving of reward with sensory stimuli related to the reward. Cortical stages of sensory processing have been shown to represent stimulus-reward associations. Here, we show, however, that reward influences neurons at a much earlier stage of sensory processing, the superior colliculus (SC), receiving direct input from the retina. Visual responses were increased shortly after the animal received the water reward, which led to an improved stimulus signal in the population of these visual neurons. Reward modulation of early visual responses may thus improve perception of visual environments predictive of reward.
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Affiliation(s)
- Liad J Baruchin
- School of Life Sciences, University of Sussex, Brighton BN1 9QG, United Kingdom
| | - Matteo Alleman
- Institute of Ophthalmology, University College London, London WC1E 6BT, United Kingdom
| | - Sylvia Schröder
- School of Life Sciences, University of Sussex, Brighton BN1 9QG, United Kingdom
- Institute of Ophthalmology, University College London, London WC1E 6BT, United Kingdom
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34
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Zhou ZC, Gordon-Fennell A, Piantadosi SC, Ji N, Smith SL, Bruchas MR, Stuber GD. Deep-brain optical recording of neural dynamics during behavior. Neuron 2023; 111:3716-3738. [PMID: 37804833 PMCID: PMC10843303 DOI: 10.1016/j.neuron.2023.09.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 08/24/2023] [Accepted: 09/06/2023] [Indexed: 10/09/2023]
Abstract
In vivo fluorescence recording techniques have produced landmark discoveries in neuroscience, providing insight into how single cell and circuit-level computations mediate sensory processing and generate complex behaviors. While much attention has been given to recording from cortical brain regions, deep-brain fluorescence recording is more complex because it requires additional measures to gain optical access to harder to reach brain nuclei. Here we discuss detailed considerations and tradeoffs regarding deep-brain fluorescence recording techniques and provide a comprehensive guide for all major steps involved, from project planning to data analysis. The goal is to impart guidance for new and experienced investigators seeking to use in vivo deep fluorescence optical recordings in awake, behaving rodent models.
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Affiliation(s)
- Zhe Charles Zhou
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98195, USA; Center for Neurobiology of Addiction, Pain, and Emotion, University of Washington, Seattle, WA 98195, USA
| | - Adam Gordon-Fennell
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98195, USA; Center for Neurobiology of Addiction, Pain, and Emotion, University of Washington, Seattle, WA 98195, USA
| | - Sean C Piantadosi
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98195, USA; Center for Neurobiology of Addiction, Pain, and Emotion, University of Washington, Seattle, WA 98195, USA
| | - Na Ji
- Department of Physics, University of California, Berkeley, Berkeley, CA 94720, USA; Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA; Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Spencer LaVere Smith
- Department of Electrical and Computer Engineering, University of California Santa Barbara, Santa Barbara, CA 93106, USA
| | - Michael R Bruchas
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98195, USA; Center for Neurobiology of Addiction, Pain, and Emotion, University of Washington, Seattle, WA 98195, USA; Department of Pharmacology, University of Washington, Seattle, WA 98195, USA; Department of Bioengineering, University of Washington, Seattle, WA 98195, USA.
| | - Garret D Stuber
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98195, USA; Center for Neurobiology of Addiction, Pain, and Emotion, University of Washington, Seattle, WA 98195, USA; Department of Pharmacology, University of Washington, Seattle, WA 98195, USA.
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35
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Timme NM, Ardinger CE, Weir SDC, Zelaya-Escobar R, Kruger R, Lapish CC. Non-Consummatory Behavior Signals Predict Aversion-Resistant Alcohol Drinking in Head-Fixed Mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.20.545767. [PMID: 37873153 PMCID: PMC10592797 DOI: 10.1101/2023.06.20.545767] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
A key facet of alcohol use disorder is continuing to drink alcohol despite negative consequences (so called "aversion-resistant drinking"). In this study, we sought to assess the degree to which head-fixed mice exhibit aversion-resistant drinking and to leverage behavioral analysis techniques available in head-fixture to relate non-consummatory behaviors to aversion-resistant drinking. We assessed aversion-resistant drinking in head-fixed female and male C57BL/6J mice. We adulterated 20% (v/v) alcohol with varying concentrations of the bitter tastant quinine to measure the degree to which mice would continue to drink despite this aversive stimulus. We recorded high-resolution video of the mice during head-fixed drinking, tracked body parts with machine vision tools, and analyzed body movements in relation to consumption. Female and male head-fixed mice exhibited heterogenous levels of aversion-resistant drinking. Additionally, non-consummatory behaviors, such as paw movement and snout movement, were related to the intensity of aversion-resistant drinking. These studies demonstrate that head-fixed mice exhibit aversion-resistant drinking and that non-consummatory behaviors can be used to assess perceived aversiveness in this paradigm. Furthermore, these studies lay the groundwork for future experiments that will utilize advanced electrophysiological techniques to record from large populations of neurons during aversion-resistant drinking to understand the neurocomputational processes that drive this clinically relevant behavior.
