1
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Vishwanathan A, Sood A, Wu J, Ramirez AD, Yang R, Kemnitz N, Ih D, Turner N, Lee K, Tartavull I, Silversmith WM, Jordan CS, David C, Bland D, Sterling A, Seung HS, Goldman MS, Aksay ERF. Predicting modular functions and neural coding of behavior from a synaptic wiring diagram. Nat Neurosci 2024; 27:2443-2454. [PMID: 39578573 PMCID: PMC11614741 DOI: 10.1038/s41593-024-01784-3] [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: 12/12/2022] [Accepted: 09/11/2024] [Indexed: 11/24/2024]
Abstract
A long-standing goal in neuroscience is to understand how a circuit's form influences its function. Here, we reconstruct and analyze a synaptic wiring diagram of the larval zebrafish brainstem to predict key functional properties and validate them through comparison with physiological data. We identify modules of strongly connected neurons that turn out to be specialized for different behavioral functions, the control of eye and body movements. The eye movement module is further organized into two three-block cycles that support the positive feedback long hypothesized to underlie low-dimensional attractor dynamics in oculomotor control. We construct a neural network model based directly on the reconstructed wiring diagram that makes predictions for the cellular-resolution coding of eye position and neural dynamics. These predictions are verified statistically with calcium imaging-based neural activity recordings. This work demonstrates how connectome-based brain modeling can reveal previously unknown anatomical structure in a neural circuit and provide insights linking network form to function.
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Affiliation(s)
| | - Alex Sood
- Center for Neuroscience, University of California, Davis, Davis, CA, USA
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Center for Computational Neuroscience, Flatiron Institute, New York, NY, USA
| | - Alexandro D Ramirez
- Institute for Computational Biomedicine and the Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY, USA
- Department of Physiology and Pharmacology, SUNY Downstate Health Sciences University, New York, NY, USA
| | - Runzhe Yang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Dodam Ih
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Nicholas Turner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ignacio Tartavull
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Chris S Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Celia David
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Doug Bland
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Amy Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Mark S Goldman
- Center for Neuroscience, University of California, Davis, Davis, CA, USA.
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, CA, USA.
- Department of Ophthalmology and Vision Science, University of California, Davis, Davis, CA, USA.
| | - Emre R F Aksay
- Institute for Computational Biomedicine and the Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY, USA.
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2
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Brown LS, Cho JR, Bolkan SS, Nieh EH, Schottdorf M, Tank DW, Brody CD, Witten IB, Goldman MS. Neural circuit models for evidence accumulation through choice-selective sequences. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.01.555612. [PMID: 38234715 PMCID: PMC10793437 DOI: 10.1101/2023.09.01.555612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Decision making is traditionally thought to be mediated by populations of neurons whose firing rates persistently accumulate evidence across time. However, recent decision-making experiments in rodents have observed neurons across the brain that fire sequentially as a function of spatial position or time, rather than persistently, with the subset of neurons in the sequence depending on the animal's choice. We develop two new candidate circuit models, in which evidence is encoded either in the relative firing rates of two competing chains of neurons or in the network location of a stereotyped pattern ("bump") of neural activity. Encoded evidence is then faithfully transferred between neuronal populations representing different positions or times. Neural recordings from four different brain regions during a decision-making task showed that, during the evidence accumulation period, different brain regions displayed tuning curves consistent with different candidate models for evidence accumulation. This work provides mechanistic models and potential neural substrates for how graded-value information may be precisely accumulated within and transferred between neural populations, a set of computations fundamental to many cognitive operations.
