1
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Ebert S, Buffet T, Sermet BS, Marre O, Cessac B. Temporal pattern recognition in retinal ganglion cells is mediated by dynamical inhibitory synapses. Nat Commun 2024; 15:6118. [PMID: 39033142 PMCID: PMC11271269 DOI: 10.1038/s41467-024-50506-7] [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: 02/16/2023] [Accepted: 07/10/2024] [Indexed: 07/23/2024] Open
Abstract
A fundamental task for the brain is to generate predictions of future sensory inputs, and signal errors in these predictions. Many neurons have been shown to signal omitted stimuli during periodic stimulation, even in the retina. However, the mechanisms of this error signaling are unclear. Here we show that depressing inhibitory synapses shape the timing of the response to an omitted stimulus in the retina. While ganglion cells, the retinal output, responded to an omitted flash with a constant latency over many frequencies of the flash sequence, we found that this was not the case once inhibition was blocked. We built a simple circuit model and showed that depressing inhibitory synapses were a necessary component to reproduce our experimental findings. A new prediction of our model is that the accuracy of the constant latency requires a sufficient amount of flashes in the stimulus, which we could confirm experimentally. Depressing inhibitory synapses could thus be a key component to generate the predictive responses observed in the retina, and potentially in many brain areas.
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Affiliation(s)
- Simone Ebert
- INRIA Biovision Team, Université Côte d'Azur, Valbonne, France.
- Institute for Modeling in Neuroscience and Cognition (NeuroMod), Université Côte d'Azur, Nice, France.
- Sorbonne Université, INSERM, CNRS, Institut De La Vision, Paris, France.
| | - Thomas Buffet
- Sorbonne Université, INSERM, CNRS, Institut De La Vision, Paris, France
| | - B Semihcan Sermet
- Sorbonne Université, INSERM, CNRS, Institut De La Vision, Paris, France
- Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Olivier Marre
- Sorbonne Université, INSERM, CNRS, Institut De La Vision, Paris, France
| | - Bruno Cessac
- INRIA Biovision Team, Université Côte d'Azur, Valbonne, France
- Institute for Modeling in Neuroscience and Cognition (NeuroMod), Université Côte d'Azur, Nice, France
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2
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Salisbury JM, Palmer SE. A dynamic scale-mixture model of motion in natural scenes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.19.563101. [PMID: 37961311 PMCID: PMC10634686 DOI: 10.1101/2023.10.19.563101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Some of the most important tasks of visual and motor systems involve estimating the motion of objects and tracking them over time. Such systems evolved to meet the behavioral needs of the organism in its natural environment, and may therefore be adapted to the statistics of motion it is likely to encounter. By tracking the movement of individual points in movies of natural scenes, we begin to identify common properties of natural motion across scenes. As expected, objects in natural scenes move in a persistent fashion, with velocity correlations lasting hundreds of milliseconds. More subtly, but crucially, we find that the observed velocity distributions are heavy-tailed and can be modeled as a Gaussian scale-mixture. Extending this model to the time domain leads to a dynamic scale-mixture model, consisting of a Gaussian process multiplied by a positive scalar quantity with its own independent dynamics. Dynamic scaling of velocity arises naturally as a consequence of changes in object distance from the observer, and may approximate the effects of changes in other parameters governing the motion in a given scene. This modeling and estimation framework has implications for the neurobiology of sensory and motor systems, which need to cope with these fluctuations in scale in order to represent motion efficiently and drive fast and accurate tracking behavior.
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3
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He L, He Y, Ma L, Huang T. A theoretical model reveals specialized synaptic depressions and temporal frequency tuning in retinal parallel channels. Front Comput Neurosci 2022; 16:1034446. [DOI: 10.3389/fncom.2022.1034446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 10/27/2022] [Indexed: 11/18/2022] Open
Abstract
In the Outer Plexiform Layer of a retina, a cone pedicle provides synaptic inputs for multiple cone bipolar cell (CBC) subtypes so that each subtype formats a parallelized processing channel to filter visual features from the environment. Due to the diversity of short-term depressions among cone-CBC contacts, these channels have different temporal frequency tunings. Here, we propose a theoretical model based on the hierarchy Linear-Nonlinear-Synapse framework to link the synaptic depression and the neural activities of the cone-CBC circuit. The model successfully captures various frequency tunings of subtype-specialized channels and infers synaptic depression recovery time constants inside circuits. Furthermore, the model can predict frequency-tuning behaviors based on synaptic activities. With the prediction of region-specialized UV cone parallel channels, we suggest the acute zone in the zebrafish retina supports detecting light-off events at high temporal frequencies.
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4
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Freedland J, Rieke F. Systematic reduction of the dimensionality of natural scenes allows accurate predictions of retinal ganglion cell spike outputs. Proc Natl Acad Sci U S A 2022; 119:e2121744119. [PMID: 36343230 PMCID: PMC9674269 DOI: 10.1073/pnas.2121744119] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 09/23/2022] [Indexed: 11/09/2022] Open
Abstract
The mammalian retina engages a broad array of linear and nonlinear circuit mechanisms to convert natural scenes into retinal ganglion cell (RGC) spike outputs. Although many individual integration mechanisms are well understood, we know less about how multiple mechanisms interact to encode the complex spatial features present in natural inputs. Here, we identified key spatial features in natural scenes that shape encoding by primate parasol RGCs. Our approach identified simplifications in the spatial structure of natural scenes that minimally altered RGC spike responses. We observed that reducing natural movies into 16 linearly integrated regions described ∼80% of the structure of parasol RGC spike responses; this performance depended on the number of regions but not their precise spatial locations. We used simplified stimuli to design high-dimensional metamers that recapitulated responses to naturalistic movies. Finally, we modeled the retinal computations that convert flashed natural images into one-dimensional spike counts.
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Affiliation(s)
- Julian Freedland
- Molecular Engineering & Sciences Institute, University of Washington, Seattle, WA 98195
| | - Fred Rieke
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195
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5
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Conti D, Mora T. Nonequilibrium dynamics of adaptation in sensory systems. Phys Rev E 2022; 106:054404. [PMID: 36559478 DOI: 10.1103/physreve.106.054404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 10/11/2022] [Indexed: 06/17/2023]
Abstract
Adaptation is used by biological sensory systems to respond to a wide range of environmental signals, by adapting their response properties to the statistics of the stimulus in order to maximize information transmission. We derive rules of optimal adaptation to changes in the mean and variance of a continuous stimulus in terms of Bayesian filters and map them onto stochastic equations that couple the state of the environment to an internal variable controlling the response function. We calculate numerical and exact results for the speed and accuracy of adaptation and its impact on information transmission. We find that, in the regime of efficient adaptation, the speed of adaptation scales sublinearly with the rate of change of the environment. Finally, we exploit the mathematical equivalence between adaptation and stochastic thermodynamics to quantitatively relate adaptation to the irreversibility of the adaptation time course, defined by the rate of entropy production. Our results suggest a means to empirically quantify adaptation in a model-free and nonparametric way.
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Affiliation(s)
- Daniele Conti
- Laboratoire de Physique, École Normale Supérieure, CNRS, PSL Université, Sorbonne Université, Université de Paris, 75005 Paris, France
| | - Thierry Mora
- Laboratoire de Physique, École Normale Supérieure, CNRS, PSL Université, Sorbonne Université, Université de Paris, 75005 Paris, France
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6
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Abstract
An ultimate goal in retina science is to understand how the neural circuit of the retina processes natural visual scenes. Yet most studies in laboratories have long been performed with simple, artificial visual stimuli such as full-field illumination, spots of light, or gratings. The underlying assumption is that the features of the retina thus identified carry over to the more complex scenario of natural scenes. As the application of corresponding natural settings is becoming more commonplace in experimental investigations, this assumption is being put to the test and opportunities arise to discover processing features that are triggered by specific aspects of natural scenes. Here, we review how natural stimuli have been used to probe, refine, and complement knowledge accumulated under simplified stimuli, and we discuss challenges and opportunities along the way toward a comprehensive understanding of the encoding of natural scenes. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Dimokratis Karamanlis
- Department of Ophthalmology, University Medical Center Göttingen, Göttingen, Germany.,Bernstein Center for Computational Neuroscience Göttingen, Göttingen, Germany.,International Max Planck Research School for Neurosciences, Göttingen, Germany
| | - Helene Marianne Schreyer
- Department of Ophthalmology, University Medical Center Göttingen, Göttingen, Germany.,Bernstein Center for Computational Neuroscience Göttingen, Göttingen, Germany
| | - Tim Gollisch
- Department of Ophthalmology, University Medical Center Göttingen, Göttingen, Germany.,Bernstein Center for Computational Neuroscience Göttingen, Göttingen, Germany.,Cluster of Excellence "Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells" (MBExC), University of Göttingen, Göttingen, Germany
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7
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Zapp SJ, Nitsche S, Gollisch T. Retinal receptive-field substructure: scaffolding for coding and computation. Trends Neurosci 2022; 45:430-445. [DOI: 10.1016/j.tins.2022.03.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/28/2022] [Accepted: 03/17/2022] [Indexed: 11/29/2022]
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8
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Murray KT, Wang MB, Lynch N. Emergence of Direction-Selective Retinal Cell Types in Task-Optimized Deep Learning Models. J Comput Biol 2022; 29:370-381. [PMID: 35275740 DOI: 10.1089/cmb.2021.0368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Convolutional neural networks (CNNs), a class of deep learning models, have experienced recent success in modeling sensory cortices and retinal circuits through optimizing performance on machine learning tasks, otherwise known as task optimization. Previous research has shown task-optimized CNNs to be capable of providing explanations as to why the retina efficiently encodes natural stimuli and how certain retinal cell types are involved in efficient encoding. In our work, we sought to use task-optimized CNNs as a means of explaining computational mechanisms responsible for motion-selective retinal circuits. We designed a biologically constrained CNN and optimized its performance on a motion-classification task. We drew inspiration from psychophysics, deep learning, and systems neuroscience literature to develop a toolbox of methods to reverse engineer the computational mechanisms learned in our model. Through reverse engineering our model, we proposed a computational mechanism in which direction-selective ganglion cells and starburst amacrine cells, both experimentally observed retinal cell types, emerge in our model to discriminate among moving stimuli. This emergence suggests that direction-selective circuits in the retina are ecologically designed to robustly discriminate among moving stimuli. Our results and methods also provide a framework for how to build more interpretable deep learning models and how to understand them.