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Affiliation(s)
- Nicholas M. Timme
- Department of Psychology, Indiana University – Purdue University Indianapolis, 402 N. Blackford St, LD 124, Indianapolis, IN, 46202, USA
| | - Cherish E. Ardinger
- Department of Psychology, Indiana University – Purdue University Indianapolis, 402 N. Blackford St, LD 124, Indianapolis, IN, 46202, USA
| | - Seth D. C. Weir
- Department of Psychology, Indiana University – Purdue University Indianapolis, 402 N. Blackford St, LD 124, Indianapolis, IN, 46202, USA
| | - Rachel Zelaya-Escobar
- Department of Psychology, Indiana University – Purdue University Indianapolis, 402 N. Blackford St, LD 124, Indianapolis, IN, 46202, USA
| | - Rachel Kruger
- Department of Psychology, Indiana University – Purdue University Indianapolis, 402 N. Blackford St, LD 124, Indianapolis, IN, 46202, USA
| | - Christopher C. Lapish
- Department of Anatomy, Cell Biology, and Physiology, Indiana University School of Medicine, 635 Barnhill Drive, MSB 5035, Indianapolis, IN, 46202, USA
- Stark Neuroscience Institute, Indiana University School of Medicine, 320 W. 15 St, NB 414, Indianapolis, IN 46202, USA
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36
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Bech P, Crochet S, Dard R, Ghaderi P, Liu Y, Malekzadeh M, Petersen CCH, Pulin M, Renard A, Sourmpis C. Striatal Dopamine Signals and Reward Learning. FUNCTION 2023; 4:zqad056. [PMID: 37841525 PMCID: PMC10572094 DOI: 10.1093/function/zqad056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 09/28/2023] [Accepted: 09/29/2023] [Indexed: 10/17/2023] Open
Abstract
We are constantly bombarded by sensory information and constantly making decisions on how to act. In order to optimally adapt behavior, we must judge which sequences of sensory inputs and actions lead to successful outcomes in specific circumstances. Neuronal circuits of the basal ganglia have been strongly implicated in action selection, as well as the learning and execution of goal-directed behaviors, with accumulating evidence supporting the hypothesis that midbrain dopamine neurons might encode a reward signal useful for learning. Here, we review evidence suggesting that midbrain dopaminergic neurons signal reward prediction error, driving synaptic plasticity in the striatum underlying learning. We focus on phasic increases in action potential firing of midbrain dopamine neurons in response to unexpected rewards. These dopamine neurons prominently innervate the dorsal and ventral striatum. In the striatum, the released dopamine binds to dopamine receptors, where it regulates the plasticity of glutamatergic synapses. The increase of striatal dopamine accompanying an unexpected reward activates dopamine type 1 receptors (D1Rs) initiating a signaling cascade that promotes long-term potentiation of recently active glutamatergic input onto striatonigral neurons. Sensorimotor-evoked glutamatergic input, which is active immediately before reward delivery will thus be strengthened onto neurons in the striatum expressing D1Rs. In turn, these neurons cause disinhibition of brainstem motor centers and disinhibition of the motor thalamus, thus promoting motor output to reinforce rewarded stimulus-action outcomes. Although many details of the hypothesis need further investigation, altogether, it seems likely that dopamine signals in the striatum might underlie important aspects of goal-directed reward-based learning.