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3
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Feierstein CE, de Goeij MHM, Ostrovsky AD, Laborde A, Portugues R, Orger MB, Machens CK. Dimensionality reduction reveals separate translation and rotation populations in the zebrafish hindbrain. Curr Biol 2023; 33:3911-3925.e6. [PMID: 37689065 PMCID: PMC10524920 DOI: 10.1016/j.cub.2023.08.037] [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: 03/15/2023] [Revised: 07/07/2023] [Accepted: 08/14/2023] [Indexed: 09/11/2023]
Abstract
In many brain areas, neuronal activity is associated with a variety of behavioral and environmental variables. In particular, neuronal responses in the zebrafish hindbrain relate to oculomotor and swimming variables as well as sensory information. However, the precise functional organization of the neurons has been difficult to unravel because neuronal responses are heterogeneous. Here, we used dimensionality reduction methods on neuronal population data to reveal the role of the hindbrain in visually driven oculomotor behavior and swimming. We imaged neuronal activity in zebrafish expressing GCaMP6s in the nucleus of almost all neurons while monitoring the behavioral response to gratings that rotated with different speeds. We then used reduced-rank regression, a method that condenses the sensory and motor variables into a smaller number of "features," to predict the fluorescence traces of all ROIs (regions of interest). Despite the potential complexity of the visuo-motor transformation, our analysis revealed that a large fraction of the population activity can be explained by only two features. Based on the contribution of these features to each ROI's activity, ROIs formed three clusters. One cluster was related to vergent movements and swimming, whereas the other two clusters related to leftward and rightward rotation. Voxels corresponding to these clusters were segregated anatomically, with leftward and rightward rotation clusters located selectively to the left and right hemispheres, respectively. Just as described in many cortical areas, our analysis revealed that single-neuron complexity co-exists with a simpler population-level description, thereby providing insights into the organization of visuo-motor transformations in the hindbrain.
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Affiliation(s)
- Claudia E Feierstein
- Champalimaud Neuroscience Programme, Champalimaud Foundation, Lisbon 1400-038, Portugal.
| | - Michelle H M de Goeij
- Champalimaud Neuroscience Programme, Champalimaud Foundation, Lisbon 1400-038, Portugal; Faculty of Medicine, Utrecht University, Utrecht 3584 CG, the Netherlands; Pfizer BV, Capelle aan den Ijssel 2909 LD, the Netherlands
| | - Aaron D Ostrovsky
- Champalimaud Neuroscience Programme, Champalimaud Foundation, Lisbon 1400-038, Portugal
| | - Alexandre Laborde
- Champalimaud Neuroscience Programme, Champalimaud Foundation, Lisbon 1400-038, Portugal
| | - Ruben Portugues
- Institute of Neuroscience, Technical University, Munich 80802, Germany; Munich Cluster of Systems Neurology (SyNergy), Munich 81377, Germany
| | - Michael B Orger
- Champalimaud Neuroscience Programme, Champalimaud Foundation, Lisbon 1400-038, Portugal.
| | - Christian K Machens
- Champalimaud Neuroscience Programme, Champalimaud Foundation, Lisbon 1400-038, Portugal.
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4
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Recording Channelrhodopsin-Evoked Field Potentials and Startle Responses from Larval Zebrafish. Methods Mol Biol 2021; 2191:201-220. [PMID: 32865747 DOI: 10.1007/978-1-0716-0830-2_13] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Zebrafish are an excellent model organism to study many aspects of vertebrate sensory encoding and behavior. Their escape responses begin with a C-shaped body bend followed by several swimming bouts away from the potentially threatening stimulus. This highly stereotyped motor behavior provides a model for studying startle reflexes and the neural circuitry underlying multisensory encoding and locomotion. Channelrhodopsin (ChR2) can be expressed in the lateral line and ear hair cells of zebrafish and can be excited in vivo to elicit these rapid forms of escape. Here we review our methods for studying transgenic ChR2-expressing zebrafish larvae, including screening for positive expression of ChR2 and recording field potentials and high-speed videos of optically evoked escape responses. We also highlight important features of the acquired data and provide a brief review of other zebrafish research that utilizes or has the potential to benefit from ChR2 and optogenetics.
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5
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Atiya NAA, Zgonnikov A, O’Hora D, Schoemann M, Scherbaum S, Wong-Lin K. Changes-of-mind in the absence of new post-decision evidence. PLoS Comput Biol 2020; 16:e1007149. [PMID: 32012147 PMCID: PMC7018100 DOI: 10.1371/journal.pcbi.1007149] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 02/13/2020] [Accepted: 11/08/2019] [Indexed: 11/19/2022] Open
Abstract
Decisions are occasionally accompanied by changes-of-mind. While considered a hallmark of cognitive flexibility, the mechanisms underlying changes-of-mind remain elusive. Previous studies on perceptual decision making have focused on changes-of-mind that are primarily driven by the accumulation of additional noisy sensory evidence after the initial decision. In a motion discrimination task, we demonstrate that changes-of-mind can occur even in the absence of additional evidence after the initial decision. Unlike previous studies of changes-of-mind, the majority of changes-of-mind in our experiment occurred in trials with prolonged initial response times. This suggests a distinct mechanism underlying such changes. Using a neural circuit model of decision uncertainty and change-of-mind behaviour, we demonstrate that this phenomenon is associated with top-down signals mediated by an uncertainty-monitoring neural population. Such a mechanism is consistent with recent neurophysiological evidence showing a link between changes-of-mind and elevated top-down neural activity. Our model explains the long response times associated with changes-of-mind through high decision uncertainty levels in such trials, and accounts for the observed motor response trajectories. Overall, our work provides a computational framework that explains changes-of-mind in the absence of new post-decision evidence.