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Affiliation(s)
- Keith T Murray
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Mien Brabeeba Wang
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Nancy Lynch
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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9
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Opposite forms of adaptation in mouse visual cortex are controlled by distinct inhibitory microcircuits. Nat Commun 2022; 13:1031. [PMID: 35210417 PMCID: PMC8873261 DOI: 10.1038/s41467-022-28635-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Accepted: 01/28/2022] [Indexed: 01/29/2023] Open
Abstract
Sensory processing in the cortex adapts to the history of stimulation but the mechanisms are not understood. Imaging the primary visual cortex of mice we find here that an increase in stimulus contrast is not followed by a simple decrease in gain of pyramidal cells; as many cells increase gain to improve detection of a subsequent decrease in contrast. Depressing and sensitizing forms of adaptation also occur in different types of interneurons (PV, SST and VIP) and the net effect within individual pyramidal cells reflects the balance of PV inputs, driving depression, and a subset of SST interneurons driving sensitization. Changes in internal state associated with locomotion increase gain across the population of pyramidal cells while maintaining the balance between these opposite forms of plasticity, consistent with activation of both VIP->SST and SST->PV disinhibitory pathways. These results reveal how different inhibitory microcircuits adjust the gain of pyramidal cells signalling changes in stimulus strength. The authors describe the role of inhibitory microcircuits in the visual cortex of mice in adaptation to contrast. They show how external stimuli and internal state interact to adjust processing in the visual cortex.
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10
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Inhibition, but not excitation, recovers from partial cone loss with greater spatiotemporal integration, synapse density, and frequency. Cell Rep 2022; 38:110317. [PMID: 35108533 PMCID: PMC8865908 DOI: 10.1016/j.celrep.2022.110317] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 11/30/2021] [Accepted: 01/07/2022] [Indexed: 12/30/2022] Open
Abstract
Neural circuits function in the face of changing inputs, either caused by normal variation in stimuli or by cell death. To maintain their ability to perform essential computations with partial inputs, neural circuits make modifications. Here, we study the retinal circuit’s responses to changes in light stimuli or in photoreceptor inputs by inducing partial cone death in the mature mouse retina. Can the retina withstand or recover from input loss? We find that the excitatory pathways exhibit functional loss commensurate with cone death and with some aspects predicted by partial light stimulation. However, inhibitory pathways recover functionally from lost input by increasing spatiotemporal integration in a way that is not recapitulated by partially stimulating the control retina. Anatomically, inhibitory synapses are upregulated on secondary bipolar cells and output ganglion cells. These findings demonstrate the greater capacity for inhibition, compared with excitation, to modify spatiotemporal processing with fewer cone inputs. Lee et al. find partial cone loss triggers inhibition, but not excitation, to increase spatiotemporal integration, recover contrast gain, and increase synaptic release onto retinal ganglion cells. Natural images filtered by cone-loss receptive fields perceptually match those of controls. Thus, inhibition compensates for fewer cones to potentially preserve perception.
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11
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Yedutenko M, Howlett MHC, Kamermans M. Enhancing the dark side: asymmetric gain of cone photoreceptors underpins their discrimination of visual scenes based on skewness. J Physiol 2021; 600:123-142. [PMID: 34783026 PMCID: PMC9300210 DOI: 10.1113/jp282152] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 11/11/2021] [Indexed: 11/08/2022] Open
Abstract
Psychophysical data indicate that humans can discriminate visual scenes based on their skewness, i.e. the ratio of dark and bright patches within a visual scene. It has also been shown that at a phenomenological level this skew discrimination is described by the so-called blackshot mechanism, which accentuates strong negative contrasts within a scene. Here, we present a set of observations suggesting that the underlying computation might start as early as the cone phototransduction cascade, whose gain is higher for strong negative contrasts than for strong positive contrasts. We recorded from goldfish cone photoreceptors and found that the asymmetry in the phototransduction gain leads to responses with larger amplitudes when using negatively rather than positively skewed light stimuli. This asymmetry in amplitude was present in the cone photocurrent, voltage response and synaptic output. Given that the properties of the phototransduction cascade are universal across vertebrates, it is possible that the mechanism shown here gives rise to a general ability to discriminate between scenes based only on their skewness, which psychophysical studies have shown humans can do. Thus, our data suggest the importance of non-linearity of the early photoreceptor for perception. Additionally, we found that stimulus skewness leads to a subtle change in photoreceptor kinetics. For negatively skewed stimuli, the impulse response functions of the cone peak later than for positively skewed stimuli. However, stimulus skewness does not affect the overall integration time of the cone. KEY POINTS: Humans can discriminate visual scenes based on skewness, i.e. the relative prevalence of bright and dark patches within a scene. Here, we show that negatively skewed time-series stimuli induce larger responses in goldfish cone photoreceptors than comparable positively skewed stimuli. This response asymmetry originates from within the phototransduction cascade, where gain is higher for strong negative contrasts (dark patches) than for strong positive contrasts (bright patches). Unlike the implicit assumption often contained within models of downstream visual neurons, our data show that cone photoreceptors do not simply relay linearly filtered versions of visual stimuli to downstream circuitry, but that they also emphasize specific stimulus features. Given that the phototransduction cascade properties among vertebrate retinas are mostly universal, our data imply that the skew discrimination by human subjects reported in psychophysical studies might stem from the asymmetric gain function of the phototransduction cascade.
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Affiliation(s)
- Matthew Yedutenko
- Retinal Signal Processing Laboratory, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Marcus H C Howlett
- Retinal Signal Processing Laboratory, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Maarten Kamermans
- Retinal Signal Processing Laboratory, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands.,Department of Biomedical Physics and Biomedical Optics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
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12
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Ding X, Lee D, Grant S, Stein H, McIntosh L, Maheswaranathan N, Baccus S. A mechanistically interpretable model of the retinal neural code for natural scenes with multiscale adaptive dynamics. CONFERENCE RECORD. ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS 2021; 2021:287-291. [PMID: 38013729 PMCID: PMC10680971 DOI: 10.1109/ieeeconf53345.2021.9723187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
The visual system processes stimuli over a wide range of spatiotemporal scales, with individual neurons receiving input from tens of thousands of neurons whose dynamics range from milliseconds to tens of seconds. This poses a challenge to create models that both accurately capture visual computations and are mechanistically interpretable. Here we present a model of salamander retinal ganglion cell spiking responses recorded with a multielectrode array that captures natural scene responses and slow adaptive dynamics. The model consists of a three-layer convolutional neural network (CNN) modified to include local recurrent synaptic dynamics taken from a linear-nonlinear-kinetic (LNK) model [1]. We presented alternating natural scenes and uniform field white noise stimuli designed to engage slow contrast adaptation. To overcome difficulties fitting slow and fast dynamics together, we first optimized all fast spatiotemporal parameters, then separately optimized recurrent slow synaptic parameters. The resulting full model reproduces a wide range of retinal computations and is mechanistically interpretable, having internal units that correspond to retinal interneurons with biophysically modeled synapses. This model allows us to study the contribution of model units to any retinal computation, and examine how long-term adaptation changes the retinal neural code for natural scenes through selective adaptation of retinal pathways.
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Affiliation(s)
- Xuehao Ding
- Department of Applied Physics, Stanford University, Stanford, USA
| | - Dongsoo Lee
- Neurosciences PhD Program, Stanford University, Stanford, USA
| | - Satchel Grant
- Department of Psychology, Stanford University, Stanford, USA
| | - Heike Stein
- August Pi i Sunyer Biomedical Research Institute, University of Barcelona, Barcelona, Spain
| | - Lane McIntosh
- Neurosciences PhD Program, Stanford University, Stanford, USA
| | | | - Stephen Baccus
- Neurobiology Department, Stanford University, Stanford, USA
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13
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Canham T, Vazquez-Corral J, Mathieu E, Bertalmío M. Matching visual induction effects on screens of different size. J Vis 2021; 21:10. [PMID: 34144607 PMCID: PMC8237091 DOI: 10.1167/jov.21.6.10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
In the film industry, the same movie is expected to be watched on displays of vastly different sizes, from cinema screens to mobile phones. But visual induction, the perceptual phenomenon by which the appearance of a scene region is affected by its surroundings, will be different for the same image shown on two displays of different dimensions. This phenomenon presents a practical challenge for the preservation of the artistic intentions of filmmakers, because it can lead to shifts in image appearance between viewing destinations. In this work, we show that a neural field model based on the efficient representation principle is able to predict induction effects and how, by regularizing its associated energy functional, the model is still able to represent induction but is now invertible. From this finding, we propose a method to preprocess an image in a screen-size dependent way so that its perception, in terms of visual induction, may remain constant across displays of different size. The potential of the method is demonstrated through psychophysical experiments on synthetic images and qualitative examples on natural images.