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Affiliation(s)
- Pol Bech
- Laboratory of Sensory Processing, Brain Mind Institute, Faculty of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland
| | - Sylvain Crochet
- Laboratory of Sensory Processing, Brain Mind Institute, Faculty of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland
| | - Robin Dard
- Laboratory of Sensory Processing, Brain Mind Institute, Faculty of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland
| | - Parviz Ghaderi
- Laboratory of Sensory Processing, Brain Mind Institute, Faculty of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland
| | - Yanqi Liu
- Laboratory of Sensory Processing, Brain Mind Institute, Faculty of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland
| | - Meriam Malekzadeh
- Laboratory of Sensory Processing, Brain Mind Institute, Faculty of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland
| | - Carl C H Petersen
- Laboratory of Sensory Processing, Brain Mind Institute, Faculty of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland
| | - Mauro Pulin
- Laboratory of Sensory Processing, Brain Mind Institute, Faculty of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland
| | - Anthony Renard
- Laboratory of Sensory Processing, Brain Mind Institute, Faculty of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland
| | - Christos Sourmpis
- Laboratory of Sensory Processing, Brain Mind Institute, Faculty of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland
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37
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Le NM, Yildirim M, Wang Y, Sugihara H, Jazayeri M, Sur M. Mixtures of strategies underlie rodent behavior during reversal learning. PLoS Comput Biol 2023; 19:e1011430. [PMID: 37708113 PMCID: PMC10501641 DOI: 10.1371/journal.pcbi.1011430] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 08/09/2023] [Indexed: 09/16/2023] Open
Abstract
In reversal learning tasks, the behavior of humans and animals is often assumed to be uniform within single experimental sessions to facilitate data analysis and model fitting. However, behavior of agents can display substantial variability in single experimental sessions, as they execute different blocks of trials with different transition dynamics. Here, we observed that in a deterministic reversal learning task, mice display noisy and sub-optimal choice transitions even at the expert stages of learning. We investigated two sources of the sub-optimality in the behavior. First, we found that mice exhibit a high lapse rate during task execution, as they reverted to unrewarded directions after choice transitions. Second, we unexpectedly found that a majority of mice did not execute a uniform strategy, but rather mixed between several behavioral modes with different transition dynamics. We quantified the use of such mixtures with a state-space model, block Hidden Markov Model (block HMM), to dissociate the mixtures of dynamic choice transitions in individual blocks of trials. Additionally, we found that blockHMM transition modes in rodent behavior can be accounted for by two different types of behavioral algorithms, model-free or inference-based learning, that might be used to solve the task. Combining these approaches, we found that mice used a mixture of both exploratory, model-free strategies and deterministic, inference-based behavior in the task, explaining their overall noisy choice sequences. Together, our combined computational approach highlights intrinsic sources of noise in rodent reversal learning behavior and provides a richer description of behavior than conventional techniques, while uncovering the hidden states that underlie the block-by-block transitions.
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Affiliation(s)
- Nhat Minh Le
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Murat Yildirim
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Neurosciences, Cleveland Clinic Lerner Research Institute, Cleveland, Ohio, United States of America
| | - Yizhi Wang
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Hiroki Sugihara
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Mehrdad Jazayeri
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Mriganka Sur
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
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38
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Wang Y, Chen Z, Ma G, Wang L, Liu Y, Qin M, Fei X, Wu Y, Xu M, Zhang S. A frontal transcallosal inhibition loop mediates interhemispheric balance in visuospatial processing. Nat Commun 2023; 14:5213. [PMID: 37626171 PMCID: PMC10457336 DOI: 10.1038/s41467-023-40985-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 08/17/2023] [Indexed: 08/27/2023] Open
Abstract
Interhemispheric communication through the corpus callosum is required for both sensory and cognitive processes. Impaired transcallosal inhibition causing interhemispheric imbalance is believed to underlie visuospatial bias after frontoparietal cortical damage, but the synaptic circuits involved remain largely unknown. Here, we show that lesions in the mouse anterior cingulate area (ACA) cause severe visuospatial bias mediated by a transcallosal inhibition loop. In a visual-change-detection task, ACA callosal-projection neurons (CPNs) were more active with contralateral visual field changes than with ipsilateral changes. Unilateral CPN inactivation impaired contralateral change detection but improved ipsilateral detection by altering interhemispheric interaction through callosal projections. CPNs strongly activated contralateral parvalbumin-positive (PV+) neurons, and callosal-input-driven PV+ neurons preferentially inhibited ipsilateral CPNs, thus mediating transcallosal inhibition. Unilateral PV+ neuron activation caused a similar behavioral bias to contralateral CPN activation and ipsilateral CPN inactivation, and bilateral PV+ neuron activation eliminated this bias. Notably, restoring interhemispheric balance by activating contralesional PV+ neurons significantly improved contralesional detection in ACA-lesioned animals. Thus, a frontal transcallosal inhibition loop comprising CPNs and callosal-input-driven PV+ neurons mediates interhemispheric balance in visuospatial processing, and enhancing contralesional transcallosal inhibition restores interhemispheric balance while also reversing lesion-induced bias.
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Affiliation(s)
- Yanjie Wang
- Songjiang Research Institute, Shanghai Songjiang District Central Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200025, China
- Center for Brain Science of Shanghai Children's Medical Center, Department of Anatomy and Physiology, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Zhaonan Chen
- Songjiang Research Institute, Shanghai Songjiang District Central Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200025, China
- Center for Brain Science of Shanghai Children's Medical Center, Department of Anatomy and Physiology, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Guofen Ma
- Songjiang Research Institute, Shanghai Songjiang District Central Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200025, China
- Center for Brain Science of Shanghai Children's Medical Center, Department of Anatomy and Physiology, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Lizhao Wang
- Songjiang Research Institute, Shanghai Songjiang District Central Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200025, China
- Center for Brain Science of Shanghai Children's Medical Center, Department of Anatomy and Physiology, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Yanmei Liu
- Songjiang Research Institute, Shanghai Songjiang District Central Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200025, China
- Center for Brain Science of Shanghai Children's Medical Center, Department of Anatomy and Physiology, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Meiling Qin
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Xiang Fei
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yifan Wu
- Songjiang Research Institute, Shanghai Songjiang District Central Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200025, China
- Center for Brain Science of Shanghai Children's Medical Center, Department of Anatomy and Physiology, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Min Xu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Siyu Zhang
- Songjiang Research Institute, Shanghai Songjiang District Central Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200025, China.
