1
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Deister CA, Moore AI, Voigts J, Bechek S, Lichtin R, Brown TC, Moore CI. Neocortical inhibitory imbalance predicts successful sensory detection. Cell Rep 2024; 43:114233. [PMID: 38905102 DOI: 10.1016/j.celrep.2024.114233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 07/17/2023] [Accepted: 04/26/2024] [Indexed: 06/23/2024] Open
Abstract
Perceptual success depends on fast-spiking, parvalbumin-positive interneurons (FS/PVs). However, competing theories of optimal rate and correlation in pyramidal (PYR) firing make opposing predictions regarding the underlying FS/PV dynamics. We addressed this with population calcium imaging of FS/PVs and putative PYR neurons during threshold detection. In primary somatosensory and visual neocortex, a distinct PYR subset shows increased rate and spike-count correlations on detected trials ("hits"), while most show no rate change and decreased correlations. A larger fraction of FS/PVs predicts hits with either rate increases or decreases. Using computational modeling, we found that inhibitory imbalance, created by excitatory "feedback" and interactions between FS/PV pools, can account for the data. Rate-decreasing FS/PVs increase rate and correlation in a PYR subset, while rate-increasing FS/PVs reduce correlations and offset enhanced excitation in PYR neurons. These findings indicate that selection of informative PYR ensembles, through transient inhibitory imbalance, is a common motif of optimal neocortical processing.
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Affiliation(s)
- Christopher A Deister
- Department of Neuroscience and Carney Institute for Brain Sciences, Brown University, Providence, RI, USA
| | - Alexander I Moore
- Department of Neuroscience and Carney Institute for Brain Sciences, Brown University, Providence, RI, USA
| | - Jakob Voigts
- Department of Neuroscience and Carney Institute for Brain Sciences, Brown University, Providence, RI, USA; Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Sophia Bechek
- Department of Neuroscience and Carney Institute for Brain Sciences, Brown University, Providence, RI, USA
| | - Rebecca Lichtin
- Department of Neuroscience and Carney Institute for Brain Sciences, Brown University, Providence, RI, USA
| | - Tyler C Brown
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christopher I Moore
- Department of Neuroscience and Carney Institute for Brain Sciences, Brown University, Providence, RI, USA.
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2
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Nardin M, Csicsvari J, Tkačik G, Savin C. The Structure of Hippocampal CA1 Interactions Optimizes Spatial Coding across Experience. J Neurosci 2023; 43:8140-8156. [PMID: 37758476 PMCID: PMC10697404 DOI: 10.1523/jneurosci.0194-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 09/11/2023] [Accepted: 09/14/2023] [Indexed: 10/03/2023] Open
Abstract
Although much is known about how single neurons in the hippocampus represent an animal's position, how circuit interactions contribute to spatial coding is less well understood. Using a novel statistical estimator and theoretical modeling, both developed in the framework of maximum entropy models, we reveal highly structured CA1 cell-cell interactions in male rats during open field exploration. The statistics of these interactions depend on whether the animal is in a familiar or novel environment. In both conditions the circuit interactions optimize the encoding of spatial information, but for regimes that differ in the informativeness of their spatial inputs. This structure facilitates linear decodability, making the information easy to read out by downstream circuits. Overall, our findings suggest that the efficient coding hypothesis is not only applicable to individual neuron properties in the sensory periphery, but also to neural interactions in the central brain.SIGNIFICANCE STATEMENT Local circuit interactions play a key role in neural computation and are dynamically shaped by experience. However, measuring and assessing their effects during behavior remains a challenge. Here, we combine techniques from statistical physics and machine learning to develop new tools for determining the effects of local network interactions on neural population activity. This approach reveals highly structured local interactions between hippocampal neurons, which make the neural code more precise and easier to read out by downstream circuits, across different levels of experience. More generally, the novel combination of theory and data analysis in the framework of maximum entropy models enables traditional neural coding questions to be asked in naturalistic settings.