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Affiliation(s)
- Nadim A. A. Atiya
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Magee Campus, Derry∼Londonderry, United Kingdom
| | - Arkady Zgonnikov
- School of Psychology, National University of Ireland Galway, Galway, Ireland
| | - Denis O’Hora
- School of Psychology, National University of Ireland Galway, Galway, Ireland
- * E-mail: (DO); (KFW-L)
| | - Martin Schoemann
- Department of Psychology, Technische Universität Dresden, Dresden, Germany
- Department of Management/MAPP, Aarhus University, Aarhus, Denmark
| | - Stefan Scherbaum
- Department of Psychology, Technische Universität Dresden, Dresden, Germany
| | - KongFatt Wong-Lin
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Magee Campus, Derry∼Londonderry, United Kingdom
- * E-mail: (DO); (KFW-L)
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6
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Brysch C, Leyden C, Arrenberg AB. Functional architecture underlying binocular coordination of eye position and velocity in the larval zebrafish hindbrain. BMC Biol 2019; 17:110. [PMID: 31884959 PMCID: PMC6936144 DOI: 10.1186/s12915-019-0720-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 11/06/2019] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND The oculomotor integrator (OI) in the vertebrate hindbrain transforms eye velocity input into persistent position coding output, which plays a crucial role in retinal image stability. For a mechanistic understanding of the integrator function and eye position control, knowledge about the tuning of the OI and other oculomotor nuclei is needed. Zebrafish are increasingly used to study integrator function and sensorimotor circuits, yet the precise neuronal tuning to motor variables remains uncharacterized. RESULTS Here, we recorded cellular calcium signals while evoking monocular and binocular optokinetic eye movements at different slow-phase eye velocities. Our analysis reveals the anatomical distributions of motoneurons and internuclear neurons in the nucleus abducens as well as those of oculomotor neurons in caudally adjacent hindbrain volumes. Each neuron is tuned to eye position and/or velocity to variable extents and is only activated after surpassing particular eye position and velocity thresholds. While the abducens (rhombomeres 5/6) mainly codes for eye position, in rhombomeres 7/8, a velocity-to-position coding gradient exists along the rostro-caudal axis, which likely corresponds to the oculomotor structures storing velocity and position, and is in agreement with a feedforward mechanism of persistent activity generation. Position encoding neurons are recruited at eye position thresholds distributed across the behaviourally relevant dynamic range, while velocity-encoding neurons have more centred firing thresholds for velocity. In the abducens, neurons coding exclusively for one eye intermingle with neurons coding for both eyes. Many of these binocular neurons are preferentially active during conjugate eye movements and less active during monocular eye movements. This differential recruitment during monocular versus conjugate tasks represents a functional diversification in the final common motor pathway. CONCLUSIONS We localized and functionally characterized the repertoire of oculomotor neurons in the zebrafish hindbrain. Our findings provide evidence for a mixed but task-specific binocular code and suggest that generation of persistent activity is organized along the rostro-caudal axis in the hindbrain.
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Affiliation(s)
- Christian Brysch
- Werner Reichardt Centre for Integrative Neuroscience and Institute for Neurobiology, University of Tübingen, 72076, Tübingen, Germany
- Graduate Training Centre of Neuroscience, University of Tübingen, 72074, Tübingen, Germany
| | - Claire Leyden
- Werner Reichardt Centre for Integrative Neuroscience and Institute for Neurobiology, University of Tübingen, 72076, Tübingen, Germany
- Graduate Training Centre of Neuroscience, University of Tübingen, 72074, Tübingen, Germany
| | - Aristides B Arrenberg
- Werner Reichardt Centre for Integrative Neuroscience and Institute for Neurobiology, University of Tübingen, 72076, Tübingen, Germany.