- Center for Brain Science of Shanghai Children's Medical Center, Department of Anatomy and Physiology, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China.
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39
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Li WR, Nakano T, Mizutani K, Matsubara T, Kawatani M, Mukai Y, Danjo T, Ito H, Aizawa H, Yamanaka A, Petersen CCH, Yoshimoto J, Yamashita T. Neural mechanisms underlying uninstructed orofacial movements during reward-based learning behaviors. Curr Biol 2023; 33:3436-3451.e7. [PMID: 37536343 DOI: 10.1016/j.cub.2023.07.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 07/06/2023] [Accepted: 07/10/2023] [Indexed: 08/05/2023]
Abstract
During reward-based learning tasks, animals make orofacial movements that globally influence brain activity at the timings of reward expectation and acquisition. These orofacial movements are not explicitly instructed and typically appear along with goal-directed behaviors. Here, we show that reinforcing optogenetic stimulation of dopamine neurons in the ventral tegmental area (oDAS) in mice is sufficient to induce orofacial movements in the whiskers and nose without accompanying goal-directed behaviors. Pavlovian conditioning with a sensory cue and oDAS elicited cue-locked and oDAS-aligned orofacial movements, which were distinguishable by a machine-learning model. Inhibition or knockout of dopamine D1 receptors in the nucleus accumbens inhibited oDAS-induced motion but spared cue-locked motion, suggesting differential regulation of these two types of orofacial motions. In contrast, inactivation of the whisker primary motor cortex (wM1) abolished both types of orofacial movements. We found specific neuronal populations in wM1 representing either oDAS-aligned or cue-locked whisker movements. Notably, optogenetic stimulation of wM1 neurons successfully replicated these two types of movements. Our results thus suggest that accumbal D1-receptor-dependent and -independent neuronal signals converge in the wM1 for facilitating distinct uninstructed orofacial movements during a reward-based learning task.
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Affiliation(s)
- Wan-Ru Li
- Department of Physiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; Department of Neuroscience II, Research Institute of Environmental Medicine, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan; Department of Functional Anatomy & Neuroscience, Graduate School of Medicine, Nagoya University, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Japan
| | - Takashi Nakano
- Department of Computational Biology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma 630-0192, Japan; International Center for Brain Science (ICBS), Fujita Health University, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan
| | - Kohta Mizutani
- Department of Neuroscience II, Research Institute of Environmental Medicine, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan; Laboratory for Advanced Brain Functions, Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita 565-0871, Japan
| | - Takanori Matsubara
- Department of Physiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; Department of Neuroscience II, Research Institute of Environmental Medicine, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
| | - Masahiro Kawatani
- Department of Physiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; Department of Neuroscience II, Research Institute of Environmental Medicine, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan; Department of Functional Anatomy & Neuroscience, Graduate School of Medicine, Nagoya University, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Japan
| | - Yasutaka Mukai
- Department of Neuroscience II, Research Institute of Environmental Medicine, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
| | - Teruko Danjo
- Department of Physiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan
| | - Hikaru Ito
- Department of Neurobiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8553, Japan; Research Facility Center for Science and Technology, Kagawa University, 1750-1 Ikenobe, Miki-cho, Kita-gun, Kagawa 761-0793, Japan
| | - Hidenori Aizawa
- Department of Neurobiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8553, Japan
| | - Akihiro Yamanaka
- Department of Neuroscience II, Research Institute of Environmental Medicine, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
| | - Carl C H Petersen
- Laboratory of Sensory Processing, Brain Mind Institute, Faculty of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Junichiro Yoshimoto
- Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma 630-0192, Japan; International Center for Brain Science (ICBS), Fujita Health University, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; Department of Biomedical Data Science, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan.
| | - Takayuki Yamashita
- Department of Physiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; Department of Neuroscience II, Research Institute of Environmental Medicine, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan; International Center for Brain Science (ICBS), Fujita Health University, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan.
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40
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Coen P, Sit TPH, Wells MJ, Carandini M, Harris KD. Mouse frontal cortex mediates additive multisensory decisions. Neuron 2023; 111:2432-2447.e13. [PMID: 37295419 PMCID: PMC10957398 DOI: 10.1016/j.neuron.2023.05.008] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 12/02/2022] [Accepted: 05/10/2023] [Indexed: 06/12/2023]
Abstract
The brain can combine auditory and visual information to localize objects. However, the cortical substrates underlying audiovisual integration remain uncertain. Here, we show that mouse frontal cortex combines auditory and visual evidence; that this combination is additive, mirroring behavior; and that it evolves with learning. We trained mice in an audiovisual localization task. Inactivating frontal cortex impaired responses to either sensory modality, while inactivating visual or parietal cortex affected only visual stimuli. Recordings from >14,000 neurons indicated that after task learning, activity in the anterior part of frontal area MOs (secondary motor cortex) additively encodes visual and auditory signals, consistent with the mice's behavioral strategy. An accumulator model applied to these sensory representations reproduced the observed choices and reaction times. These results suggest that frontal cortex adapts through learning to combine evidence across sensory cortices, providing a signal that is transformed into a binary decision by a downstream accumulator.