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Affiliation(s)
- Michele Nardin
- Institute of Science and Technology Austria, Klosterneuburg AT-3400, Austria
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia 20147
| | - Jozsef Csicsvari
- Institute of Science and Technology Austria, Klosterneuburg AT-3400, Austria
| | - Gašper Tkačik
- Institute of Science and Technology Austria, Klosterneuburg AT-3400, Austria
| | - Cristina Savin
- Center for Neural Science, New York University, New York, New York 10003
- Center for Data Science, New York University, New York, New York 10011
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3
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Ly C, Barreiro AK, Gautam SH, Shew WL. Odor-evoked increases in olfactory bulb mitral cell spiking variability. iScience 2021; 24:102946. [PMID: 34485855 PMCID: PMC8397902 DOI: 10.1016/j.isci.2021.102946] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 07/07/2021] [Accepted: 08/02/2021] [Indexed: 01/04/2023] Open
Abstract
The spiking variability of neural networks has important implications for how information is encoded to higher brain regions. It has been well documented by numerous labs in many cortical and motor regions that spiking variability decreases with stimulus onset, yet whether this principle holds in the OB has not been tested. In stark contrast to this common view, we demonstrate that the onset of sensory input can cause an increase in the variability of neural activity in the mammalian OB. We show this in both anesthetized and awake rodents. Furthermore, we use computational models to describe the mechanisms of this phenomenon. Our findings establish sensory evoked increases in spiking variability as a viable alternative coding strategy.
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Affiliation(s)
- Cheng Ly
- Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Andrea K. Barreiro
- Department of Mathematics, Southern Methodist University, Dallas, TX 75275, USA
| | - Shree Hari Gautam
- Department of Physics, University of Arkansas, Fayetteville, AR 72701, USA
| | - Woodrow L. Shew
- Department of Physics, University of Arkansas, Fayetteville, AR 72701, USA
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4
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Azeredo da Silveira R, Rieke F. The Geometry of Information Coding in Correlated Neural Populations. Annu Rev Neurosci 2021; 44:403-424. [PMID: 33863252 DOI: 10.1146/annurev-neuro-120320-082744] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Neurons in the brain represent information in their collective activity. The fidelity of this neural population code depends on whether and how variability in the response of one neuron is shared with other neurons. Two decades of studies have investigated the influence of these noise correlations on the properties of neural coding. We provide an overview of the theoretical developments on the topic. Using simple, qualitative, and general arguments, we discuss, categorize, and relate the various published results. We emphasize the relevance of the fine structure of noise correlation, and we present a new approach to the issue. Throughout this review, we emphasize a geometrical picture of how noise correlations impact the neural code.
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Affiliation(s)
| | - Fred Rieke
- Department of Physics, Ecole Normale Supérieure, 75005 Paris, France;
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5
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Ito T, Brincat SL, Siegel M, Mill RD, He BJ, Miller EK, Rotstein HG, Cole MW. Task-evoked activity quenches neural correlations and variability across cortical areas. PLoS Comput Biol 2020; 16:e1007983. [PMID: 32745096 PMCID: PMC7425988 DOI: 10.1371/journal.pcbi.1007983] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 08/13/2020] [Accepted: 05/27/2020] [Indexed: 02/06/2023] Open
Abstract
Many large-scale functional connectivity studies have emphasized the importance of communication through increased inter-region correlations during task states. In contrast, local circuit studies have demonstrated that task states primarily reduce correlations among pairs of neurons, likely enhancing their information coding by suppressing shared spontaneous activity. Here we sought to adjudicate between these conflicting perspectives, assessing whether co-active brain regions during task states tend to increase or decrease their correlations. We found that variability and correlations primarily decrease across a variety of cortical regions in two highly distinct data sets: non-human primate spiking data and human functional magnetic resonance imaging data. Moreover, this observed variability and correlation reduction was accompanied by an overall increase in dimensionality (reflecting less information redundancy) during task states, suggesting that decreased correlations increased information coding capacity. We further found in both spiking and neural mass computational models that task-evoked activity increased the stability around a stable attractor, globally quenching neural variability and correlations. Together, our results provide an integrative mechanistic account that encompasses measures of large-scale neural activity, variability, and correlations during resting and task states.
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Affiliation(s)
- Takuya Ito
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
- Behavioral and Neural Sciences PhD Program, Rutgers University, Newark, New Jersey, United States of America
| | - Scott L. Brincat
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Markus Siegel
- Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
- Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- MEG Center, University of Tübingen, Tübingen, Germany
| | - Ravi D. Mill
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
| | - Biyu J. He
- Neuroscience Institute, New York University, New York, New York, United States of America
- Departments of Neurology, Neuroscience and Physiology, and Radiology, New York University, New York, New York, United States of America
| | - Earl K. Miller
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Horacio G. Rotstein
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
- Federated Department of Biological Sciences, Rutgers University, Newark, New Jersey, United States of America
- Institute for Brain and Neuroscience Research, New Jersey Institute of Technology, Newark, New Jersey, United States of America
| | - Michael W. Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
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6
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Berry MJ, Tkačik G. Clustering of Neural Activity: A Design Principle for Population Codes. Front Comput Neurosci 2020; 14:20. [PMID: 32231528 PMCID: PMC7082423 DOI: 10.3389/fncom.2020.00020] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Accepted: 02/18/2020] [Indexed: 11/24/2022] Open
Abstract
We propose that correlations among neurons are generically strong enough to organize neural activity patterns into a discrete set of clusters, which can each be viewed as a population codeword. Our reasoning starts with the analysis of retinal ganglion cell data using maximum entropy models, showing that the population is robustly in a frustrated, marginally sub-critical, or glassy, state. This leads to an argument that neural populations in many other brain areas might share this structure. Next, we use latent variable models to show that this glassy state possesses well-defined clusters of neural activity. Clusters have three appealing properties: (i) clusters exhibit error correction, i.e., they are reproducibly elicited by the same stimulus despite variability at the level of constituent neurons; (ii) clusters encode qualitatively different visual features than their constituent neurons; and (iii) clusters can be learned by downstream neural circuits in an unsupervised fashion. We hypothesize that these properties give rise to a "learnable" neural code which the cortical hierarchy uses to extract increasingly complex features without supervision or reinforcement.
