1
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Shinn M. Phantom oscillations in principal component analysis. Proc Natl Acad Sci U S A 2023; 120:e2311420120. [PMID: 37988465 PMCID: PMC10691246 DOI: 10.1073/pnas.2311420120] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 10/18/2023] [Indexed: 11/23/2023] Open
Abstract
Principal component analysis (PCA) is a dimensionality reduction method that is known for being simple and easy to interpret. Principal components are often interpreted as low-dimensional patterns in high-dimensional space. However, this simple interpretation fails for timeseries, spatial maps, and other continuous data. In these cases, nonoscillatory data may have oscillatory principal components. Here, we show that two common properties of data cause oscillatory principal components: smoothness and shifts in time or space. These two properties implicate almost all neuroscience data. We show how the oscillations produced by PCA, which we call "phantom oscillations," impact data analysis. We also show that traditional cross-validation does not detect phantom oscillations, so we suggest procedures that do. Our findings are supported by a collection of mathematical proofs. Collectively, our work demonstrates that patterns which emerge from high-dimensional data analysis may not faithfully represent the underlying data.
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Affiliation(s)
- Maxwell Shinn
- University College London (UCL) Queen Square Institute of Neurology, University College London, LondonWC1E 6BT, United Kingdom
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2
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Gharesi N, Luneau L, Kalaska JF, Baillet S. Evaluation of abstract rule-based associations in the human premotor cortex during passive observation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.06.543581. [PMID: 37333191 PMCID: PMC10274620 DOI: 10.1101/2023.06.06.543581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Decision-making often manifests in behavior, typically yielding overt motor actions. This complex process requires the registration of sensory information with one's internal representation of the current context, before a categorical judgment of the most appropriate motor behavior can be issued. The construct concept of embodied decision-making encapsulates this sequence of complex processes, whereby behaviorally salient information from the environment is represented in an abstracted space of potential motor actions rather than only in an abstract cognitive "decision" space. Theoretical foundations and some empirical evidence account for support the involvement of premotor cortical circuits in embodied cognitive functions. Animal models show that premotor circuits participate in the registration and evaluation of actions performed by peers in social situations, that is, prior to controlling one's voluntary movements guided by arbitrary stimulus-response rules. However, such evidence from human data is currently limited. Here we used time-resolved magnetoencephalography imaging to characterize activations of the premotor cortex as human participants observed arbitrary, non-biological visual stimuli that either respected or violated a simple stimulus-response association rule. The participants had learned this rule previously, either actively, by performing a motor task (active learning), or passively, by observing a computer perform the same task (passive learning). We discovered that the human premotor cortex is activated during the passive observation of the correct execution of a sequence of events according to a rule learned previously. Premotor activation also differs when the subjects observe incorrect stimulus sequences. These premotor effects are present even when the observed events are of a non-motor, abstract nature, and even when the stimulus-response association rule was learned via passive observations of a computer agent performing the task, without requiring overt motor actions from the human participant. We found evidence of these phenomena by tracking cortical beta-band signaling in temporal alignment with the observation of task events and behavior. We conclude that premotor cortical circuits that are typically engaged during voluntary motor behavior are also involved in the interpretation of events of a non-ecological, unfamiliar nature but related to a learned abstract rule. As such, the present study provides the first evidence of neurophysiological processes of embodied decision-making in human premotor circuits when the observed events do not involve motor actions of a third party.
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Affiliation(s)
- Niloofar Gharesi
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Lucie Luneau
- Groupe de recherche sur la signalisation neuronale et la circuiterie, Département de Neurosciences, Université de Montréal, Montréal, QC, Canada
| | - John F Kalaska
- Groupe de recherche sur la signalisation neuronale et la circuiterie, Département de Neurosciences, Université de Montréal, Montréal, QC, Canada
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
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3
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Kira S, Safaai H, Morcos AS, Panzeri S, Harvey CD. A distributed and efficient population code of mixed selectivity neurons for flexible navigation decisions. Nat Commun 2023; 14:2121. [PMID: 37055431 PMCID: PMC10102117 DOI: 10.1038/s41467-023-37804-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 03/30/2023] [Indexed: 04/15/2023] Open
Abstract
Decision-making requires flexibility to rapidly switch one's actions in response to sensory stimuli depending on information stored in memory. We identified cortical areas and neural activity patterns underlying this flexibility during virtual navigation, where mice switched navigation toward or away from a visual cue depending on its match to a remembered cue. Optogenetics screening identified V1, posterior parietal cortex (PPC), and retrosplenial cortex (RSC) as necessary for accurate decisions. Calcium imaging revealed neurons that can mediate rapid navigation switches by encoding a mixture of a current and remembered visual cue. These mixed selectivity neurons emerged through task learning and predicted the mouse's choices by forming efficient population codes before correct, but not incorrect, choices. They were distributed across posterior cortex, even V1, and were densest in RSC and sparsest in PPC. We propose flexibility in navigation decisions arises from neurons that mix visual and memory information within a visual-parietal-retrosplenial network.
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Affiliation(s)
- Shinichiro Kira
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Houman Safaai
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Ari S Morcos
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Stefano Panzeri
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
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4
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Abstract
While working memory (WM) allows us to store past information, its function is to guide future behavior. Given this role, the tight link between how WMs are maintained and how they are read out to be transformed into context-appropriate actions remains relatively unexplored. Beyond helping us understand memory-guided behavior, focusing on WM readout may also help us better understand the neural basis of memory maintenance.