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Affiliation(s)
- Trevor Canham
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.,
| | - Javier Vazquez-Corral
- Computer Vision Center and the Computer Sciences Department at Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain., http://www.jvazquez-corral.net
| | | | - Marcelo Bertalmío
- Instituto de óptica, Spanish National Research Council (CSIC), Spain.,
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14
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Abstract
The ability to adapt to changes in stimulus statistics is a hallmark of sensory systems. Here, we developed a theoretical framework that can account for the dynamics of adaptation from an information processing perspective. We use this framework to optimize and analyze adaptive sensory codes, and we show that codes optimized for stationary environments can suffer from prolonged periods of poor performance when the environment changes. To mitigate the adversarial effects of these environmental changes, sensory systems must navigate tradeoffs between the ability to accurately encode incoming stimuli and the ability to rapidly detect and adapt to changes in the distribution of these stimuli. We derive families of codes that balance these objectives, and we demonstrate their close match to experimentally observed neural dynamics during mean and variance adaptation. Our results provide a unifying perspective on adaptation across a range of sensory systems, environments, and sensory tasks.
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15
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Nonlinear spatial integration in retinal bipolar cells shapes the encoding of artificial and natural stimuli. Neuron 2021; 109:1692-1706.e8. [PMID: 33798407 PMCID: PMC8153253 DOI: 10.1016/j.neuron.2021.03.015] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 01/22/2021] [Accepted: 03/10/2021] [Indexed: 11/21/2022]
Abstract
The retina dissects the visual scene into parallel information channels, which extract specific visual features through nonlinear processing. The first nonlinear stage is typically considered to occur at the output of bipolar cells, resulting from nonlinear transmitter release from synaptic terminals. In contrast, we show here that bipolar cells themselves can act as nonlinear processing elements at the level of their somatic membrane potential. Intracellular recordings from bipolar cells in the salamander retina revealed frequent nonlinear integration of visual signals within bipolar cell receptive field centers, affecting the encoding of artificial and natural stimuli. These nonlinearities provide sensitivity to spatial structure below the scale of bipolar cell receptive fields in both bipolar and downstream ganglion cells and appear to arise at the excitatory input into bipolar cells. Thus, our data suggest that nonlinear signal pooling starts earlier than previously thought: that is, at the input stage of bipolar cells. Some retinal bipolar cells represent visual contrast in a nonlinear fashion These bipolar cells also nonlinearly integrate visual signals over space The spatial nonlinearity affects the encoding of natural stimuli by bipolar cells The nonlinearity results from feedforward input, not from feedback inhibition
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16
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Fehrman C, Robbins TD, Meliza CD. Nonlinear effects of intrinsic dynamics on temporal encoding in a model of avian auditory cortex. PLoS Comput Biol 2021; 17:e1008768. [PMID: 33617539 PMCID: PMC7932506 DOI: 10.1371/journal.pcbi.1008768] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 03/04/2021] [Accepted: 02/04/2021] [Indexed: 11/18/2022] Open
Abstract
Neurons exhibit diverse intrinsic dynamics, which govern how they integrate synaptic inputs to produce spikes. Intrinsic dynamics are often plastic during development and learning, but the effects of these changes on stimulus encoding properties are not well known. To examine this relationship, we simulated auditory responses to zebra finch song using a linear-dynamical cascade model, which combines a linear spectrotemporal receptive field with a dynamical, conductance-based neuron model, then used generalized linear models to estimate encoding properties from the resulting spike trains. We focused on the effects of a low-threshold potassium current (KLT) that is present in a subset of cells in the zebra finch caudal mesopallium and is affected by early auditory experience. We found that KLT affects both spike adaptation and the temporal filtering properties of the receptive field. The direction of the effects depended on the temporal modulation tuning of the linear (input) stage of the cascade model, indicating a strongly nonlinear relationship. These results suggest that small changes in intrinsic dynamics in tandem with differences in synaptic connectivity can have dramatic effects on the tuning of auditory neurons. Experience-dependent developmental plasticity involves changes not only to synaptic connections, but to voltage-gated currents as well. Using biophysical models, it is straightforward to predict the effects of this intrinsic plasticity on the firing patterns of individual neurons, but it remains difficult to understand the consequences for sensory coding. We investigated this in the context of the zebra finch auditory cortex, where early exposure to a complex acoustic environment causes increased expression of a low-threshold potassium current. We simulated responses to song using a detailed biophysical model and then characterized encoding properties using generalized linear models. This analysis revealed that this potassium current has strong, nonlinear effects on how the model encodes the song’s temporal structure, and that the sign of these effects depend on the temporal tuning of the synaptic inputs. This nonlinearity gives intrinsic plasticity broad scope as a mechanism for developmental learning in the auditory system.
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Affiliation(s)
- Christof Fehrman
- Psychology Department, University of Virginia, Charlottesville, Virginia, United States of America
| | - Tyler D. Robbins
- Cognitive Science Program, University of Virginia, Charlottesville, Virginia, United States of America
| | - C. Daniel Meliza
- Psychology Department, University of Virginia, Charlottesville, Virginia, United States of America
- Neuroscience Graduate Program, University of Virginia, Charlottesville, Virginia, United States of America
- * E-mail:
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17
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Yedutenko M, Howlett MHC, Kamermans M. High Contrast Allows the Retina to Compute More Than Just Contrast. Front Cell Neurosci 2021; 14:595193. [PMID: 33519381 PMCID: PMC7843368 DOI: 10.3389/fncel.2020.595193] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Accepted: 12/22/2020] [Indexed: 11/29/2022] Open
Abstract
The goal of sensory processing is to represent the environment of an animal. All sensory systems share a similar constraint: they need to encode a wide range of stimulus magnitudes within their narrow neuronal response range. The most efficient way, exploited by even the simplest nervous systems, is to encode relative changes in stimulus magnitude rather than the absolute magnitudes. For instance, the retina encodes contrast, which are the variations of light intensity occurring in time and in space. From this perspective, it is easy to understand why the bright plumage of a moving bird gains a lot of attention, while an octopus remains motionless and mimics its surroundings for concealment. Stronger contrasts simply cause stronger visual signals. However, the gains in retinal performance associated with higher contrast are far more than what can be attributed to just a trivial linear increase in signal strength. Here we discuss how this improvement in performance is reflected throughout different parts of the neural circuitry, within its neural code and how high contrast activates many non-linear mechanisms to unlock several sophisticated retinal computations that are virtually impossible in low contrast conditions.
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Affiliation(s)
- Matthew Yedutenko
- Retinal Signal Processing Lab, Netherlands Institute for Neuroscience, Amsterdam, Netherlands
| | - Marcus H. C. Howlett
- Retinal Signal Processing Lab, Netherlands Institute for Neuroscience, Amsterdam, Netherlands
| | - Maarten Kamermans
- Retinal Signal Processing Lab, Netherlands Institute for Neuroscience, Amsterdam, Netherlands
- Department of Biomedical Physics and Biomedical Optics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
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18
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Latimer KW, Rieke F, Pillow JW. Inferring synaptic inputs from spikes with a conductance-based neural encoding model. eLife 2019; 8:47012. [PMID: 31850846 PMCID: PMC6989090 DOI: 10.7554/elife.47012] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 12/17/2019] [Indexed: 01/15/2023] Open
Abstract
Descriptive statistical models of neural responses generally aim to characterize the mapping from stimuli to spike responses while ignoring biophysical details of the encoding process. Here, we introduce an alternative approach, the conductance-based encoding model (CBEM), which describes a mapping from stimuli to excitatory and inhibitory synaptic conductances governing the dynamics of sub-threshold membrane potential. Remarkably, we show that the CBEM can be fit to extracellular spike train data and then used to predict excitatory and inhibitory synaptic currents. We validate these predictions with intracellular recordings from macaque retinal ganglion cells. Moreover, we offer a novel quasi-biophysical interpretation of the Poisson generalized linear model (GLM) as a special case of the CBEM in which excitation and inhibition are perfectly balanced. This work forges a new link between statistical and biophysical models of neural encoding and sheds new light on the biophysical variables that underlie spiking in the early visual pathway.
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Affiliation(s)
- Kenneth W Latimer
- Department of Physiology and Biophysics, University of Washington, Seattle, United States
| | - Fred Rieke
- Department of Physiology and Biophysics, University of Washington, Seattle, United States
| | - Jonathan W Pillow
- Princeton Neuroscience Institute, Department of Psychology, Princeton University, Princeton, United States
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19
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Zhou J, Benson NC, Kay K, Winawer J. Predicting neuronal dynamics with a delayed gain control model. PLoS Comput Biol 2019; 15:e1007484. [PMID: 31747389 PMCID: PMC6892546 DOI: 10.1371/journal.pcbi.1007484] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Revised: 12/04/2019] [Accepted: 10/10/2019] [Indexed: 11/19/2022] Open
Abstract
Visual neurons respond to static images with specific dynamics: neuronal responses sum sub-additively over time, reduce in amplitude with repeated or sustained stimuli (neuronal adaptation), and are slower at low stimulus contrast. Here, we propose a simple model that predicts these seemingly disparate response patterns observed in a diverse set of measurements-intracranial electrodes in patients, fMRI, and macaque single unit spiking. The model takes a time-varying contrast time course of a stimulus as input, and produces predicted neuronal dynamics as output. Model computation consists of linear filtering, expansive exponentiation, and a divisive gain control. The gain control signal relates to but is slower than the linear signal, and this delay is critical in giving rise to predictions matched to the observed dynamics. Our model is simpler than previously proposed related models, and fitting the model to intracranial EEG data uncovers two regularities across human visual field maps: estimated linear filters (temporal receptive fields) systematically differ across and within visual field maps, and later areas exhibit more rapid and substantial gain control. The model is further generalizable to account for dynamics of contrast-dependent spike rates in macaque V1, and amplitudes of fMRI BOLD in human V1.