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Affiliation(s)
- Michael J. Berry
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - Gašper Tkačik
- Institute of Science and Technology Austria, Klosterneuburg, Austria
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7
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Pruszynski JA, Zylberberg J. The language of the brain: real-world neural population codes. Curr Opin Neurobiol 2019; 58:30-36. [PMID: 31326721 DOI: 10.1016/j.conb.2019.06.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2018] [Accepted: 06/22/2019] [Indexed: 11/29/2022]
Affiliation(s)
- J Andrew Pruszynski
- Department of Physiology and Pharmacology, Western University, London, ON, Canada; Department of Psychology, Western University, London, ON, Canada; Robarts Research Institute, London, ON, Canada
| | - Joel Zylberberg
- Center for Vision Research, York University, Toronto, ON, Canada; Department of Physics and Astronomy, York University, Toronto, ON, Canada; Canadian Institute for Advanced Research, Toronto, ON, Canada.
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8
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Activity Correlations between Direction-Selective Retinal Ganglion Cells Synergistically Enhance Motion Decoding from Complex Visual Scenes. Neuron 2019; 101:963-976.e7. [PMID: 30709656 PMCID: PMC6424814 DOI: 10.1016/j.neuron.2019.01.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 11/15/2018] [Accepted: 12/31/2018] [Indexed: 11/26/2022]
Abstract
Neurons in sensory systems are often tuned to particular stimulus features. During complex naturalistic stimulation, however, multiple features may simultaneously affect neuronal responses, which complicates the readout of individual features. To investigate feature representation under complex stimulation, we studied how direction-selective ganglion cells in salamander retina respond to texture motion where direction, velocity, and spatial pattern inside the receptive field continuously change. We found that the cells preserve their direction preference under this stimulation, yet their direction encoding becomes ambiguous due to simultaneous activation by luminance changes. The ambiguities can be resolved by considering populations of direction-selective cells with different preferred directions. This gives rise to synergistic motion decoding, yielding more information from the population than the summed information from single-cell responses. Strong positive response correlations between cells with different preferred directions amplify this synergy. Our results show how correlated population activity can enhance feature extraction in complex visual scenes. Direction-selective ganglion cells respond to motion as well as luminance changes This obscures the readout of direction from single cells under complex texture motion Population decoding improves direction readout supralinearly over individual cells Strong spike correlations further enhance readout through increased synergy
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9
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Berry Ii MJ, Lebois F, Ziskind A, da Silveira RA. Functional Diversity in the Retina Improves the Population Code. Neural Comput 2018; 31:270-311. [PMID: 30576618 DOI: 10.1162/neco_a_01158] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Within a given brain region, individual neurons exhibit a wide variety of different feature selectivities. Here, we investigated the impact of this extensive functional diversity on the population neural code. Our approach was to build optimal decoders to discriminate among stimuli using the spiking output of a real, measured neural population and compare its performance against a matched, homogeneous neural population with the same number of cells and spikes. Analyzing large populations of retinal ganglion cells, we found that the real, heterogeneous population can yield a discrimination error lower than the homogeneous population by several orders of magnitude and consequently can encode much more visual information. This effect increases with population size and with graded degrees of heterogeneity. We complemented these results with an analysis of coding based on the Chernoff distance, as well as derivations of inequalities on coding in certain limits, from which we can conclude that the beneficial effect of heterogeneity occurs over a broad set of conditions. Together, our results indicate that the presence of functional diversity in neural populations can enhance their coding fidelity appreciably. A noteworthy outcome of our study is that this effect can be extremely strong and should be taken into account when investigating design principles for neural circuits.