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5
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Transition of distinct context-dependent ensembles from secondary to primary motor cortex in skilled motor performance. Cell Rep 2022; 41:111494. [DOI: 10.1016/j.celrep.2022.111494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 06/27/2022] [Accepted: 09/21/2022] [Indexed: 11/19/2022] Open
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6
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Ehrlich DB, Murray JD. Geometry of neural computation unifies working memory and planning. Proc Natl Acad Sci U S A 2022; 119:e2115610119. [PMID: 36067286 PMCID: PMC9478653 DOI: 10.1073/pnas.2115610119] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Real-world tasks require coordination of working memory, decision-making, and planning, yet these cognitive functions have disproportionately been studied as independent modular processes in the brain. Here, we propose that contingency representations, defined as mappings for how future behaviors depend on upcoming events, can unify working memory and planning computations. We designed a task capable of disambiguating distinct types of representations. In task-optimized recurrent neural networks, we investigated possible circuit mechanisms for contingency representations and found that these representations can explain neurophysiological observations from the prefrontal cortex during working memory tasks. Our experiments revealed that human behavior is consistent with contingency representations and not with traditional sensory models of working memory. Finally, we generated falsifiable predictions for neural data to identify contingency representations in neural data and to dissociate different models of working memory. Our findings characterize a neural representational strategy that can unify working memory, planning, and context-dependent decision-making.
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Affiliation(s)
- Daniel B. Ehrlich
- aInterdepartmental Neuroscience Program, Yale University, New Haven, CT 06510
- bDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT 06510
| | - John D. Murray
- aInterdepartmental Neuroscience Program, Yale University, New Haven, CT 06510
- bDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT 06510
- 1To whom correspondence may be addressed.
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7
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Wang T, Chen Y, Cui H. From Parametric Representation to Dynamical System: Shifting Views of the Motor Cortex in Motor Control. Neurosci Bull 2022; 38:796-808. [PMID: 35298779 PMCID: PMC9276910 DOI: 10.1007/s12264-022-00832-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 11/29/2021] [Indexed: 11/01/2022] Open
Abstract
In contrast to traditional representational perspectives in which the motor cortex is involved in motor control via neuronal preference for kinetics and kinematics, a dynamical system perspective emerging in the last decade views the motor cortex as a dynamical machine that generates motor commands by autonomous temporal evolution. In this review, we first look back at the history of the representational and dynamical perspectives and discuss their explanatory power and controversy from both empirical and computational points of view. Here, we aim to reconcile the above perspectives, and evaluate their theoretical impact, future direction, and potential applications in brain-machine interfaces.
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Affiliation(s)
- Tianwei Wang
- Center for Excellence in Brain Science and Intelligent Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China.,Shanghai Center for Brain and Brain-inspired Intelligence Technology, Shanghai, 200031, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yun Chen
- Center for Excellence in Brain Science and Intelligent Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China.,Shanghai Center for Brain and Brain-inspired Intelligence Technology, Shanghai, 200031, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - He Cui
- Center for Excellence in Brain Science and Intelligent Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China. .,Shanghai Center for Brain and Brain-inspired Intelligence Technology, Shanghai, 200031, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China.
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8
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To deconvolve, or not to deconvolve: Inferences of neuronal activities using calcium imaging data. J Neurosci Methods 2022; 366:109431. [PMID: 34856319 DOI: 10.1016/j.jneumeth.2021.109431] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 11/25/2021] [Accepted: 11/26/2021] [Indexed: 01/19/2023]
Abstract
BACKGROUND With the increasing popularity of calcium imaging in neuroscience research, choosing the right methods to analyze calcium imaging data is critical to address various scientific questions. Unlike spike trains measured using electrodes, fluorescence intensity traces provide an indirect and noisy measurement of the underlying neuronal activities. The observed calcium traces are either analyzed directly or deconvolved to spike trains to infer neuronal activities. When both approaches are applicable, it is unclear whether deconvolving calcium traces is a necessary step. METHODS In this article, we compare the performance of using calcium traces or their deconvolved spike trains for three common analyses: clustering, principal component analysis (PCA), and population decoding. RESULTS We found that (1) the two approaches lead to diverging results; (2) estimated spike trains, when smoothed or binned appropriately, usually lead to satisfactory performances, such as more accurate estimation of cluster membership; (3) although estimate spike train produce results more similar to true spike data than trace data, we found that the PCA results from trace data might better reflect the underlying neuronal ensembles (clusters); and (4) for both approaches, decobability can be improved by using denoising or smoothing methods. COMPARISON WITH EXISTING METHODS Our simulations and applications to real data suggest that estimated spike data outperform trace data in cluster analysis and give comparable results for population decoding. In addition, the decobability of estimated spike data can be slightly better than that of calcium trace data with appropriate filtering / smoothing methods. CONCLUSION We conclude that spike detection might be a useful pre-processing step for certain problems such as clustering; however, the continuous nature of calcium imaging data provides a natural smoothness that might be helpful for problems such as dimensional reduction.