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Affiliation(s)
- Philip Coen
- UCL Queen Square Institute of Neurology, University College London, London, UK; UCL Institute of Ophthalmology, University College London, London, UK.
| | - Timothy P H Sit
- Sainsbury-Wellcome Center, University College London, London, UK
| | - Miles J Wells
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Matteo Carandini
- UCL Institute of Ophthalmology, University College London, London, UK
| | - Kenneth D Harris
- UCL Queen Square Institute of Neurology, University College London, London, UK
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41
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Quintana D, Bounds HA, Brown J, Wang M, Bhatla N, Wiegert JS, Adesnik H. Dissociating instructive from permissive roles of brain circuits with reversible neural activity manipulations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.11.540397. [PMID: 37214966 PMCID: PMC10197619 DOI: 10.1101/2023.05.11.540397] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Neuroscientists rely on targeted perturbations and lesions to causally map functions in the brain1. Yet, since the brain is highly interconnected, manipulation of one area can impact behavior through indirect effects on many other brain regions, complicating the interpretation of such results2,3. On the other hand, the often-observed recovery of behavior performance after lesion can cast doubt on whether the lesioned area was ever directly involved4,5. Recent studies have highlighted how the results of acute and irreversible inactivation can directly conflict4-6, making it unclear whether a brain area is instructive or merely permissive in a specific brain function. To overcome this challenge, we developed a three-stage optogenetic approach which leverages the ability to precisely control the temporal period of regional inactivation with either brief or sustained illumination. Using a visual detection task, we found that acute optogenetic inactivation of the primary visual cortex (V1) suppressed task performance if cortical inactivation was intermittent across trials within each behavioral session. However, when we inactivated V1 for entire behavioral sessions, animals quickly recovered performance in just one to two days. Most importantly, after returning these recovered animals to intermittent cortical inactivation, they quickly reverted to failing on optogenetic inactivation trials. These data support a revised model where the cortex is the default circuit that instructs perceptual performance in basic sensory tasks. More generally, this novel, temporally controllable optogenetic perturbation paradigm can be broadly applied to brain circuits and specific cell types to assess whether they are instructive or merely permissive in a brain function or behavior.
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Affiliation(s)
- Daniel Quintana
- Department of Molecular and Cell Biology, University of California, Berkeley
| | - Hayley A Bounds
- Department of Molecular and Cell Biology, University of California, Berkeley
- The Helen Wills Neuroscience Institute
| | - Jennifer Brown
- Department of Molecular and Cell Biology, University of California, Berkeley
| | - May Wang
- Department of Molecular and Cell Biology, University of California, Berkeley
| | - Nikhil Bhatla
- Department of Molecular and Cell Biology, University of California, Berkeley
| | - J Simon Wiegert
- University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Neurophysiology, MCTN, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Hillel Adesnik
- Department of Molecular and Cell Biology, University of California, Berkeley
- The Helen Wills Neuroscience Institute
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42
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Berners-Lee A, Shtrahman E, Grimaud J, Murthy VN. Experience-dependent evolution of odor mixture representations in piriform cortex. PLoS Biol 2023; 21:e3002086. [PMID: 37098044 PMCID: PMC10129003 DOI: 10.1371/journal.pbio.3002086] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 03/17/2023] [Indexed: 04/26/2023] Open
Abstract
Rodents can learn from exposure to rewarding odors to make better and quicker decisions. The piriform cortex is thought to be important for learning complex odor associations; however, it is not understood exactly how it learns to remember discriminations between many, sometimes overlapping, odor mixtures. We investigated how odor mixtures are represented in the posterior piriform cortex (pPC) of mice while they learn to discriminate a unique target odor mixture against hundreds of nontarget mixtures. We find that a significant proportion of pPC neurons discriminate between the target and all other nontarget odor mixtures. Neurons that prefer the target odor mixture tend to respond with brief increases in firing rate at odor onset compared to other neurons, which exhibit sustained and/or decreased firing. We allowed mice to continue training after they had reached high levels of performance and find that pPC neurons become more selective for target odor mixtures as well as for randomly chosen repeated nontarget odor mixtures that mice did not have to discriminate from other nontargets. These single unit changes during overtraining are accompanied by better categorization decoding at the population level, even though behavioral metrics of mice such as reward rate and latency to respond do not change. However, when difficult ambiguous trial types are introduced, the robustness of the target selectivity is correlated with better performance on the difficult trials. Taken together, these data reveal pPC as a dynamic and robust system that can optimize for both current and possible future task demands at once.