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Affiliation(s)
- Jingyang Zhou
- Department of Psychology, New York University, New York City, New York, United States of America
| | - Noah C. Benson
- Department of Psychology, New York University, New York City, New York, United States of America
| | - Kendrick Kay
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Twin Cities, Minnesota, United States of America
| | - Jonathan Winawer
- Department of Psychology, New York University, New York City, New York, United States of America
- Center for Neural Science, New York University, New York City, New York, United States of America
- Stanford Human Intracranial Cognitive Electrophysiology Program (SHICEP), Palo Alto, California, United States of America
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20
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Multiple Timescales Account for Adaptive Responses across Sensory Cortices. J Neurosci 2019; 39:10019-10033. [PMID: 31662427 DOI: 10.1523/jneurosci.1642-19.2019] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 10/01/2019] [Accepted: 10/01/2019] [Indexed: 11/21/2022] Open
Abstract
Sensory systems encounter remarkably diverse stimuli in the external environment. Natural stimuli exhibit timescales and amplitudes of variation that span a wide range. Mechanisms of adaptation, a ubiquitous feature of sensory systems, allow for the accommodation of this range of scales. Are there common rules of adaptation across different sensory modalities? We measured the membrane potential responses of individual neurons in the visual, somatosensory, and auditory cortices of male and female mice to discrete, punctate stimuli delivered at a wide range of fixed and nonfixed frequencies. We find that the adaptive profile of the response is largely preserved across these three areas, exhibiting attenuation and responses to the cessation of stimulation, which are signatures of response to changes in stimulus statistics. We demonstrate that these adaptive responses can emerge from a simple model based on the integration of fixed filters operating over multiple time scales.SIGNIFICANCE STATEMENT Our recent sensations affect our current expectations and perceptions of the environment. Neural correlates of this process exist throughout the brain and are loosely termed adaptation. Adaptive processes have been described across sensory cortices, but direct comparisons of these processes have not been possible because paradigms have been tailored specifically for each modality. We developed a common stimulus set that was used to characterize adaptation in somatosensory, visual, and auditory cortex. We describe here the similarities and differences in adaptation across these cortical areas and demonstrate that adaptive responses may emerge from a set of static filters that operate over a broad range of timescales.
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21
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Weber AI, Fairhall AL. The role of adaptation in neural coding. Curr Opin Neurobiol 2019; 58:135-140. [DOI: 10.1016/j.conb.2019.09.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 08/30/2019] [Accepted: 09/12/2019] [Indexed: 10/25/2022]
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22
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Abstract
With modern neurophysiological methods able to record neural activity throughout the visual pathway in the context of arbitrarily complex visual stimulation, our understanding of visual system function is becoming limited by the available models of visual neurons that can be directly related to such data. Different forms of statistical models are now being used to probe the cellular and circuit mechanisms shaping neural activity, understand how neural selectivity to complex visual features is computed, and derive the ways in which neurons contribute to systems-level visual processing. However, models that are able to more accurately reproduce observed neural activity often defy simple interpretations. As a result, rather than being used solely to connect with existing theories of visual processing, statistical modeling will increasingly drive the evolution of more sophisticated theories.
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Affiliation(s)
- Daniel A. Butts
- Department of Biology and Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland 20742, USA
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23
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Adaptation of Inhibition Mediates Retinal Sensitization. Curr Biol 2019; 29:2640-2651.e4. [PMID: 31378605 DOI: 10.1016/j.cub.2019.06.081] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Revised: 05/14/2019] [Accepted: 06/27/2019] [Indexed: 11/22/2022]
Abstract
In response to a changing sensory environment, sensory systems adjust their neural code for a number of purposes, including an enhanced sensitivity for novel stimuli, prediction of sensory features, and the maintenance of sensitivity. Retinal sensitization is a form of short-term plasticity that elevates local sensitivity following strong, local, visual stimulation and has been shown to create a prediction of the presence of a nearby localized object. The neural mechanism that generates this elevation in sensitivity remains unknown. Using simultaneous intracellular and multielectrode recording in the salamander retina, we show that a decrease in tonic amacrine transmission is necessary for and is correlated spatially and temporally with ganglion cell sensitization. Furthermore, introducing a decrease in amacrine transmission is sufficient to sensitize nearby ganglion cells. A computational model accounting for adaptive dynamics and nonlinear pathways confirms a decrease in steady inhibitory transmission can cause sensitization. Adaptation of inhibition enhances the sensitivity to the sensory feature conveyed by an inhibitory pathway, creating a prediction of future input.
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24
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Abstract
Adaptation is a common principle that recurs throughout the nervous system at all stages of processing. This principle manifests in a variety of phenomena, from spike frequency adaptation, to apparent changes in receptive fields with changes in stimulus statistics, to enhanced responses to unexpected stimuli. The ubiquity of adaptation leads naturally to the question: What purpose do these different types of adaptation serve? A diverse set of theories, often highly overlapping, has been proposed to explain the functional role of adaptive phenomena. In this review, we discuss several of these theoretical frameworks, highlighting relationships among them and clarifying distinctions. We summarize observations of the varied manifestations of adaptation, particularly as they relate to these theoretical frameworks, focusing throughout on the visual system and making connections to other sensory systems.
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Affiliation(s)
- Alison I Weber
- Department of Physiology and Biophysics and Computational Neuroscience Center, University of Washington, Seattle, Washington 98195, USA; ,
| | - Kamesh Krishnamurthy
- Neuroscience Institute and Center for Physics of Biological Function, Department of Physics, Princeton University, Princeton, New Jersey 08544, USA;
| | - Adrienne L Fairhall
- Department of Physiology and Biophysics and Computational Neuroscience Center, University of Washington, Seattle, Washington 98195, USA; , .,UW Institute for Neuroengineering, University of Washington, Seattle, Washington 98195, USA
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25
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Shi Q, Gupta P, Boukhvalova AK, Singer JH, Butts DA. Functional characterization of retinal ganglion cells using tailored nonlinear modeling. Sci Rep 2019; 9:8713. [PMID: 31213620 PMCID: PMC6581951 DOI: 10.1038/s41598-019-45048-8] [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: 11/11/2018] [Accepted: 05/31/2019] [Indexed: 01/30/2023] Open
Abstract
The mammalian retina encodes the visual world in action potentials generated by 20-50 functionally and anatomically-distinct types of retinal ganglion cell (RGC). Individual RGC types receive synaptic input from distinct presynaptic circuits; therefore, their responsiveness to specific features in the visual scene arises from the information encoded in synaptic input and shaped by postsynaptic signal integration and spike generation. Unfortunately, there is a dearth of tools for characterizing the computations reflected in RGC spike output. Therefore, we developed a statistical model, the separable Nonlinear Input Model, to characterize the excitatory and suppressive components of RGC receptive fields. We recorded RGC responses to a correlated noise ("cloud") stimulus in an in vitro preparation of mouse retina and found that our model accurately predicted RGC responses at high spatiotemporal resolution. It identified multiple receptive fields reflecting the main excitatory and suppressive components of the response of each neuron. Significantly, our model accurately identified ON-OFF cells and distinguished their distinct ON and OFF receptive fields, and it demonstrated a diversity of suppressive receptive fields in the RGC population. In total, our method offers a rich description of RGC computation and sets a foundation for relating it to retinal circuitry.
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Affiliation(s)
- Qing Shi
- Department of Biology, University of Maryland, College Park, MD, United States.
| | - Pranjal Gupta
- Department of Biology, University of Maryland, College Park, MD, United States
| | | | - Joshua H Singer
- Department of Biology, University of Maryland, College Park, MD, United States
- Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD, United States
| | - Daniel A Butts
- Department of Biology, University of Maryland, College Park, MD, United States
- Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD, United States
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26
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Gepner R, Wolk J, Wadekar DS, Dvali S, Gershow M. Variance adaptation in navigational decision making. eLife 2018; 7:37945. [PMID: 30480547 PMCID: PMC6257812 DOI: 10.7554/elife.37945] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2018] [Accepted: 10/29/2018] [Indexed: 11/13/2022] Open
Abstract
Sensory systems relay information about the world to the brain, which enacts behaviors through motor outputs. To maximize information transmission, sensory systems discard redundant information through adaptation to the mean and variance of the environment. The behavioral consequences of sensory adaptation to environmental variance have been largely unexplored. Here, we study how larval fruit flies adapt sensory-motor computations underlying navigation to changes in the variance of visual and olfactory inputs. We show that variance adaptation can be characterized by rescaling of the sensory input and that for both visual and olfactory inputs, the temporal dynamics of adaptation are consistent with optimal variance estimation. In multisensory contexts, larvae adapt independently to variance in each sense, and portions of the navigational pathway encoding mixed odor and light signals are also capable of variance adaptation. Our results suggest multiplication as a mechanism for odor-light integration.
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Affiliation(s)
- Ruben Gepner
- Department of Physics, New York University, New York, United States
| | - Jason Wolk
- Department of Physics, New York University, New York, United States
| | | | - Sophie Dvali
- Department of Physics, New York University, New York, United States
| | - Marc Gershow
- Department of Physics, New York University, New York, United States.,Center for Neural Science, New York University, New York, United States.,Neuroscience Institute, New York University, New York, United States
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27
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Ozuysal Y, Kastner DB, Baccus SA. Adaptive feature detection from differential processing in parallel retinal pathways. PLoS Comput Biol 2018; 14:e1006560. [PMID: 30457994 PMCID: PMC6245510 DOI: 10.1371/journal.pcbi.1006560] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Accepted: 10/11/2018] [Indexed: 11/25/2022] Open
Abstract
To transmit information efficiently in a changing environment, the retina adapts to visual contrast by adjusting its gain, latency and mean response. Additionally, the temporal frequency selectivity, or bandwidth changes to encode the absolute intensity when the stimulus environment is noisy, and intensity differences when noise is low. We show that the On pathway of On-Off retinal amacrine and ganglion cells is required to change temporal bandwidth but not other adaptive properties. This remarkably specific adaptive mechanism arises from differential effects of contrast on the On and Off pathways. We analyzed a biophysical model fit only to a cell’s membrane potential, and verified pharmacologically that it accurately revealed the two pathways. We conclude that changes in bandwidth arise mostly from differences in synaptic threshold in the two pathways, rather than synaptic release dynamics as has previously been proposed to underlie contrast adaptation. Different efficient codes are selected by different thresholds in two independently adapting neural pathways.