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9
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Shared population-level dynamics in monkey premotor cortex during solo action, joint action and action observation. Prog Neurobiol 2021; 210:102214. [PMID: 34979174 DOI: 10.1016/j.pneurobio.2021.102214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 11/25/2021] [Accepted: 12/23/2021] [Indexed: 11/23/2022]
Abstract
Studies of neural population dynamics of cell activity from monkey motor areas during reaching show that it mostly represents the generation and timing of motor behavior. We compared neural dynamics in dorsal premotor cortex (PMd) during the performance of a visuomotor task executed individually or cooperatively and during an observation task. In the visuomotor conditions, monkeys applied isometric forces on a joystick to guide a visual cursor in different directions, either alone or jointly with a conspecific. In the observation condition, they observed the cursor's motion guided by the partner. We found that in PMd neural dynamics were widely shared across action execution and observation, with cursor motion directions more accurately discriminated than task types. This suggests that PMd encodes spatial aspects irrespective of specific behavioral demands. Furthermore, our results suggest that largest components of premotor population dynamics, which have previously been suggested to reflect a transformation from planning to movement execution, may rather reflect higher cognitive-motor processes, such as the covert representation of actions and goals shared across tasks that require movement and those that do not.
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10
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Zheng Q, Zhou L, Gu Y. Temporal synchrony effects of optic flow and vestibular inputs on multisensory heading perception. Cell Rep 2021; 37:109999. [PMID: 34788608 DOI: 10.1016/j.celrep.2021.109999] [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: 10/14/2020] [Revised: 08/21/2021] [Accepted: 10/21/2021] [Indexed: 11/25/2022] Open
Abstract
Precise heading perception requires integration of optic flow and vestibular cues, yet the two cues often carry distinct temporal dynamics that may confound cue integration benefit. Here, we varied temporal offset between the two sensory inputs while macaques discriminated headings around straight ahead. We find the best heading performance does not occur under natural condition of synchronous inputs with zero offset but rather when visual stimuli are artificially adjusted to lead vestibular by a few hundreds of milliseconds. This amount exactly matches the lag between the vestibular acceleration and visual speed signals as measured from single-unit-activity in frontal and posterior parietal cortices. Manually aligning cues in these areas best facilitates integration with some nonlinear gain modulation effects. These findings are consistent with predictions from a model by which the brain integrates optic flow speed with a faster vestibular acceleration signal for sensing instantaneous heading direction during self-motion in the environment.
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Affiliation(s)
- Qihao Zheng
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, 200031 Shanghai, China; University of Chinese Academy of Sciences, 100049 Beijing, China
| | - Luxin Zhou
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, 200031 Shanghai, China; University of Chinese Academy of Sciences, 100049 Beijing, China
| | - Yong Gu
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, 200031 Shanghai, China; University of Chinese Academy of Sciences, 100049 Beijing, China; Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, 201210 Shanghai, China.
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11
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Lenc T, Merchant H, Keller PE, Honing H, Varlet M, Nozaradan S. Mapping between sound, brain and behaviour: four-level framework for understanding rhythm processing in humans and non-human primates. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200325. [PMID: 34420381 PMCID: PMC8380981 DOI: 10.1098/rstb.2020.0325] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/14/2021] [Indexed: 12/16/2022] Open
Abstract
Humans perceive and spontaneously move to one or several levels of periodic pulses (a meter, for short) when listening to musical rhythm, even when the sensory input does not provide prominent periodic cues to their temporal location. Here, we review a multi-levelled framework to understanding how external rhythmic inputs are mapped onto internally represented metric pulses. This mapping is studied using an approach to quantify and directly compare representations of metric pulses in signals corresponding to sensory inputs, neural activity and behaviour (typically body movement). Based on this approach, recent empirical evidence can be drawn together into a conceptual framework that unpacks the phenomenon of meter into four levels. Each level highlights specific functional processes that critically enable and shape the mapping from sensory input to internal meter. We discuss the nature, constraints and neural substrates of these processes, starting with fundamental mechanisms investigated in macaque monkeys that enable basic forms of mapping between simple rhythmic stimuli and internally represented metric pulse. We propose that human evolution has gradually built a robust and flexible system upon these fundamental processes, allowing more complex levels of mapping to emerge in musical behaviours. This approach opens promising avenues to understand the many facets of rhythmic behaviours across individuals and species. This article is part of the theme issue 'Synchrony and rhythm interaction: from the brain to behavioural ecology'.
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Affiliation(s)
- Tomas Lenc
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, New South Wales 2751, Australia
- Institute of Neuroscience (IONS), Université Catholique de Louvain (UCL), Brussels 1200, Belgium
| | - Hugo Merchant
- Instituto de Neurobiologia, UNAM, Campus Juriquilla, Querétaro 76230, Mexico
| | - Peter E. Keller
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, New South Wales 2751, Australia
| | - Henkjan Honing
- Amsterdam Brain and Cognition (ABC), Institute for Logic, Language and Computation (ILLC), University of Amsterdam, Amsterdam 1090 GE, The Netherlands
| | - Manuel Varlet
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, New South Wales 2751, Australia
- School of Psychology, Western Sydney University, Penrith, New South Wales 2751, Australia
| | - Sylvie Nozaradan
- Institute of Neuroscience (IONS), Université Catholique de Louvain (UCL), Brussels 1200, Belgium
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12
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Zhou Y, Rosen MC, Swaminathan SK, Masse NY, Zhu O, Freedman DJ. Distributed functions of prefrontal and parietal cortices during sequential categorical decisions. eLife 2021; 10:e58782. [PMID: 34491201 PMCID: PMC8423442 DOI: 10.7554/elife.58782] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 07/13/2021] [Indexed: 12/19/2022] Open
Abstract
Comparing sequential stimuli is crucial for guiding complex behaviors. To understand mechanisms underlying sequential decisions, we compared neuronal responses in the prefrontal cortex (PFC), the lateral intraparietal (LIP), and medial intraparietal (MIP) areas in monkeys trained to decide whether sequentially presented stimuli were from matching (M) or nonmatching (NM) categories. We found that PFC leads M/NM decisions, whereas LIP and MIP appear more involved in stimulus evaluation and motor planning, respectively. Compared to LIP, PFC showed greater nonlinear integration of currently visible and remembered stimuli, which correlated with the monkeys' M/NM decisions. Furthermore, multi-module recurrent networks trained on the same task exhibited key features of PFC and LIP encoding, including nonlinear integration in the PFC-like module, which was causally involved in the networks' decisions. Network analysis found that nonlinear units have stronger and more widespread connections with input, output, and within-area units, indicating putative circuit-level mechanisms for sequential decisions.