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Affiliation(s)
- Alice Berners-Lee
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States of America
| | - Elizabeth Shtrahman
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States of America
| | - Julien Grimaud
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States of America
- Cell Engineering Laboratory (CellTechs), Sup'Biotech, Villejuif, France
| | - Venkatesh N Murthy
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States of America
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43
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Orlandi JG, Abdolrahmani M, Aoki R, Lyamzin DR, Benucci A. Distributed context-dependent choice information in mouse posterior cortex. Nat Commun 2023; 14:192. [PMID: 36635318 PMCID: PMC9837177 DOI: 10.1038/s41467-023-35824-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 01/03/2023] [Indexed: 01/14/2023] Open
Abstract
Choice information appears in multi-area brain networks mixed with sensory, motor, and cognitive variables. In the posterior cortex-traditionally implicated in decision computations-the presence, strength, and area specificity of choice signals are highly variable, limiting a cohesive understanding of their computational significance. Examining the mesoscale activity in the mouse posterior cortex during a visual task, we found that choice signals defined a decision variable in a low-dimensional embedding space with a prominent contribution along the ventral visual stream. Their subspace was near-orthogonal to concurrently represented sensory and motor-related activations, with modulations by task difficulty and by the animals' attention state. A recurrent neural network trained with animals' choices revealed an equivalent decision variable whose context-dependent dynamics agreed with that of the neural data. Our results demonstrated an independent, multi-area decision variable in the posterior cortex, controlled by task features and cognitive demands, possibly linked to contextual inference computations in dynamic animal-environment interactions.
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Affiliation(s)
- Javier G Orlandi
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan.,Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, T2N 1N4, Canada
| | | | - Ryo Aoki
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan
| | - Dmitry R Lyamzin
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan
| | - Andrea Benucci
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan. .,University of Tokyo, Graduate School of Information Science and Technology, Department of Mathematical Informatics, 1-1-1 Yayoi, Bunkyo City, Tokyo, 113-0032, Japan.
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44
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Verdier A, Dominique N, Groussard D, Aldanondo A, Bathellier B, Bagur S. Enhanced perceptual task performance without deprivation in mice using medial forebrain bundle stimulation. CELL REPORTS METHODS 2022; 2:100355. [PMID: 36590697 PMCID: PMC9795331 DOI: 10.1016/j.crmeth.2022.100355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 10/04/2022] [Accepted: 11/09/2022] [Indexed: 05/11/2023]
Abstract
Perceptual decision-making tasks are essential to many fields of neuroscience. Current protocols generally reward deprived animals with water. However, balancing animals' deprivation level with their well-being is challenging, and trial number is limited by satiation. Here, we present electrical stimulation of the medial forebrain bundle (MFB) as an alternative that avoids deprivation while yielding stable motivation for thousands of trials. Using licking or lever press as a report, MFB animals learnt auditory discrimination tasks at similar speed to water-deprived mice. Moreover, they more reliably reached higher accuracy in harder tasks, performing up to 4,500 trials per session without loss of motivation. MFB stimulation did not impact the underlying sensory behavior since psychometric parameters and response times are preserved. MFB mice lacked signs of metabolic or behavioral stress compared with water-deprived mice. Overall, MFB stimulation is a highly promising tool for task learning because it enhances task performance while avoiding deprivation.
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Affiliation(s)
- Antonin Verdier
- Institut de l’Audition, Institut Pasteur, Université de Paris, INSERM, 75012 Paris, France
| | - Noémi Dominique
- Institut Pasteur, Université Paris Cité, DT, Animalerie Centrale, 75724 Paris, France
| | - Déborah Groussard
- Institut Pasteur, Université Paris Cité, DT, Animalerie Centrale, 75724 Paris, France
| | - Anna Aldanondo
- Institut de l’Audition, Institut Pasteur, Université de Paris, INSERM, 75012 Paris, France
| | - Brice Bathellier
- Institut de l’Audition, Institut Pasteur, Université de Paris, INSERM, 75012 Paris, France
| | - Sophie Bagur
- Institut de l’Audition, Institut Pasteur, Université de Paris, INSERM, 75012 Paris, France
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45
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Barkus C, Bergmann C, Branco T, Carandini M, Chadderton PT, Galiñanes GL, Gilmour G, Huber D, Huxter JR, Khan AG, King AJ, Maravall M, O'Mahony T, Ragan CI, Robinson ESJ, Schaefer AT, Schultz SR, Sengpiel F, Prescott MJ. Refinements to rodent head fixation and fluid/food control for neuroscience. J Neurosci Methods 2022; 381:109705. [PMID: 36096238 PMCID: PMC7617528 DOI: 10.1016/j.jneumeth.2022.109705] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 09/01/2022] [Accepted: 09/03/2022] [Indexed: 12/14/2022]
Abstract
The use of head fixation in mice is increasingly common in research, its use having initially been restricted to the field of sensory neuroscience. Head restraint has often been combined with fluid control, rather than food restriction, to motivate behaviour, but this too is now in use for both restrained and non-restrained animals. Despite this, there is little guidance on how best to employ these techniques to optimise both scientific outcomes and animal welfare. This article summarises current practices and provides recommendations to improve animal wellbeing and data quality, based on a survey of the community, literature reviews, and the expert opinion and practical experience of an international working group convened by the UK's National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs). Topics covered include head fixation surgery and post-operative care, habituation to restraint, and the use of fluid/food control to motivate performance. We also discuss some recent developments that may offer alternative ways to collect data from large numbers of behavioural trials without the need for restraint. The aim is to provide support for researchers at all levels, animal care staff, and ethics committees to refine procedures and practices in line with the refinement principle of the 3Rs.