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Affiliation(s)
- Yusuf Ozuysal
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America
| | - David B. Kastner
- Neuroscience Program, Stanford University, Stanford, CA, United States of America
| | - Stephen A. Baccus
- Department of Neurobiology, Stanford University, Stanford, CA, United States of America
- * E-mail:
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28
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Wienbar S, Schwartz GW. The dynamic receptive fields of retinal ganglion cells. Prog Retin Eye Res 2018; 67:102-117. [PMID: 29944919 PMCID: PMC6235744 DOI: 10.1016/j.preteyeres.2018.06.003] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 06/15/2018] [Accepted: 06/20/2018] [Indexed: 11/30/2022]
Abstract
Retinal ganglion cells (RGCs) were one of the first classes of sensory neurons to be described in terms of a receptive field (RF). Over the last six decades, our understanding of the diversity of RGC types and the nuances of their response properties has grown exponentially. We will review the current understanding of RGC RFs mostly from studies in mammals, but including work from other vertebrates as well. We will argue for a new paradigm that embraces the fluidity of RGC RFs with an eye toward the neuroethology of vision. Specifically, we will focus on (1) different methods for measuring RGC RFs, (2) RF models, (3) feature selectivity and the distinction between fluid and stable RF properties, and (4) ideas about the future of understanding RGC RFs.
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Affiliation(s)
- Sophia Wienbar
- Departments of Ophthalmology and Physiology, Feinberg School of Medicine, Northwestern University, United States.
| | - Gregory W Schwartz
- Departments of Ophthalmology and Physiology, Feinberg School of Medicine, Northwestern University, United States.
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29
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Turner MH, Schwartz GW, Rieke F. Receptive field center-surround interactions mediate context-dependent spatial contrast encoding in the retina. eLife 2018; 7:e38841. [PMID: 30188320 PMCID: PMC6185113 DOI: 10.7554/elife.38841] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2018] [Accepted: 08/29/2018] [Indexed: 11/30/2022] Open
Abstract
Antagonistic receptive field surrounds are a near-universal property of early sensory processing. A key assumption in many models for retinal ganglion cell encoding is that receptive field surrounds are added only to the fully formed center signal. But anatomical and functional observations indicate that surrounds are added before the summation of signals across receptive field subunits that creates the center. Here, we show that this receptive field architecture has an important consequence for spatial contrast encoding in the macaque monkey retina: the surround can control sensitivity to fine spatial structure by changing the way the center integrates visual information over space. The impact of the surround is particularly prominent when center and surround signals are correlated, as they are in natural stimuli. This effect of the surround differs substantially from classic center-surround models and raises the possibility that the surround plays unappreciated roles in shaping ganglion cell sensitivity to natural inputs.
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Affiliation(s)
- Maxwell H Turner
- Department of Physiology and BiophysicsUniversity of WashingtonSeattleUnited States
- Graduate Program in NeuroscienceUniversity of WashingtonSeattleUnited States
| | - Gregory W Schwartz
- Departments of Ophthalmology and Physiology, Feinberg School of MedicineNorthwestern UniversityChicagoUnited States
- Department of Neurobiology, Weinberg College of Arts and SciencesNorthwestern UniversityChicagoUnited States
| | - Fred Rieke
- Department of Physiology and BiophysicsUniversity of WashingtonSeattleUnited States
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30
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Maheswaranathan N, Kastner DB, Baccus SA, Ganguli S. Inferring hidden structure in multilayered neural circuits. PLoS Comput Biol 2018; 14:e1006291. [PMID: 30138312 PMCID: PMC6124781 DOI: 10.1371/journal.pcbi.1006291] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Revised: 09/05/2018] [Accepted: 06/09/2018] [Indexed: 01/26/2023] Open
Abstract
A central challenge in sensory neuroscience involves understanding how neural circuits shape computations across cascaded cell layers. Here we attempt to reconstruct the response properties of experimentally unobserved neurons in the interior of a multilayered neural circuit, using cascaded linear-nonlinear (LN-LN) models. We combine non-smooth regularization with proximal consensus algorithms to overcome difficulties in fitting such models that arise from the high dimensionality of their parameter space. We apply this framework to retinal ganglion cell processing, learning LN-LN models of retinal circuitry consisting of thousands of parameters, using 40 minutes of responses to white noise. Our models demonstrate a 53% improvement in predicting ganglion cell spikes over classical linear-nonlinear (LN) models. Internal nonlinear subunits of the model match properties of retinal bipolar cells in both receptive field structure and number. Subunits have consistently high thresholds, supressing all but a small fraction of inputs, leading to sparse activity patterns in which only one subunit drives ganglion cell spiking at any time. From the model’s parameters, we predict that the removal of visual redundancies through stimulus decorrelation across space, a central tenet of efficient coding theory, originates primarily from bipolar cell synapses. Furthermore, the composite nonlinear computation performed by retinal circuitry corresponds to a boolean OR function applied to bipolar cell feature detectors. Our methods are statistically and computationally efficient, enabling us to rapidly learn hierarchical non-linear models as well as efficiently compute widely used descriptive statistics such as the spike triggered average (STA) and covariance (STC) for high dimensional stimuli. This general computational framework may aid in extracting principles of nonlinear hierarchical sensory processing across diverse modalities from limited data. Computation in neural circuits arises from the cascaded processing of inputs through multiple cell layers. Each of these cell layers performs operations such as filtering and thresholding in order to shape a circuit’s output. It remains a challenge to describe both the computations and the mechanisms that mediate them given limited data recorded from a neural circuit. A standard approach to describing circuit computation involves building quantitative encoding models that predict the circuit response given its input, but these often fail to map in an interpretable way onto mechanisms within the circuit. In this work, we build two layer linear-nonlinear cascade models (LN-LN) in order to describe how the retinal output is shaped by nonlinear mechanisms in the inner retina. We find that these LN-LN models, fit to ganglion cell recordings alone, identify filters and nonlinearities that are readily mapped onto individual circuit components inside the retina, namely bipolar cells and the bipolar-to-ganglion cell synaptic threshold. This work demonstrates how combining simple prior knowledge of circuit properties with partial experimental recordings of a neural circuit’s output can yield interpretable models of the entire circuit computation, including parts of the circuit that are hidden or not directly observed in neural recordings.
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Affiliation(s)
- Niru Maheswaranathan
- Neurosciences Graduate Program, Stanford University, Stanford, California, United States of America
| | - David B. Kastner
- Neurosciences Graduate Program, Stanford University, Stanford, California, United States of America
| | - Stephen A. Baccus
- Department of Neurobiology, Stanford University, Stanford, California, United States of America
| | - Surya Ganguli
- Department of Applied Physics, Stanford University, Stanford, California, United States of America
- * E-mail:
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31
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Fast intensity adaptation enhances the encoding of sound in Drosophila. Nat Commun 2018; 9:134. [PMID: 29317624 PMCID: PMC5760620 DOI: 10.1038/s41467-017-02453-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2017] [Accepted: 12/01/2017] [Indexed: 12/14/2022] Open
Abstract
To faithfully encode complex stimuli, sensory neurons should correct, via adaptation, for stimulus properties that corrupt pattern recognition. Here we investigate sound intensity adaptation in the Drosophila auditory system, which is largely devoted to processing courtship song. Mechanosensory neurons (JONs) in the antenna are sensitive not only to sound-induced antennal vibrations, but also to wind or gravity, which affect the antenna's mean position. Song pattern recognition, therefore, requires adaptation to antennal position (stimulus mean) in addition to sound intensity (stimulus variance). We discover fast variance adaptation in Drosophila JONs, which corrects for background noise over the behaviorally relevant intensity range. We determine where mean and variance adaptation arises and how they interact. A computational model explains our results using a sequence of subtractive and divisive adaptation modules, interleaved by rectification. These results lay the foundation for identifying the molecular and biophysical implementation of adaptation to the statistics of natural sensory stimuli.
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32
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Khani MH, Gollisch T. Diversity in spatial scope of contrast adaptation among mouse retinal ganglion cells. J Neurophysiol 2017; 118:3024-3043. [PMID: 28904106 PMCID: PMC5712662 DOI: 10.1152/jn.00529.2017] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Revised: 09/07/2017] [Accepted: 09/07/2017] [Indexed: 02/05/2023] Open
Abstract
Retinal ganglion cells adapt to changes in visual contrast by adjusting their response kinetics and sensitivity. While much work has focused on the time scales of these adaptation processes, less is known about the spatial scale of contrast adaptation. For example, do small, localized contrast changes affect a cell's signal processing across its entire receptive field? Previous investigations have provided conflicting evidence, suggesting that contrast adaptation occurs either locally within subregions of a ganglion cell's receptive field or globally over the receptive field in its entirety. Here, we investigated the spatial extent of contrast adaptation in ganglion cells of the isolated mouse retina through multielectrode-array recordings. We applied visual stimuli so that ganglion cell receptive fields contained regions where the average contrast level changed periodically as well as regions with constant average contrast level. This allowed us to analyze temporal stimulus integration and sensitivity separately for stimulus regions with and without contrast changes. We found that the spatial scope of contrast adaptation depends strongly on cell identity, with some ganglion cells displaying clear local adaptation, whereas others, in particular large transient ganglion cells, adapted globally to contrast changes. Thus, the spatial scope of contrast adaptation in mouse retinal ganglion cells appears to be cell-type specific. This could reflect differences in mechanisms of contrast adaptation and may contribute to the functional diversity of different ganglion cell types.NEW & NOTEWORTHY Understanding whether adaptation of a neuron in a sensory system can occur locally inside the receptive field or whether it always globally affects the entire receptive field is important for understanding how the neuron processes complex sensory stimuli. For mouse retinal ganglion cells, we here show that both local and global contrast adaptation exist and that this diversity in spatial scope can contribute to the functional diversity of retinal ganglion cell types.