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7
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A neural circuit model of decision uncertainty and change-of-mind. Nat Commun 2019; 10:2287. [PMID: 31123260 PMCID: PMC6533317 DOI: 10.1038/s41467-019-10316-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Accepted: 04/30/2019] [Indexed: 01/15/2023] Open
Abstract
Decision-making is often accompanied by a degree of confidence on whether a choice is correct. Decision uncertainty, or lack in confidence, may lead to change-of-mind. Studies have identified the behavioural characteristics associated with decision confidence or change-of-mind, and their neural correlates. Although several theoretical accounts have been proposed, there is no neural model that can compute decision uncertainty and explain its effects on change-of-mind. We propose a neuronal circuit model that computes decision uncertainty while accounting for a variety of behavioural and neural data of decision confidence and change-of-mind, including testable model predictions. Our theoretical analysis suggests that change-of-mind occurs due to the presence of a transient uncertainty-induced choice-neutral stable steady state and noisy fluctuation within the neuronal network. Our distributed network model indicates that the neural basis of change-of-mind is more distinctively identified in motor-based neurons. Overall, our model provides a framework that unifies decision confidence and change-of-mind. We make decisions with varying degrees of confidence and, if our confidence in a decision falls, we may change our mind. Here, the authors present a neuronal circuit model to account for how change of mind occurs under particular low-confidence conditions.
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8
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Dehmelt FA, von Daranyi A, Leyden C, Arrenberg AB. Evoking and tracking zebrafish eye movement in multiple larvae with ZebEyeTrack. Nat Protoc 2019; 13:1539-1568. [PMID: 29988103 DOI: 10.1038/s41596-018-0002-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Reliable measurement of spontaneous and evoked eye movement is critical for behavioral vision research. Zebrafish are increasingly used as a model organism for visual neural circuits, but ready-to-use eye-tracking solutions are scarce. Here, we present a protocol for automated real-time measurement of angular horizontal eye position in up to six immobilized larval fish using a custom-built LabVIEW-based software, ZebEyeTrack. We provide its customizable source code, as well as a streamlined and compiled version, ZebEyeTrack Light. The full version of ZebEyeTrack controls all required hardware and synchronizes six essential aspects of the experiment: (i) stimulus design; (ii) visual stimulation with moving bars; (ii) eye detection and tracking, as well as general motion detection; (iv) real-time analysis; (v) eye-position-dependent closed-loop event control; and (vi) recording of external event times. This includes optional integration with external hardware such as lasers and scanning microscopes. Once installation is complete, experiments, including stimulus design, can be completed in <10 min, and recordings can last anywhere between seconds and many hours. Results include digitized angular eye positions and hardware status, which can be used to compute tuning curves, optokinetic gain, and other custom data analysis. After the experiment, or based on existing videos, optokinetic response (OKR) performance can be analyzed semi-automatically via the graphical user interface, and results can be exported. ZebEyeTrack has been used successfully for psychophysics experiments, for optogenetic stimulation, and in combination with calcium imaging.
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Affiliation(s)
- Florian A Dehmelt
- Werner Reichardt Centre for Integrative Neuroscience and Institute of Neurobiology, University of Tübingen, Tübingen, Germany
| | - Adam von Daranyi
- Werner Reichardt Centre for Integrative Neuroscience, Central Office System Administration, University of Tübingen, Tübingen, Germany
| | - Claire Leyden
- Werner Reichardt Centre for Integrative Neuroscience and Institute of Neurobiology, University of Tübingen, Tübingen, Germany.,Graduate Training Centre of Neuroscience, University of Tübingen, Tübingen, Germany
| | - Aristides B Arrenberg
- Werner Reichardt Centre for Integrative Neuroscience and Institute of Neurobiology, University of Tübingen, Tübingen, Germany.