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Affiliation(s)
- Chris Barkus
- National Centre for Replacement, Refinement and Reduction of Animals in Research (NC3Rs), London, UK.
| | | | - Tiago Branco
- Sainsbury Wellcome Centre, University College London, London, UK
| | - Matteo Carandini
- Institute of Ophthalmology, University College London, London, UK
| | - Paul T Chadderton
- School of Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, UK
| | | | | | - Daniel Huber
- Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland
| | | | - Adil G Khan
- Centre for Developmental Neurobiology, King's College London, London, UK
| | - Andrew J King
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
| | - Miguel Maravall
- Sussex Neuroscience, School of Life Sciences, University of Sussex, Brighton, UK
| | - Tina O'Mahony
- Sainsbury Wellcome Centre, University College London, London, UK
| | - C Ian Ragan
- National Centre for Replacement, Refinement and Reduction of Animals in Research (NC3Rs), London, UK
| | - Emma S J Robinson
- School of Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, UK
| | - Andreas T Schaefer
- Sensory Circuits and Neurotechnology Laboratory, The Francis Crick Institute, London, UK; Department of Neuroscience, Physiology & Pharmacology, University College London, London, UK
| | - Simon R Schultz
- Centre for Neurotechnology and Department of Bioengineering, Imperial College London, London, UK
| | | | - Mark J Prescott
- National Centre for Replacement, Refinement and Reduction of Animals in Research (NC3Rs), London, UK
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46
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Peters AJ, Marica AM, Fabre JMJ, Harris KD, Carandini M. Visuomotor learning promotes visually evoked activity in the medial prefrontal cortex. Cell Rep 2022; 41:111487. [PMID: 36261004 PMCID: PMC9631115 DOI: 10.1016/j.celrep.2022.111487] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 08/03/2022] [Accepted: 09/21/2022] [Indexed: 12/05/2022] Open
Abstract
The medial prefrontal cortex (mPFC) is necessary for executing many learned associations between stimuli and movement. It is unclear, however, how activity in the mPFC evolves across learning, and how this activity correlates with sensory stimuli and the learned movements they evoke. To address these questions, we record cortical activity with widefield calcium imaging while mice learned to associate a visual stimulus with a forelimb movement. After learning, the mPFC shows stimulus-evoked activity both during task performance and during passive viewing, when the stimulus evokes no action. This stimulus-evoked activity closely tracks behavioral performance across training, with both exhibiting a marked increase between days when mice first learn the task, followed by a steady increase with further training. Electrophysiological recordings localized this activity to the secondary motor and anterior cingulate cortex. We conclude that learning a visuomotor task promotes a route for visual information to reach the prefrontal cortex.
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Affiliation(s)
- Andrew J Peters
- UCL Institute of Ophthalmology, University College London, London, UK.
| | | | - Julie M J Fabre
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Kenneth D Harris
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Matteo Carandini
- UCL Institute of Ophthalmology, University College London, London, UK.
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47
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Lee JJ, Krumin M, Harris KD, Carandini M. Task specificity in mouse parietal cortex. Neuron 2022; 110:2961-2969.e5. [PMID: 35963238 PMCID: PMC9616730 DOI: 10.1016/j.neuron.2022.07.017] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 01/16/2022] [Accepted: 07/15/2022] [Indexed: 11/26/2022]
Abstract
Parietal cortex is implicated in a variety of behavioral processes, but it is unknown whether and how its individual neurons participate in multiple tasks. We trained head-fixed mice to perform two visual decision tasks involving a steering wheel or a virtual T-maze and recorded from the same parietal neurons during these two tasks. Neurons that were active during the T-maze task were typically inactive during the steering-wheel task and vice versa. Recording from the same neurons in the same apparatus without task stimuli yielded the same specificity as in the task, suggesting that task specificity depends on physical context. To confirm this, we trained some mice in a third task combining the steering wheel context with the visual environment of the T-maze. This hybrid task engaged the same neurons as those engaged in the steering-wheel task. Thus, participation by neurons in mouse parietal cortex is task specific, and this specificity is determined by physical context.