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Affiliation(s)
- Yang Zhou
- Department of Neurobiology, The University of ChicagoChicagoUnited States
- School of Psychological and Cognitive Sciences, PKU-IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Peking UniversityBeijingChina
| | - Matthew C Rosen
- Department of Neurobiology, The University of ChicagoChicagoUnited States
| | | | - Nicolas Y Masse
- Department of Neurobiology, The University of ChicagoChicagoUnited States
| | - Ou Zhu
- Department of Neurobiology, The University of ChicagoChicagoUnited States
| | - David J Freedman
- Department of Neurobiology, The University of ChicagoChicagoUnited States
- Neuroscience Institute, The University of ChicagoChicagoUnited States
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13
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Curtis CE, Sprague TC. Persistent Activity During Working Memory From Front to Back. Front Neural Circuits 2021; 15:696060. [PMID: 34366794 PMCID: PMC8334735 DOI: 10.3389/fncir.2021.696060] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 06/28/2021] [Indexed: 01/06/2023] Open
Abstract
Working memory (WM) extends the duration over which information is available for processing. Given its importance in supporting a wide-array of high level cognitive abilities, uncovering the neural mechanisms that underlie WM has been a primary goal of neuroscience research over the past century. Here, we critically review what we consider the two major "arcs" of inquiry, with a specific focus on findings that were theoretically transformative. For the first arc, we briefly review classic studies that led to the canonical WM theory that cast the prefrontal cortex (PFC) as a central player utilizing persistent activity of neurons as a mechanism for memory storage. We then consider recent challenges to the theory regarding the role of persistent neural activity. The second arc, which evolved over the last decade, stemmed from sophisticated computational neuroimaging approaches enabling researchers to decode the contents of WM from the patterns of neural activity in many parts of the brain including early visual cortex. We summarize key findings from these studies, their implications for WM theory, and finally the challenges these findings pose. Our goal in doing so is to identify barriers to developing a comprehensive theory of WM that will require a unification of these two "arcs" of research.
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Affiliation(s)
- Clayton E. Curtis
- Department of Psychology, New York University, New York, NY, United States
- Center for Neural Science, New York University, New York, NY, United States
| | - Thomas C. Sprague
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, United States
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14
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Fricke C, Gentner R, Alizadeh J, Classen J. Linking Individual Movements to a Skilled Repertoire: Fast Modulation of Motor Synergies by Repetition of Stereotyped Movements. Cereb Cortex 2021; 30:1185-1198. [PMID: 31386110 DOI: 10.1093/cercor/bhz159] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 06/21/2019] [Accepted: 06/25/2019] [Indexed: 01/15/2023] Open
Abstract
Motor skills emerge when practicing individual movements enables the motor system to extract building instructions that facilitate the generation of future diverse movements. Here we asked how practicing stereotyped movements for minutes affects motor synergies that encode human motor skills acquired over years of training. Participants trained a kinematically highly constrained combined index-finger and thumb movement. Before and after training, finger movements were evoked at rest by transcranial magnetic stimulation (TMS). Post-training, the angle between posture vectors describing TMS-evoked movements and the training movements temporarily decreased, suggesting the presence of a short-term memory for the trained movement. Principal component analysis was used to identify joint covariance patterns in TMS-evoked movements. The quality of reconstruction of training or grasping movements from linear combinations of a small subset of these TMS-derived synergies was used as an index of neural efficiency of movement generation. The reconstruction quality increased for the trained movement but remained constant for grasping movements. These findings suggest that the motor system rapidly reorganizes to enhance the coding efficiency of a difficult movement without compromising the coding efficiency of overlearned movements. Practice of individual movements may drive an unsupervised bottom-up process that ultimately shapes synergistic neuronal organization by constant competition of action memories.
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Affiliation(s)
| | - Reinhard Gentner
- Department of Neurology, Liebigstrasse 20, 04103 Leipzig, Germany
| | - Jalal Alizadeh
- Department of Neurology, Liebigstrasse 20, 04103 Leipzig, Germany
| | - Joseph Classen
- Department of Neurology, Liebigstrasse 20, 04103 Leipzig, Germany
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15
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Sachuriga, Nishimaru H, Takamura Y, Matsumoto J, Ferreira Pereira de Araújo M, Ono T, Nishijo H. Neuronal Representation of Locomotion During Motivated Behavior in the Mouse Anterior Cingulate Cortex. Front Syst Neurosci 2021; 15:655110. [PMID: 33994964 PMCID: PMC8116624 DOI: 10.3389/fnsys.2021.655110] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 02/26/2021] [Indexed: 11/24/2022] Open
Abstract
The anterior cingulate cortex (ACC) is located within the dorsomedial prefrontal cortex (PFC), and processes and facilitates goal-directed behaviors relating to emotion, reward, and motor control. However, it is unclear how ACC neurons dynamically encode motivated behavior during locomotion. In this study, we examined how information for locomotion and behavioral outcomes is temporally represented by individual and ensembles of ACC neurons in mice during a self-paced locomotor reward-based task. By recording and analyzing the activity of ACC neurons with a microdrive tetrode array while the mouse performed the locomotor task, we found that more than two-fifths of the neurons showed phasic activity relating to locomotion or the reward behavior. Some of these neurons showed significant differences in their firing rate depending on the behavioral outcome. Furthermore, by applying a demixed principal component analysis, the ACC population activity was decomposed into components representing locomotion and the previous/future outcome. These results indicated that ACC neurons dynamically integrate motor and behavioral inputs during goal-directed behaviors.