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9
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Sensorimotor computation underlying phototaxis in zebrafish. Nat Commun 2017; 8:651. [PMID: 28935857 PMCID: PMC5608914 DOI: 10.1038/s41467-017-00310-3] [Citation(s) in RCA: 64] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Accepted: 06/20/2017] [Indexed: 11/09/2022] Open
Abstract
Animals continuously gather sensory cues to move towards favourable environments. Efficient goal-directed navigation requires sensory perception and motor commands to be intertwined in a feedback loop, yet the neural substrate underlying this sensorimotor task in the vertebrate brain remains elusive. Here, we combine virtual-reality behavioural assays, volumetric calcium imaging, optogenetic stimulation and circuit modelling to reveal the neural mechanisms through which a zebrafish performs phototaxis, i.e. actively orients towards a light source. Key to this process is a self-oscillating hindbrain population (HBO) that acts as a pacemaker for ocular saccades and controls the orientation of successive swim-bouts. It further integrates visual stimuli in a state-dependent manner, i.e. its response to visual inputs varies with the motor context, a mechanism that manifests itself in the phase-locked entrainment of the HBO by periodic stimuli. A rate model is developed that reproduces our observations and demonstrates how this sensorimotor processing eventually biases the animal trajectory towards bright regions. Active locomotion requires closed-loop sensorimotor co ordination between perception and action. Here the authors show using behavioural, imaging and modelling approaches that gaze orientation during phototaxis behaviour in larval zebrafish is related to oscillatory dynamics of a neuronal population in the hindbrain.
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10
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Vishwanathan A, Daie K, Ramirez AD, Lichtman JW, Aksay ERF, Seung HS. Electron Microscopic Reconstruction of Functionally Identified Cells in a Neural Integrator. Curr Biol 2017; 27:2137-2147.e3. [PMID: 28712570 PMCID: PMC5569574 DOI: 10.1016/j.cub.2017.06.028] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2016] [Revised: 03/02/2017] [Accepted: 06/09/2017] [Indexed: 11/27/2022]
Abstract
Neural integrators are involved in a variety of sensorimotor and cognitive behaviors. The oculomotor system contains a simple example, a hindbrain neural circuit that takes velocity signals as inputs and temporally integrates them to control eye position. Here we investigated the structural underpinnings of temporal integration in the larval zebrafish by first identifying integrator neurons using two-photon calcium imaging and then reconstructing the same neurons through serial electron microscopic analysis. Integrator neurons were identified as those neurons with activities highly correlated with eye position during spontaneous eye movements. Three morphological classes of neurons were observed: ipsilaterally projecting neurons located medially, contralaterally projecting neurons located more laterally, and a population at the extreme lateral edge of the hindbrain for which we were not able to identify axons. Based on their somatic locations, we inferred that neurons with only ipsilaterally projecting axons are glutamatergic, whereas neurons with only contralaterally projecting axons are largely GABAergic. Dendritic and synaptic organization of the ipsilaterally projecting neurons suggests a broad sampling from inputs on the ipsilateral side. We also observed the first conclusive evidence of synapses between integrator neurons, which have long been hypothesized by recurrent network models of integration via positive feedback.
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Affiliation(s)
| | - Kayvon Daie
- Institute for Computational Biomedicine and Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10021, USA
| | - Alexandro D Ramirez
- Institute for Computational Biomedicine and Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10021, USA
| | - Jeff W Lichtman
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Emre R F Aksay
- Institute for Computational Biomedicine and Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10021, USA
| | - H Sebastian Seung
- Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA.
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11
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Barrett DG, Denève S, Machens CK. Optimal compensation for neuron loss. eLife 2016; 5. [PMID: 27935480 PMCID: PMC5283835 DOI: 10.7554/elife.12454] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Accepted: 12/08/2016] [Indexed: 11/13/2022] Open
Abstract
The brain has an impressive ability to withstand neural damage. Diseases that kill neurons can go unnoticed for years, and incomplete brain lesions or silencing of neurons often fail to produce any behavioral effect. How does the brain compensate for such damage, and what are the limits of this compensation? We propose that neural circuits instantly compensate for neuron loss, thereby preserving their function as much as possible. We show that this compensation can explain changes in tuning curves induced by neuron silencing across a variety of systems, including the primary visual cortex. We find that compensatory mechanisms can be implemented through the dynamics of networks with a tight balance of excitation and inhibition, without requiring synaptic plasticity. The limits of this compensatory mechanism are reached when excitation and inhibition become unbalanced, thereby demarcating a recovery boundary, where signal representation fails and where diseases may become symptomatic.