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Affiliation(s)
- Mohammad Hossein Khani
- University Medical Center Göttingen, Dept. of Ophthalmology and Bernstein Center for Computational Neuroscience Göttingen, Göttingen, Germany; and.,International Max Planck Research School for Neuroscience, Göttingen, Germany
| | - Tim Gollisch
- University Medical Center Göttingen, Dept. of Ophthalmology and Bernstein Center for Computational Neuroscience Göttingen, Göttingen, Germany; and
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33
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34
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Krishnamoorthy V, Weick M, Gollisch T. Sensitivity to image recurrence across eye-movement-like image transitions through local serial inhibition in the retina. eLife 2017; 6. [PMID: 28230526 PMCID: PMC5338922 DOI: 10.7554/elife.22431] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2016] [Accepted: 02/20/2017] [Indexed: 01/28/2023] Open
Abstract
Standard models of stimulus encoding in the retina postulate that image presentations activate neurons according to the increase of preferred contrast inside the receptive field. During natural vision, however, images do not arrive in isolation, but follow each other rapidly, separated by sudden gaze shifts. We here report that, contrary to standard models, specific ganglion cells in mouse retina are suppressed after a rapid image transition by changes in visual patterns across the transition, but respond with a distinct spike burst when the same pattern reappears. This sensitivity to image recurrence depends on opposing effects of glycinergic and GABAergic inhibition and can be explained by a circuit of local serial inhibition. Rapid image transitions thus trigger a mode of operation that differs from the processing of simpler stimuli and allows the retina to tag particular image parts or to detect transition types that lead to recurring stimulus patterns. DOI:http://dx.doi.org/10.7554/eLife.22431.001
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Affiliation(s)
- Vidhyasankar Krishnamoorthy
- Department of Ophthalmology, University Medical Center Göttingen, Bernstein Center for Computational Neuroscience Göttingen, Göttingen, Germany.,Visual Coding Group, Max Planck Institute of Neurobiology, Martinsried, Germany
| | - Michael Weick
- Department of Ophthalmology, University Medical Center Göttingen, Bernstein Center for Computational Neuroscience Göttingen, Göttingen, Germany
| | - Tim Gollisch
- Department of Ophthalmology, University Medical Center Göttingen, Bernstein Center for Computational Neuroscience Göttingen, Göttingen, Germany.,Visual Coding Group, Max Planck Institute of Neurobiology, Martinsried, Germany
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35
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Cui Y, Wang YV, Park SJH, Demb JB, Butts DA. Divisive suppression explains high-precision firing and contrast adaptation in retinal ganglion cells. eLife 2016; 5:e19460. [PMID: 27841746 PMCID: PMC5108594 DOI: 10.7554/elife.19460] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Accepted: 10/19/2016] [Indexed: 11/13/2022] Open
Abstract
Visual processing depends on specific computations implemented by complex neural circuits. Here, we present a circuit-inspired model of retinal ganglion cell computation, targeted to explain their temporal dynamics and adaptation to contrast. To localize the sources of such processing, we used recordings at the levels of synaptic input and spiking output in the in vitro mouse retina. We found that an ON-Alpha ganglion cell's excitatory synaptic inputs were described by a divisive interaction between excitation and delayed suppression, which explained nonlinear processing that was already present in ganglion cell inputs. Ganglion cell output was further shaped by spike generation mechanisms. The full model accurately predicted spike responses with unprecedented millisecond precision, and accurately described contrast adaptation of the spike train. These results demonstrate how circuit and cell-intrinsic mechanisms interact for ganglion cell function and, more generally, illustrate the power of circuit-inspired modeling of sensory processing.
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Affiliation(s)
- Yuwei Cui
- Department of Biology, University of Maryland, College Park, United States
- Program in Neuroscience and Cognitive Science, University of Maryland, College Park, United States
| | - Yanbin V Wang
- Department of Ophthalmology and Visual Science, Yale University, New Haven, United States
- Department of Cellular and Molecular Physiology, Yale University, New Haven, United States
| | - Silvia J H Park
- Department of Ophthalmology and Visual Science, Yale University, New Haven, United States
| | - Jonathan B Demb
- Department of Ophthalmology and Visual Science, Yale University, New Haven, United States
- Department of Cellular and Molecular Physiology, Yale University, New Haven, United States
| | - Daniel A Butts
- Department of Biology, University of Maryland, College Park, United States
- Program in Neuroscience and Cognitive Science, University of Maryland, College Park, United States
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36
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Wang Z, Stocker AA, Lee DD. Efficient Neural Codes That Minimize L p Reconstruction Error. Neural Comput 2016; 28:2656-2686. [PMID: 27764595 DOI: 10.1162/neco_a_00900] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
The efficient coding hypothesis assumes that biological sensory systems use neural codes that are optimized to best possibly represent the stimuli that occur in their environment. Most common models use information-theoretic measures, whereas alternative formulations propose incorporating downstream decoding performance. Here we provide a systematic evaluation of different optimality criteria using a parametric formulation of the efficient coding problem based on the [Formula: see text] reconstruction error of the maximum likelihood decoder. This parametric family includes both the information maximization criterion and squared decoding error as special cases. We analytically derived the optimal tuning curve of a single neuron encoding a one-dimensional stimulus with an arbitrary input distribution. We show how the result can be generalized to a class of neural populations by introducing the concept of a meta-tuning curve. The predictions of our framework are tested against previously measured characteristics of some early visual systems found in biology. We find solutions that correspond to low values of [Formula: see text], suggesting that across different animal models, neural representations in the early visual pathways optimize similar criteria about natural stimuli that are relatively close to the information maximization criterion.
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Affiliation(s)
- Zhuo Wang
- Department of Mathematics, University of Pennsylvania, Philadelphia, PA 19104, U.S.A.
| | - Alan A Stocker
- Departments of Psychology and Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, U.S.A.
| | - Daniel D Lee
- Departments of Electrical and Systems Engineering, Computer and Information Science, and Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, U.S.A.
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37
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Martínez-Cañada P, Morillas C, Pino B, Ros E, Pelayo F. A Computational Framework for Realistic Retina Modeling. Int J Neural Syst 2016; 26:1650030. [DOI: 10.1142/s0129065716500301] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Computational simulations of the retina have led to valuable insights about the biophysics of its neuronal activity and processing principles. A great number of retina models have been proposed to reproduce the behavioral diversity of the different visual processing pathways. While many of these models share common computational stages, previous efforts have been more focused on fitting specific retina functions rather than generalizing them beyond a particular model. Here, we define a set of computational retinal microcircuits that can be used as basic building blocks for the modeling of different retina mechanisms. To validate the hypothesis that similar processing structures may be repeatedly found in different retina functions, we implemented a series of retina models simply by combining these computational retinal microcircuits. Accuracy of the retina models for capturing neural behavior was assessed by fitting published electrophysiological recordings that characterize some of the best-known phenomena observed in the retina: adaptation to the mean light intensity and temporal contrast, and differential motion sensitivity. The retinal microcircuits are part of a new software platform for efficient computational retina modeling from single-cell to large-scale levels. It includes an interface with spiking neural networks that allows simulation of the spiking response of ganglion cells and integration with models of higher visual areas.
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Affiliation(s)
- Pablo Martínez-Cañada
- Department of Computer Architecture and Technology, CITIC-UGR, University of Granada, Spain
| | - Christian Morillas
- Department of Computer Architecture and Technology, CITIC-UGR, University of Granada, Spain
| | - Begoña Pino
- Department of Computer Architecture and Technology, CITIC-UGR, University of Granada, Spain
| | - Eduardo Ros
- Department of Computer Architecture and Technology, CITIC-UGR, University of Granada, Spain
| | - Francisco Pelayo
- Department of Computer Architecture and Technology, CITIC-UGR, University of Granada, Spain
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38
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Abstract
As information flows through the brain, neuronal firing progresses from encoding the world as sensed by the animal to driving the motor output of subsequent behavior. One of the more tractable goals of quantitative neuroscience is to develop predictive models that relate the sensory or motor streams with neuronal firing. Here we review and contrast analytical tools used to accomplish this task. We focus on classes of models in which the external variable is compared with one or more feature vectors to extract a low-dimensional representation, the history of spiking and other variables are potentially incorporated, and these factors are nonlinearly transformed to predict the occurrences of spikes. We illustrate these techniques in application to datasets of different degrees of complexity. In particular, we address the fitting of models in the presence of strong correlations in the external variable, as occurs in natural sensory stimuli and in movement. Spectral correlation between predicted and measured spike trains is introduced to contrast the relative success of different methods.
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Affiliation(s)
- Johnatan Aljadeff
- Department of Physics, University of California, San Diego, San Diego, CA 92093, USA; Department of Neurobiology, University of Chicago, Chicago, IL 60637, USA.
| | - Benjamin J Lansdell
- Department of Applied Mathematics, University of Washington, Seattle, WA 98195, USA
| | - Adrienne L Fairhall
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA; WRF UW Institute for Neuroengineering, University of Washington, Seattle, WA 98195, USA
| | - David Kleinfeld
- Department of Physics, University of California, San Diego, San Diego, CA 92093, USA; Section of Neurobiology, University of California, San Diego, La Jolla, CA 92093, USA; Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA.