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Affiliation(s)
- Sachuriga
- System Emotional Science, Faculty of Medicine, University of Toyama, Toyama, Japan.,Graduate School of Innovative Life Science, University of Toyama, Toyama, Japan
| | - Hiroshi Nishimaru
- System Emotional Science, Faculty of Medicine, University of Toyama, Toyama, Japan.,Graduate School of Innovative Life Science, University of Toyama, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Yusaku Takamura
- System Emotional Science, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Jumpei Matsumoto
- System Emotional Science, Faculty of Medicine, University of Toyama, Toyama, Japan.,Graduate School of Innovative Life Science, University of Toyama, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | | | - Taketoshi Ono
- System Emotional Science, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Hisao Nishijo
- System Emotional Science, Faculty of Medicine, University of Toyama, Toyama, Japan.,Graduate School of Innovative Life Science, University of Toyama, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
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16
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Rossi-Pool R, Zainos A, Alvarez M, Diaz-deLeon G, Romo R. A continuum of invariant sensory and behavioral-context perceptual coding in secondary somatosensory cortex. Nat Commun 2021; 12:2000. [PMID: 33790301 PMCID: PMC8012659 DOI: 10.1038/s41467-021-22321-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 03/08/2021] [Indexed: 11/08/2022] Open
Abstract
A crucial role of cortical networks is the conversion of sensory inputs into perception. In the cortical somatosensory network, neurons of the primary somatosensory cortex (S1) show invariant sensory responses, while frontal lobe neuronal activity correlates with the animal's perceptual behavior. Here, we report that in the secondary somatosensory cortex (S2), neurons with invariant sensory responses coexist with neurons whose responses correlate with perceptual behavior. Importantly, the vast majority of the neurons fall along a continuum of combined sensory and categorical dynamics. Furthermore, during a non-demanding control task, the sensory responses remain unaltered while the sensory information exhibits an increase. However, perceptual responses and the associated categorical information decrease, implicating a task context-dependent processing mechanism. Conclusively, S2 neurons exhibit intriguing dynamics that are intermediate between those of S1 and frontal lobe. Our results contribute relevant evidence about the role that S2 plays in the conversion of touch into perception.
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Affiliation(s)
- Román Rossi-Pool
- Instituto de Fisiología Celular─Neurociencias, Universidad Nacional Autónoma de México, Mexico City, Mexico.
| | - Antonio Zainos
- Instituto de Fisiología Celular─Neurociencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Manuel Alvarez
- Instituto de Fisiología Celular─Neurociencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Gabriel Diaz-deLeon
- Instituto de Fisiología Celular─Neurociencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Ranulfo Romo
- Instituto de Fisiología Celular─Neurociencias, Universidad Nacional Autónoma de México, Mexico City, Mexico.
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City, Mexico.
- El Colegio Nacional, Mexico City, Mexico.
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17
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Invariant timescale hierarchy across the cortical somatosensory network. Proc Natl Acad Sci U S A 2021; 118:2021843118. [PMID: 33431695 PMCID: PMC7826380 DOI: 10.1073/pnas.2021843118] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
The ability of cortical networks to integrate information from different sources is essential for cognitive processes. On one hand, sensory areas exhibit fast dynamics often phase-locked to stimulation; on the other hand, frontal lobe areas with slow response latencies to stimuli must integrate and maintain information for longer periods. Thus, cortical areas may require different timescales depending on their functional role. Studying the cortical somatosensory network while monkeys discriminated between two vibrotactile stimulus patterns, we found that a hierarchical order could be established across cortical areas based on their intrinsic timescales. Further, even though subareas (areas 3b, 1, and 2) of the primary somatosensory (S1) cortex exhibit analogous firing rate responses, a clear differentiation was observed in their timescales. Importantly, we observed that this inherent timescale hierarchy was invariant between task contexts (demanding vs. nondemanding). Even if task context severely affected neural coding in cortical areas downstream to S1, their timescales remained unaffected. Moreover, we found that these time constants were invariant across neurons with different latencies or coding. Although neurons had completely different dynamics, they all exhibited comparable timescales within each cortical area. Our results suggest that this measure is demonstrative of an inherent characteristic of each cortical area, is not a dynamical feature of individual neurons, and does not depend on task demands.