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Affiliation(s)
- David Gt Barrett
- Laboratoire de Neurosciences Cognitives, École Normale Supérieure, Paris, France.,Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Sophie Denève
- Laboratoire de Neurosciences Cognitives, École Normale Supérieure, Paris, France
| | - Christian K Machens
- Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal
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12
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13
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Abstract
The perturbation of neural activity is a powerful experimental approach for understanding brain function. Light-gated ion channels and pumps (optogenetics) can be used to control neural activity with high temporal and spatial precision in animal models. This optogenetic approach requires suitable methods for delivering light to the brain. In zebrafish, fiber optic stimulation of agarose-embedded larvae has successfully been used in several studies to control neural activity and behavior. This approach is easy to implement and cost-efficient. Here, a protocol for fiber optic-based photostimulation of larval zebrafish is provided.
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Affiliation(s)
- Aristides B Arrenberg
- Institute of Neurobiology, Centre for Integrative Neuroscience, University of Tübingen, Otfried-Müller-Str. 25, Tübingen, 72076, Germany.
- Developmental Biology, Faculty of Biology, Institute Biology I, Hauptstrasse 1, Freiburg, Germany.
- BIOSS-Centre for Biological Signalling Studies, Albert-Ludwigs-Universität Freiburg, Schänzlestrasse 18, Freiburg, D-79104, Germany.
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14
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Abstract
The accumulation and storage of information over time, temporal integration, is key to numerous behaviors. Many oculomotor tasks depend on integration of eye-velocity signals to eye-position commands, a transformation achieved by a hindbrain cell group termed the velocity-to-position neural integrator (VPNI). Although the VPNI's coding properties have been well characterized, its mechanism of function remains poorly understood because few links exist between neuronal activity, structure, and genotypic identity. To fill this gap, we used calcium imaging and single-cell electroporation during oculomotor behaviors to map VPNI neural activity in zebrafish onto a hindbrain scaffold consisting of alternating excitatory and inhibitory parasagittal stripes. Three distinct classes of VPNI cells were identified. One glutamatergic class was medially located along a stripe associated with the alx transcription factor; these cells had ipsilateral projections terminating near abducens motoneurons and collateralized extensively within the ipsilateral VPNI in a manner consistent with integration through recurrent excitation. A second glutamatergic class was more laterally located along a stripe associated with transcription factor dbx1b; these glutamatergic cells had contralateral projections collateralizing near abducens motoneurons, consistent with a role in disconjugate eye movements. A third class, immunohistochemically suggested to be GABAergic, was located primarily in the dbx1b stripe and also had contralateral projections terminating near abducens motoneurons; these cells collateralized extensively in the dendritic field of contralateral VPNI neurons, consistent with a role in coordinating activity between functionally opposing populations. This mapping between VPNI activity, structure, and genotype may provide a blueprint for understanding the mechanisms governing temporal integration.
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15
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Wyatt C, Bartoszek EM, Yaksi E. Methods for studying the zebrafish brain: past, present and future. Eur J Neurosci 2015; 42:1746-63. [PMID: 25900095 DOI: 10.1111/ejn.12932] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2015] [Revised: 04/16/2015] [Accepted: 04/20/2015] [Indexed: 01/16/2023]
Abstract
The zebrafish (Danio rerio) is one of the most promising new model organisms. The increasing popularity of this amazing small vertebrate is evident from the exponentially growing numbers of research articles, funded projects and new discoveries associated with the use of zebrafish for studying development, brain function, human diseases and screening for new drugs. Thanks to the development of novel technologies, the range of zebrafish research is constantly expanding with new tools synergistically enhancing traditional techniques. In this review we will highlight the past and present techniques which have made, and continue to make, zebrafish an attractive model organism for various fields of biology, with a specific focus on neuroscience.
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Affiliation(s)
- Cameron Wyatt
- Neuro-Electronics Research Flanders, Imec Campus, Kapeldreef, Leuven, Belgium.,VIB, Leuven, Belgium
| | - Ewelina M Bartoszek
- Neuro-Electronics Research Flanders, Imec Campus, Kapeldreef, Leuven, Belgium.,VIB, Leuven, Belgium.,Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Norwegian University of Science and Technology, Trondheim, Norway
| | - Emre Yaksi
- Neuro-Electronics Research Flanders, Imec Campus, Kapeldreef, Leuven, Belgium.,VIB, Leuven, Belgium.,KU Leuven, Leuven, Belgium.,Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Norwegian University of Science and Technology, Trondheim, Norway
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