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Affiliation(s)
- Julie J Lee
- UCL Institute of Ophthalmology, University College London, Gower Street, London WC1E 6AE, UK.
| | - Michael Krumin
- UCL Institute of Ophthalmology, University College London, Gower Street, London WC1E 6AE, UK
| | - Kenneth D Harris
- UCL Queen Square Institute of Neurology, University College London, Gower Street, London WC1E 6AE, UK
| | - Matteo Carandini
- UCL Institute of Ophthalmology, University College London, Gower Street, London WC1E 6AE, UK
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Meister M. Learning, fast and slow. Curr Opin Neurobiol 2022; 75:102555. [PMID: 35617751 DOI: 10.1016/j.conb.2022.102555] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/18/2022] [Accepted: 04/18/2022] [Indexed: 11/28/2022]
Abstract
Animals can learn efficiently from a single experience and change their future behavior in response. However, in other instances, animals learn very slowly, requiring thousands of experiences. Here, I survey tasks involving fast and slow learning and consider some hypotheses for what differentiates the underlying neural mechanisms. It has been proposed that fast learning relies on neural representations that favor efficient Hebbian modification of synapses. These efficient representations may be encoded in the genome, resulting in a repertoire of fast learning that differs across species. Alternatively, the required neural representations may be acquired from experience through a slow process of unsupervised learning from the environment.
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Affiliation(s)
- Markus Meister
- Division of Biology and Biological Engineering, Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, United States.
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Oude Lohuis MN, Pie JL, Marchesi P, Montijn JS, de Kock CPJ, Pennartz CMA, Olcese U. Multisensory task demands temporally extend the causal requirement for visual cortex in perception. Nat Commun 2022; 13:2864. [PMID: 35606448 PMCID: PMC9126973 DOI: 10.1038/s41467-022-30600-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 05/09/2022] [Indexed: 01/02/2023] Open
Abstract
Primary sensory areas constitute crucial nodes during perceptual decision making. However, it remains unclear to what extent they mainly constitute a feedforward processing step, or rather are continuously involved in a recurrent network together with higher-order areas. We found that the temporal window in which primary visual cortex is required for the detection of identical visual stimuli was extended when task demands were increased via an additional sensory modality that had to be monitored. Late-onset optogenetic inactivation preserved bottom-up, early-onset responses which faithfully encoded stimulus features, and was effective in impairing detection only if it preceded a late, report-related phase of the cortical response. Increasing task demands were marked by longer reaction times and the effect of late optogenetic inactivation scaled with reaction time. Thus, independently of visual stimulus complexity, multisensory task demands determine the temporal requirement for ongoing sensory-related activity in V1, which overlaps with report-related activity. How primary sensory cortices contribute to decision making remains poorly understood. Here the authors report that increasing task demands extend the temporal window in which the primary visual cortex is required for detecting identical stimuli.
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50
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Manita S, Ikezoe K, Kitamura K. A Novel Device of Reaching, Grasping, and Retrieving Task for Head-Fixed Mice. Front Neural Circuits 2022; 16:842748. [PMID: 35633733 PMCID: PMC9133411 DOI: 10.3389/fncir.2022.842748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 04/25/2022] [Indexed: 11/13/2022] Open
Abstract
Reaching, grasping, and retrieving movements are essential to our daily lives and are common in many mammalian species. To understand the mechanism for controlling this movement at the neural circuit level, it is necessary to observe the activity of individual neurons involved in the movement. For stable electrophysiological or optical recordings of neural activity in a behaving animal, head fixation effectively minimizes motion artifacts. Here, we developed a new device that allows mice to perform reaching, grasping, and retrieving movements during head fixation. In this method, agar cubes were presented as target objects in front of water-restricted mice, and the mice were able to reach, grasp, and retrieve them with their forelimb. The agar cubes were supplied by a custom-made automatic dispenser, which uses a microcontroller to control the two motors to push out the agar cubes. This agar presentation system supplied approximately 20 agar cubes in consecutive trials. We confirmed that each agar cube could be presented to the mouse with an average weight of 55 ± 3 mg and positional accuracy of less than 1 mm. Using this system, we showed that head-fixed mice could perform reaching, grasping, and retrieving tasks after 1 week of training. When the agar cube was placed near the mice, they could grasp it with a high success rate without extensive training. On the other hand, when the agar cube was presented far from the mice, the success rate was initially low and increased with subsequent test sessions. Furthermore, we showed that activity in the primary motor cortex is required for reaching movements in this task. Therefore, our system can be used to study neural circuit mechanisms for the control and learning of reaching, grasping, and retrieving movements under head-fixed conditions.
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