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18
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Velenosi LA, Wu YH, Schmidt TT, Blankenburg F. Intraparietal sulcus maintains working memory representations of somatosensory categories in an adaptive, context-dependent manner. Neuroimage 2020; 221:117146. [DOI: 10.1016/j.neuroimage.2020.117146] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 07/03/2020] [Accepted: 07/04/2020] [Indexed: 02/01/2023] Open
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19
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Prefrontal cortex exhibits multidimensional dynamic encoding during decision-making. Nat Neurosci 2020; 23:1410-1420. [PMID: 33020653 PMCID: PMC7610668 DOI: 10.1038/s41593-020-0696-5] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Accepted: 07/21/2020] [Indexed: 01/27/2023]
Abstract
Recent work has suggested that prefrontal cortex (PFC) plays a key role in context-dependent perceptual decision-making. Here we address that role using a new method for identifying task-relevant dimensions of neural population activity. Specifically, we show that PFC has a multi-dimensional code for context, decisions, and both relevant and irrelevant sensory information. Moreover, these representations evolve in time, with an early linear accumulation phase followed by a phase with rotational dynamics. We identify the dimensions of neural activity associated with these phases, and show that they do not arise from distinct populations, but of a single population with broad tuning characteristics. Finally, we use model-based decoding to show that the transition from linear to rotational dynamics coincides with a plateau in decoding accuracy, revealing that rotational dynamics in PFC preserve sensory choice information for the duration of the stimulus integration period.
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20
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Cueva CJ, Saez A, Marcos E, Genovesio A, Jazayeri M, Romo R, Salzman CD, Shadlen MN, Fusi S. Low-dimensional dynamics for working memory and time encoding. Proc Natl Acad Sci U S A 2020; 117:23021-23032. [PMID: 32859756 PMCID: PMC7502752 DOI: 10.1073/pnas.1915984117] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Our decisions often depend on multiple sensory experiences separated by time delays. The brain can remember these experiences and, simultaneously, estimate the timing between events. To understand the mechanisms underlying working memory and time encoding, we analyze neural activity recorded during delays in four experiments on nonhuman primates. To disambiguate potential mechanisms, we propose two analyses, namely, decoding the passage of time from neural data and computing the cumulative dimensionality of the neural trajectory over time. Time can be decoded with high precision in tasks where timing information is relevant and with lower precision when irrelevant for performing the task. Neural trajectories are always observed to be low-dimensional. In addition, our results further constrain the mechanisms underlying time encoding as we find that the linear "ramping" component of each neuron's firing rate strongly contributes to the slow timescale variations that make decoding time possible. These constraints rule out working memory models that rely on constant, sustained activity and neural networks with high-dimensional trajectories, like reservoir networks. Instead, recurrent networks trained with backpropagation capture the time-encoding properties and the dimensionality observed in the data.
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Affiliation(s)
- Christopher J Cueva
- Department of Neuroscience, Columbia University, New York, NY 10027;
- Center for Theoretical Neuroscience, Columbia University, New York, NY 10027
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027
| | - Alex Saez
- Department of Neuroscience, Columbia University, New York, NY 10027
| | - Encarni Marcos
- Instituto de Neurociencias de Alicante, Consejo Superior de Investigaciones Científicas-Universidad Miguel Hernández de Elche, San Juan de Alicante 03550, Spain
- Department of Physiology and Pharmacology, Sapienza University of Rome, Rome 00185, Italy
| | - Aldo Genovesio
- Department of Physiology and Pharmacology, Sapienza University of Rome, Rome 00185, Italy
| | - Mehrdad Jazayeri
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Ranulfo Romo
- Instituto de Fisiolgía Celular-Neurociencias, Universidad Nacional Autónoma de México, 04510 Mexico City, Mexico;
- El Colegio Nacional, 06020 Mexico City, Mexico
| | - C Daniel Salzman
- Department of Neuroscience, Columbia University, New York, NY 10027
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027
- Kavli Institute for Brain Science, Columbia University, New York, NY 10027
- Department of Psychiatry, Columbia University, New York, NY 10032
- New York State Psychiatric Institute, New York, NY 10032
| | - Michael N Shadlen
- Department of Neuroscience, Columbia University, New York, NY 10027
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027
- Kavli Institute for Brain Science, Columbia University, New York, NY 10027
- Department of Psychiatry, Columbia University, New York, NY 10032
- New York State Psychiatric Institute, New York, NY 10032
| | - Stefano Fusi
- Department of Neuroscience, Columbia University, New York, NY 10027;
- Center for Theoretical Neuroscience, Columbia University, New York, NY 10027
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027
- Kavli Institute for Brain Science, Columbia University, New York, NY 10027
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21
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Romo R, Rossi-Pool R. Turning Touch into Perception. Neuron 2020; 105:16-33. [PMID: 31917952 DOI: 10.1016/j.neuron.2019.11.033] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 11/16/2019] [Accepted: 11/27/2019] [Indexed: 12/27/2022]
Abstract
Many brain areas modulate their activity during vibrotactile tasks. The activity from these areas may code the stimulus parameters, stimulus perception, or perceptual reports. Here, we discuss findings obtained in behaving monkeys aimed to understand these processes. In brief, neurons from the somatosensory thalamus and primary somatosensory cortex (S1) only code the stimulus parameters during the stimulation periods. In contrast, areas downstream of S1 code the stimulus parameters during not only the task components but also perception. Surprisingly, the midbrain dopamine system is an actor not considered before in perception. We discuss the evidence that it codes the subjective magnitude of a sensory percept. The findings reviewed here may help us to understand where and how sensation transforms into perception in the brain.
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Affiliation(s)
- Ranulfo Romo
- Instituto de Fisiología Celular - Neurociencias, Universidad Nacional Autónoma de México, 04510 Mexico City, Mexico; El Colegio Nacional, 06020 Mexico City, Mexico.
| | - Román Rossi-Pool
- Instituto de Fisiología Celular - Neurociencias, Universidad Nacional Autónoma de México, 04510 Mexico City, Mexico.