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39
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Butts DA, Cui Y, Casti ARR. Nonlinear computations shaping temporal processing of precortical vision. J Neurophysiol 2016; 116:1344-57. [PMID: 27334959 DOI: 10.1152/jn.00878.2015] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2015] [Accepted: 06/17/2016] [Indexed: 11/22/2022] Open
Abstract
Computations performed by the visual pathway are constructed by neural circuits distributed over multiple stages of processing, and thus it is challenging to determine how different stages contribute on the basis of recordings from single areas. In the current article, we address this problem in the lateral geniculate nucleus (LGN), using experiments combined with nonlinear modeling capable of isolating various circuit contributions. We recorded cat LGN neurons presented with temporally modulated spots of various sizes, which drove temporally precise LGN responses. We utilized simultaneously recorded S-potentials, corresponding to the primary retinal ganglion cell (RGC) input to each LGN cell, to distinguish the computations underlying temporal precision in the retina from those in the LGN. Nonlinear models with excitatory and delayed suppressive terms were sufficient to explain temporal precision in the LGN, and we found that models of the S-potentials were nearly identical, although with a lower threshold. To determine whether additional influences shaped the response at the level of the LGN, we extended this model to use the S-potential input in combination with stimulus-driven terms to predict the LGN response. We found that the S-potential input "explained away" the major excitatory and delayed suppressive terms responsible for temporal patterning of LGN spike trains but revealed additional contributions, largely PULL suppression, to the LGN response. Using this novel combination of recordings and modeling, we were thus able to dissect multiple circuit contributions to LGN temporal responses across retina and LGN, and set the foundation for targeted study of each stage.
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Affiliation(s)
- Daniel A Butts
- Department of Biology and Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland; and
| | - Yuwei Cui
- Department of Biology and Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland; and
| | - Alexander R R Casti
- Department of Mathematics, Gildart-Haase School of Engineering and Computer Sciences, Fairleigh Dickinson University, Teaneck, New Jersey
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40
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Kastner DB, Schwalger T, Ziegler L, Gerstner W. A Model of Synaptic Reconsolidation. Front Neurosci 2016; 10:206. [PMID: 27242410 PMCID: PMC4870270 DOI: 10.3389/fnins.2016.00206] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2015] [Accepted: 04/25/2016] [Indexed: 11/18/2022] Open
Abstract
Reconsolidation of memories has mostly been studied at the behavioral and molecular level. Here, we put forward a simple extension of existing computational models of synaptic consolidation to capture hippocampal slice experiments that have been interpreted as reconsolidation at the synaptic level. The model implements reconsolidation through stabilization of consolidated synapses by stabilizing entities combined with an activity-dependent reservoir of stabilizing entities that are immune to protein synthesis inhibition (PSI). We derive a reduced version of our model to explore the conditions under which synaptic reconsolidation does or does not occur, often referred to as the boundary conditions of reconsolidation. We find that our computational model of synaptic reconsolidation displays complex boundary conditions. Our results suggest that a limited resource of hypothetical stabilizing molecules or complexes, which may be implemented by protein phosphorylation or different receptor subtypes, can underlie the phenomenon of synaptic reconsolidation.
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Affiliation(s)
- David B Kastner
- School of Computer and Communication Sciences and Brain Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne Lausanne, Switzerland
| | - Tilo Schwalger
- School of Computer and Communication Sciences and Brain Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne Lausanne, Switzerland
| | - Lorric Ziegler
- School of Computer and Communication Sciences and Brain Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne Lausanne, Switzerland
| | - Wulfram Gerstner
- School of Computer and Communication Sciences and Brain Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne Lausanne, Switzerland
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41
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Martínez-Álvarez A, Crespo-Cano R, Díaz-Tahoces A, Cuenca-Asensi S, Ferrández Vicente JM, Fernández E. Automatic Tuning of a Retina Model for a Cortical Visual Neuroprosthesis Using a Multi-Objective Optimization Genetic Algorithm. Int J Neural Syst 2016; 26:1650021. [PMID: 27354187 DOI: 10.1142/s0129065716500210] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The retina is a very complex neural structure, which contains many different types of neurons interconnected with great precision, enabling sophisticated conditioning and coding of the visual information before it is passed via the optic nerve to higher visual centers. The encoding of visual information is one of the basic questions in visual and computational neuroscience and is also of seminal importance in the field of visual prostheses. In this framework, it is essential to have artificial retina systems to be able to function in a way as similar as possible to the biological retinas. This paper proposes an automatic evolutionary multi-objective strategy based on the NSGA-II algorithm for tuning retina models. Four metrics were adopted for guiding the algorithm in the search of those parameters that best approximate a synthetic retinal model output with real electrophysiological recordings. Results show that this procedure exhibits a high flexibility when different trade-offs has to be considered during the design of customized neuro prostheses.
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Affiliation(s)
- Antonio Martínez-Álvarez
- 1 Department of Computer Technology, University of Alicante, Carretera San Vicente s/n, Alicante 03690, Spain
| | - Rubén Crespo-Cano
- 1 Department of Computer Technology, University of Alicante, Carretera San Vicente s/n, Alicante 03690, Spain
| | - Ariadna Díaz-Tahoces
- 2 Institute of Bioengineering, University Miguel Hernández and CIBER BBN, Av. de la Universidad s/n, Elche (Alicante), 03202, Spain
| | - Sergio Cuenca-Asensi
- 1 Department of Computer Technology, University of Alicante, Carretera San Vicente s/n, Alicante 03690, Spain
| | - José Manuel Ferrández Vicente
- 3 Telecomm School, Universidad Politécnica de Cartagena, Campus Muralla del Mar s/n, Cartagena (Murcia), 30202, Spain
| | - Eduardo Fernández
- 2 Institute of Bioengineering, University Miguel Hernández and CIBER BBN, Av. de la Universidad s/n, Elche (Alicante), 03202, Spain
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42
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Thorson IL, Liénard J, David SV. The Essential Complexity of Auditory Receptive Fields. PLoS Comput Biol 2015; 11:e1004628. [PMID: 26683490 PMCID: PMC4684325 DOI: 10.1371/journal.pcbi.1004628] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2015] [Accepted: 10/26/2015] [Indexed: 12/05/2022] Open
Abstract
Encoding properties of sensory neurons are commonly modeled using linear finite impulse response (FIR) filters. For the auditory system, the FIR filter is instantiated in the spectro-temporal receptive field (STRF), often in the framework of the generalized linear model. Despite widespread use of the FIR STRF, numerous formulations for linear filters are possible that require many fewer parameters, potentially permitting more efficient and accurate model estimates. To explore these alternative STRF architectures, we recorded single-unit neural activity from auditory cortex of awake ferrets during presentation of natural sound stimuli. We compared performance of > 1000 linear STRF architectures, evaluating their ability to predict neural responses to a novel natural stimulus. Many were able to outperform the FIR filter. Two basic constraints on the architecture lead to the improved performance: (1) factorization of the STRF matrix into a small number of spectral and temporal filters and (2) low-dimensional parameterization of the factorized filters. The best parameterized model was able to outperform the full FIR filter in both primary and secondary auditory cortex, despite requiring fewer than 30 parameters, about 10% of the number required by the FIR filter. After accounting for noise from finite data sampling, these STRFs were able to explain an average of 40% of A1 response variance. The simpler models permitted more straightforward interpretation of sensory tuning properties. They also showed greater benefit from incorporating nonlinear terms, such as short term plasticity, that provide theoretical advances over the linear model. Architectures that minimize parameter count while maintaining maximum predictive power provide insight into the essential degrees of freedom governing auditory cortical function. They also maximize statistical power available for characterizing additional nonlinear properties that limit current auditory models. Understanding how the brain solves sensory problems can provide useful insight for the development of automated systems such as speech recognizers and image classifiers. Recent developments in nonlinear regression and machine learning have produced powerful algorithms for characterizing the input-output relationship of complex systems. However, the complexity of sensory neural systems, combined with practical limitations on experimental data, make it difficult to apply arbitrarily complex analyses to neural data. In this study we pushed analysis in the opposite direction, toward simpler models. We asked how simple a model can be while still capturing the essential sensory properties of neurons in auditory cortex. We found that substantially simpler formulations of the widely-used spectro-temporal receptive field are able to perform as well as the best current models. These simpler formulations define new basis sets that can be incorporated into state-of-the-art machine learning algorithms for a more exhaustive exploration of sensory processing.
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Affiliation(s)
- Ivar L. Thorson
- Oregon Hearing Research Center, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Jean Liénard
- Department of Mathematics, Washington State University, Vancouver, Washington, United States of America
| | - Stephen V. David
- Oregon Hearing Research Center, Oregon Health & Science University, Portland, Oregon, United States of America
- * E-mail:
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43
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Cui Y, Wang YV, Demb JB, Butts DA. An accurate circuit-based description of retinal ganglion cell computation. BMC Neurosci 2015. [PMCID: PMC4697562 DOI: 10.1186/1471-2202-16-s1-o9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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44
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Abstract
The first synapses transmitting visual information contain an unusual organelle, the ribbon, which is involved in the transport and priming of vesicles to be released at the active zone. The ribbon is one of many design features that allow efficient refilling of the active zone, which in turn enables graded changes in membrane potential to be transmitted using a continuous mode of neurotransmitter release. The ribbon also plays a key role in supplying vesicles for rapid and transient bursts of release that signal fast changes, such as the onset of light. We increasingly understand how the physiological properties of ribbon synapses determine basic transformations of the visual signal and, in particular, how the process of refilling the active zone regulates the gain and adaptive properties of the retinal circuit. The molecular basis of ribbon function is, however, far from clear.