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22
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Hou H, Zheng Q, Zhao Y, Pouget A, Gu Y. Neural Correlates of Optimal Multisensory Decision Making under Time-Varying Reliabilities with an Invariant Linear Probabilistic Population Code. Neuron 2019; 104:1010-1021.e10. [DOI: 10.1016/j.neuron.2019.08.038] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 07/21/2019] [Accepted: 08/22/2019] [Indexed: 12/27/2022]
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23
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Keemink SW, Machens CK. Decoding and encoding (de)mixed population responses. Curr Opin Neurobiol 2019; 58:112-121. [PMID: 31563083 DOI: 10.1016/j.conb.2019.09.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 08/19/2019] [Accepted: 09/08/2019] [Indexed: 10/25/2022]
Abstract
A central tenet of neuroscience is that the brain works through large populations of interacting neurons. With recent advances in recording techniques, the inner working of these populations has come into full view. Analyzing the resulting large-scale data sets is challenging because of the often complex and 'mixed' dependency of neural activities on experimental parameters, such as stimuli, decisions, or motor responses. Here we review recent insights gained from analyzing these data with dimensionality reduction methods that 'demix' these dependencies. We demonstrate that the mappings from (carefully chosen) experimental parameters to population activities appear to be typical and stable across tasks, brain areas, and animals, and are often identifiable by linear methods. By considering when and why dimensionality reduction and demixing work well, we argue for a view of population coding in which populations represent (demixed) latent signals, corresponding to stimuli, decisions, motor responses, and so on. These latent signals are encoded into neural population activity via non-linear mappings and decoded via linear readouts. We explain how such a scheme can facilitate the propagation of information across cortical areas, and we review neural network architectures that can reproduce the encoding and decoding of latent signals in population activities. These architectures promise a link from the biophysics of single neurons to the activities of neural populations.
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24
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Abstract
Neuronal populations respond within a small number of relevant dimensions. New research by Trautmann et al. (2019) shows that spike sorting is not necessary to extract the important features of this low-dimensional population signal. Combined responses of multiple neurons (multiunit activity) only generate small changes in the extracted signals.
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Affiliation(s)
- Román Rossi-Pool
- Instituto de Fisiología Celular-Neurociencias, Universidad Nacional Autónoma de México, 04510 Mexico City, Mexico.
| | - Ranulfo Romo
- Instituto de Fisiología Celular-Neurociencias, Universidad Nacional Autónoma de México, 04510 Mexico City, Mexico; El Colegio Nacional, 06020 Mexico City, Mexico.
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25
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Abstract
In this issue of Neuron, Rossi-Pool et al. (2017) show that the complex and heterogeneous response profiles of individual neurons in the dorsal premotor cortex during comparison of tactile temporal patterns can be understood in terms of two robust activity patterns that emerge across the population.
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Affiliation(s)
- Mehrdad Jazayeri
- McGovern Institute for Brain Research, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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26
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Trautmann EM, Stavisky SD, Lahiri S, Ames KC, Kaufman MT, O'Shea DJ, Vyas S, Sun X, Ryu SI, Ganguli S, Shenoy KV. Accurate Estimation of Neural Population Dynamics without Spike Sorting. Neuron 2019; 103:292-308.e4. [PMID: 31171448 DOI: 10.1016/j.neuron.2019.05.003] [Citation(s) in RCA: 121] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 02/06/2019] [Accepted: 04/30/2019] [Indexed: 11/25/2022]
Abstract
A central goal of systems neuroscience is to relate an organism's neural activity to behavior. Neural population analyses often reduce the data dimensionality to focus on relevant activity patterns. A major hurdle to data analysis is spike sorting, and this problem is growing as the number of recorded neurons increases. Here, we investigate whether spike sorting is necessary to estimate neural population dynamics. The theory of random projections suggests that we can accurately estimate the geometry of low-dimensional manifolds from a small number of linear projections of the data. We recorded data using Neuropixels probes in motor cortex of nonhuman primates and reanalyzed data from three previous studies and found that neural dynamics and scientific conclusions are quite similar using multiunit threshold crossings rather than sorted neurons. This finding unlocks existing data for new analyses and informs the design and use of new electrode arrays for laboratory and clinical use.
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Affiliation(s)
- Eric M Trautmann
- Neurosciences Program, Stanford University, Stanford, CA, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
| | - Sergey D Stavisky
- Neurosciences Program, Stanford University, Stanford, CA, USA; Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Subhaneil Lahiri
- Department of Applied Physics, Stanford University, Stanford, CA, USA
| | - Katherine C Ames
- Neurosciences Program, Stanford University, Stanford, CA, USA; Department of Neuroscience, Columbia University, New York, NY, USA
| | - Matthew T Kaufman
- Neurosciences Program, Stanford University, Stanford, CA, USA; Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA
| | - Daniel J O'Shea
- Neurosciences Program, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Saurabh Vyas
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Xulu Sun
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Stephen I Ryu
- Palo Alto Medical Foundation, Palo Alto, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Surya Ganguli
- Department of Applied Physics, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA; Department of Neurobiology, Stanford University, Stanford, CA, USA; Stanford Neurosciences Institute, Stanford, CA, USA; Bio-X Program, Stanford University, Stanford, CA, USA
| | - Krishna V Shenoy
- Neurosciences Program, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA; Department of Neurobiology, Stanford University, Stanford, CA, USA; Stanford Neurosciences Institute, Stanford, CA, USA; Bio-X Program, Stanford University, Stanford, CA, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
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27
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Wang M, Montanède C, Chandrasekaran C, Peixoto D, Shenoy KV, Kalaska JF. Macaque dorsal premotor cortex exhibits decision-related activity only when specific stimulus-response associations are known. Nat Commun 2019; 10:1793. [PMID: 30996222 PMCID: PMC6470163 DOI: 10.1038/s41467-019-09460-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 03/12/2019] [Indexed: 01/16/2023] Open
Abstract
How deliberation on sensory cues and action selection interact in decision-related brain areas is still not well understood. Here, monkeys reached to one of two targets, whose colors alternated randomly between trials, by discriminating the dominant color of a checkerboard cue composed of different numbers of squares of the two target colors in different trials. In a Targets First task the colored targets appeared first, followed by the checkerboard; in a Checkerboard First task, this order was reversed. After both cues appeared in both tasks, responses of dorsal premotor cortex (PMd) units covaried with action choices, strength of evidence for action choices, and RTs- hallmarks of decision-related activity. However, very few units were modulated by checkerboard color composition or the color of the chosen target, even during the checkerboard deliberation epoch of the Checkerboard First task. These findings implicate PMd in the action-selection but not the perceptual components of the decision-making process in these tasks.
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Affiliation(s)
- Megan Wang
- Neurosciences Graduate Program, Stanford University, Stanford, CA, 94305, USA
| | - Christéva Montanède
- Département de Neurosciences, Pavillon Paul-G.-Desmarais, Faculté de Médecine, Université de Montréal, succursale Centre-ville, Montréal, QC, H3C 3J7, Canada
| | - Chandramouli Chandrasekaran
- Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, 94305, USA
- Department of Anatomy and Neurobiology, Boston University, Boston, MA, 02118, USA
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, 02215, USA
| | - Diogo Peixoto
- Department of Neurobiology, Stanford University, Stanford, CA, 94305, USA
- Champalimaud Neuroscience Programme, 1400-038, Lisbon, Portugal
| | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, 94305, USA
- Department of Neurobiology, Stanford University, Stanford, CA, 94305, USA
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
- Bio-X Program, Stanford University, Stanford, CA, 94305, USA
- Stanford Neurosciences Institute, Stanford University, Stanford, CA, 94305, USA
| | - John F Kalaska
- Département de Neurosciences, Pavillon Paul-G.-Desmarais, Faculté de Médecine, Université de Montréal, succursale Centre-ville, Montréal, QC, H3C 3J7, Canada.
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28
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Gámez J, Mendoza G, Prado L, Betancourt A, Merchant H. The amplitude in periodic neural state trajectories underlies the tempo of rhythmic tapping. PLoS Biol 2019; 17:e3000054. [PMID: 30958818 PMCID: PMC6472824 DOI: 10.1371/journal.pbio.3000054] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 04/18/2019] [Accepted: 03/19/2019] [Indexed: 01/03/2023] Open
Abstract
Our motor commands can be exquisitely timed according to the demands of the environment, and the ability to generate rhythms of different tempos is a hallmark of musical cognition. Yet, the neuronal underpinnings behind rhythmic tapping remain elusive. Here, we found that the activity of hundreds of primate medial premotor cortices (MPCs; pre-supplementary motor area [preSMA] and supplementary motor area [SMA]) neurons show a strong periodic pattern that becomes evident when their responses are projected into a state space using dimensionality reduction analysis. We show that different tapping tempos are encoded by circular trajectories that travelled at a constant speed but with different radii, and that this neuronal code is highly resilient to the number of participating neurons. Crucially, the changes in the amplitude of the oscillatory dynamics in neuronal state space are a signature of duration encoding during rhythmic timing, regardless of whether it is guided by an external metronome or is internally controlled and is not the result of repetitive motor commands. This dynamic state signal predicted the duration of the rhythmically produced intervals on a trial-by-trial basis. Furthermore, the increase in variability of the neural trajectories accounted for the scalar property, a hallmark feature of temporal processing across tasks and species. Finally, we found that the interval-dependent increments in the radius of periodic neural trajectories are the result of a larger number of neurons engaged in the production of longer intervals. Our results support the notion that rhythmic timing during tapping behaviors is encoded in the radial curvature of periodic MPC neural population trajectories.
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Affiliation(s)
- Jorge Gámez
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Germán Mendoza
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Luis Prado
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Abraham Betancourt
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Hugo Merchant
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
- * E-mail:
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29
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Temporal signals underlying a cognitive process in the dorsal premotor cortex. Proc Natl Acad Sci U S A 2019; 116:7523-7532. [PMID: 30918128 DOI: 10.1073/pnas.1820474116] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
During discrimination between two sequential vibrotactile stimulus patterns, the primate dorsal premotor cortex (DPC) neurons exhibit a complex repertoire of coding dynamics associated with the working memory, comparison, and decision components of this task. In addition, these neurons and neurons with no coding responses show complex strong fluctuations in their firing rate associated with the temporal sequence of task events. Here, to make sense of this temporal complexity, we extracted the temporal signals that were latent in the population. We found a strong link between the individual and population response, suggesting a common neural substrate. Notably, in contrast to coding dynamics, these time-dependent responses were unaffected during error trials. However, in a nondemanding task in which monkeys did not require discrimination for reward, these time-dependent signals were largely reduced and changed. These results suggest that temporal dynamics in DPC reflect the underlying cognitive processes of this task.
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30
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Rossi-Pool R, Vergara J, Romo R. The Memory Map of Visual Space. Trends Neurosci 2018; 41:117-120. [DOI: 10.1016/j.tins.2017.12.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Accepted: 12/11/2017] [Indexed: 10/17/2022]
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