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Affiliation(s)
- Leon Lagnado
- School of Life Sciences, University of Sussex, Brighton BN1 9QG, United Kingdom;
| | - Frank Schmitz
- Department of Neuroanatomy, Institute for Anatomy and Cell Biology, Medical School Saarland University, Homburg/Saar, Germany;
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45
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Abstract
The mammalian retina is an important model system for studying neural circuitry: Its role in sensation is clear, its cell types are relatively well defined, and its responses to natural stimuli-light patterns-can be studied in vitro. To solve the retina, we need to understand how the circuits presynaptic to its output neurons, ganglion cells, divide the visual scene into parallel representations to be assembled and interpreted by the brain. This requires identifying the component interneurons and understanding how their intrinsic properties and synapses generate circuit behaviors. Because the cellular composition and fundamental properties of the retina are shared across species, basic mechanisms studied in the genetically modifiable mouse retina apply to primate vision. We propose that the apparent complexity of retinal computation derives from a straightforward mechanism-a dynamic balance of synaptic excitation and inhibition regulated by use-dependent synaptic depression-applied differentially to the parallel pathways that feed ganglion cells.
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Affiliation(s)
- Jonathan B Demb
- Department of Ophthalmology and Visual Science and Department of Cellular and Molecular Physiology, Yale University, New Haven, Connecticut 06511;
| | - Joshua H Singer
- Department of Biology, University of Maryland, College Park, Maryland 20742;
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46
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Jadzinsky PD, Baccus SA. Synchronized amplification of local information transmission by peripheral retinal input. eLife 2015; 4:e09266. [PMID: 26568312 PMCID: PMC4749570 DOI: 10.7554/elife.09266] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2015] [Accepted: 11/12/2015] [Indexed: 11/13/2022] Open
Abstract
Sensory stimuli have varying statistics influenced by both the environment and by active sensing behaviors that rapidly and globally change the sensory input. Consequently, sensory systems often adjust their neural code to the expected statistics of their sensory input to transmit novel sensory information. Here, we show that sudden peripheral motion amplifies and accelerates information transmission in salamander ganglion cells in a 50 ms time window. Underlying this gating of information is a transient increase in adaptation to contrast, enhancing sensitivity to a broader range of stimuli. Using a model and natural images, we show that this effect coincides with an expected increase in information in bipolar cells after a global image shift. Our findings reveal the dynamic allocation of energy resources to increase neural activity at times of expected high information content, a principle of adaptation that balances the competing requirements of conserving spikes and transmitting information.
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Affiliation(s)
- Pablo D Jadzinsky
- Department of Neurobiology, Stanford University School of Medicine, Stanford, United States
| | - Stephen A Baccus
- Department of Neurobiology, Stanford University School of Medicine, Stanford, United States
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47
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Neagu I, Levine E. A Primer on Quantitative Modeling. Methods Mol Biol 2015; 1327:241-50. [PMID: 26423980 DOI: 10.1007/978-1-4939-2842-2_18] [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] [Indexed: 02/17/2023]
Abstract
Caenorhabditis elegans is particularly suitable for obtaining quantitative data about behavior, neuronal activity, gene expression, ecological interactions, quantitative traits, and much more. To exploit the full potential of these data one seeks to interpret them within quantitative models. Using two examples from the C. elegans literature we briefly explore several types of modeling approaches relevant to worm biology, and show how they might be used to interpret data, formulate testable hypotheses, and suggest new experiments. We emphasize that the choice of modeling approach is strongly dependent on the questions of interest and the type of available knowledge.
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Affiliation(s)
- Iulia Neagu
- Department of Physics and FAS Center for Systems Biology, Harvard University, 17 Oxford Street, Cambridge, MA, 02138, USA
| | - Erel Levine
- Department of Physics and FAS Center for Systems Biology, Harvard University, 17 Oxford Street, Cambridge, MA, 02138, USA.
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48
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Liu JK, Gollisch T. Spike-Triggered Covariance Analysis Reveals Phenomenological Diversity of Contrast Adaptation in the Retina. PLoS Comput Biol 2015; 11:e1004425. [PMID: 26230927 PMCID: PMC4521887 DOI: 10.1371/journal.pcbi.1004425] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2015] [Accepted: 07/03/2015] [Indexed: 11/25/2022] Open
Abstract
When visual contrast changes, retinal ganglion cells adapt by adjusting their sensitivity as well as their temporal filtering characteristics. The latter has classically been described by contrast-induced gain changes that depend on temporal frequency. Here, we explored a new perspective on contrast-induced changes in temporal filtering by using spike-triggered covariance analysis to extract multiple parallel temporal filters for individual ganglion cells. Based on multielectrode-array recordings from ganglion cells in the isolated salamander retina, we found that contrast adaptation of temporal filtering can largely be captured by contrast-invariant sets of filters with contrast-dependent weights. Moreover, differences among the ganglion cells in the filter sets and their contrast-dependent contributions allowed us to phenomenologically distinguish three types of filter changes. The first type is characterized by newly emerging features at higher contrast, which can be reproduced by computational models that contain response-triggered gain-control mechanisms. The second type follows from stronger adaptation in the Off pathway as compared to the On pathway in On-Off-type ganglion cells. Finally, we found that, in a subset of neurons, contrast-induced filter changes are governed by particularly strong spike-timing dynamics, in particular by pronounced stimulus-dependent latency shifts that can be observed in these cells. Together, our results show that the contrast dependence of temporal filtering in retinal ganglion cells has a multifaceted phenomenology and that a multi-filter analysis can provide a useful basis for capturing the underlying signal-processing dynamics. Our sensory systems have to process stimuli under a wide range of environmental conditions. To cope with this challenge, the involved neurons adapt by adjusting their signal processing to the recently encountered intensity range. In the visual system, one finds, for example, that higher visual contrast leads to changes in how visual signals are temporally filtered, making signal processing faster and more band-pass-like at higher contrast. By analyzing signals from neurons in the retina of salamanders, we here found that these adaptation effects can be described by a fixed set of filters, independent of contrast, whose relative contributions change with contrast. Also, we found that different phenomena contribute to this adaptation. In particular, some cells change their relative sensitivity to light increments and light decrements, whereas other cells are influenced by a strong contrast-dependence of the exact timing of their responses. Our results show that contrast adaptation in the retina is not an entirely homogeneous phenomenon, and that models with multiple filters can help in characterizing sensory adaptation.
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Affiliation(s)
- Jian K. Liu
- Department of Ophthalmology, University Medical Center Göttingen, Göttingen, Germany
- Bernstein Center for Computational Neuroscience Göttingen, Göttingen, Germany
| | - Tim Gollisch
- Department of Ophthalmology, University Medical Center Göttingen, Göttingen, Germany
- Bernstein Center for Computational Neuroscience Göttingen, Göttingen, Germany
- * E-mail:
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49
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Mechanisms for Rapid Adaptive Control of Motion Processing in Macaque Visual Cortex. J Neurosci 2015; 35:10268-80. [PMID: 26180202 DOI: 10.1523/jneurosci.1418-11.2015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
UNLABELLED A key feature of neural networks is their ability to rapidly adjust their function, including signal gain and temporal dynamics, in response to changes in sensory inputs. These adjustments are thought to be important for optimizing the sensitivity of the system, yet their mechanisms remain poorly understood. We studied adaptive changes in temporal integration in direction-selective cells in macaque primary visual cortex, where specific hypotheses have been proposed to account for rapid adaptation. By independently stimulating direction-specific channels, we found that the control of temporal integration of motion at one direction was independent of motion signals driven at the orthogonal direction. We also found that individual neurons can simultaneously support two different profiles of temporal integration for motion in orthogonal directions. These findings rule out a broad range of adaptive mechanisms as being key to the control of temporal integration, including untuned normalization and nonlinearities of spike generation and somatic adaptation in the recorded direction-selective cells. Such mechanisms are too broadly tuned, or occur too far downstream, to explain the channel-specific and multiplexed temporal integration that we observe in single neurons. Instead, we are compelled to conclude that parallel processing pathways are involved, and we demonstrate one such circuit using a computer model. This solution allows processing in different direction/orientation channels to be separately optimized and is sensible given that, under typical motion conditions (e.g., translation or looming), speed on the retina is a function of the orientation of image components. SIGNIFICANCE STATEMENT Many neurons in visual cortex are understood in terms of their spatial and temporal receptive fields. It is now known that the spatiotemporal integration underlying visual responses is not fixed but depends on the visual input. For example, neurons that respond selectively to motion direction integrate signals over a shorter time window when visual motion is fast and a longer window when motion is slow. We investigated the mechanisms underlying this useful adaptation by recording from neurons as they responded to stimuli moving in two different directions at different speeds. Computer simulations of our results enabled us to rule out several candidate theories in favor of a model that integrates across multiple parallel channels that operate at different time scales.
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50
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Neural Mechanisms for Evaluating Environmental Variability in Caenorhabditis elegans. Neuron 2015; 86:428-41. [PMID: 25864633 DOI: 10.1016/j.neuron.2015.03.026] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2014] [Revised: 01/18/2015] [Accepted: 02/20/2015] [Indexed: 11/21/2022]
Abstract
The ability to evaluate variability in the environment is vital for making optimal behavioral decisions. Here we show that Caenorhabditis elegans evaluates variability in its food environment and modifies its future behavior accordingly. We derive a behavioral model that reveals a critical period over which information about the food environment is acquired and predicts future search behavior. We also identify a pair of high-threshold sensory neurons that encode variability in food concentration and the downstream dopamine-dependent circuit that generates appropriate search behavior upon removal from food. Further, we show that CREB is required in a subset of interneurons and determines the timescale over which the variability is integrated. Interestingly, the variability circuit is a subset of a larger circuit driving search behavior, showing that learning directly modifies the very same neurons driving behavior. Our study reveals how a neural circuit decodes environmental variability to generate contextually appropriate decisions.
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