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Affiliation(s)
- Michael J Berry Ii
- Princeton Neuroscience Institute and Department of Molecular Biology, Princeton University, Princeton, NJ 08544, U.S.A.
| | - Felix Lebois
- Department of Physics, Ecole Normale Supérieure, 75005 Paris, France
| | - Avi Ziskind
- Department of Physics, Princeton University, Princeton, NJ 08544, U.S.A.
| | - Rava Azeredo da Silveira
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, U.S.A.; Department of Physics, Ecole Normale Supérieure, 75005 Paris; Laboratoire de Physique Statistique, Ecole Normale Supérieure, PSL Research University, 75231 Paris; Université Paris Diderot Sorbonne Paris Cité, 75031 Paris; Sorbonne Universités UPMC Université Paris 6, 75005 Paris, France; CNRS
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10
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Abstract
Understanding how cognitive processes affect the responses of sensory neurons may clarify the relationship between neuronal population activity and behavior. However, tools for analyzing neuronal activity have not kept up with technological advances in recording from large neuronal populations. Here, we describe prevalent hypotheses of how cognitive processes affect sensory neurons, driven largely by a model based on the activity of single neurons or pools of neurons as the units of computation. We then use simple simulations to expand this model to a new conceptual framework that focuses on subspaces of population activity as the relevant units of computation, uses comparisons between brain areas or to behavior to guide analyses of these subspaces, and suggests that population activity is optimized to decode the large variety of stimuli and tasks that animals encounter in natural behavior. This framework provides new ways of understanding the ever-growing quantity of recorded population activity data.
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Affiliation(s)
- Douglas A Ruff
- Department of Neuroscience and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA;
| | - Amy M Ni
- Department of Neuroscience and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA;
| | - Marlene R Cohen
- Department of Neuroscience and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA;
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11
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Barreiro AK, Ly C. Investigating the Correlation-Firing Rate Relationship in Heterogeneous Recurrent Networks. JOURNAL OF MATHEMATICAL NEUROSCIENCE 2018; 8:8. [PMID: 29872932 PMCID: PMC5989010 DOI: 10.1186/s13408-018-0063-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Accepted: 05/21/2018] [Indexed: 05/13/2023]
Abstract
The structure of spiking activity in cortical networks has important implications for how the brain ultimately codes sensory signals. However, our understanding of how network and intrinsic cellular mechanisms affect spiking is still incomplete. In particular, whether cell pairs in a neural network show a positive (or no) relationship between pairwise spike count correlation and average firing rate is generally unknown. This relationship is important because it has been observed experimentally in some sensory systems, and it can enhance information in a common population code. Here we extend our prior work in developing mathematical tools to succinctly characterize the correlation and firing rate relationship in heterogeneous coupled networks. We find that very modest changes in how heterogeneous networks occupy parameter space can dramatically alter the correlation-firing rate relationship.
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Affiliation(s)
| | - Cheng Ly
- Department of Statistical Science and Operations Research, Virginia Commonwealth University, Richmond, USA
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12
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Calderini M, Zhang S, Berberian N, Thivierge JP. Optimal Readout of Correlated Neural Activity in a Decision-Making Circuit. Neural Comput 2018; 30:1573-1611. [PMID: 29652584 DOI: 10.1162/neco_a_01083] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
The neural correlates of decision making have been extensively studied with tasks involving a choice between two alternatives that is guided by visual cues. While a large body of work argues for a role of the lateral intraparietal (LIP) region of cortex in these tasks, this role may be confounded by the interaction between LIP and other regions, including medial temporal (MT) cortex. Here, we describe a simplified linear model of decision making that is adapted to two tasks: a motion discrimination and a categorization task. We show that the distinct contribution of MT and LIP may indeed be confounded in these tasks. In particular, we argue that the motion discrimination task relies on a straightforward visuomotor mapping, which leads to redundant information between MT and LIP. The categorization task requires a more complex mapping between visual information and decision behavior, and therefore does not lead to redundancy between MT and LIP. Going further, the model predicts that noise correlations within LIP should be greater in the categorization compared to the motion discrimination task due to the presence of shared inputs from MT. The impact of these correlations on task performance is examined by analytically deriving error estimates of an optimal linear readout for shared and unique inputs. Taken together, results clarify the contribution of MT and LIP to decision making and help characterize the role of noise correlations in these regions.
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Affiliation(s)
- Matias Calderini
- Center for Neural Dynamics and School of Psychology, University of Ottawa, Ontario K1N 6N5, Canada
| | - Sophie Zhang
- Center for Neural Dynamics and School of Psychology, University of Ottawa, Ontario K1N 6N5, Canada
| | - Nareg Berberian
- Center for Neural Dynamics and School of Psychology, University of Ottawa, Ontario K1N 6N5, Canada
| | - Jean-Philippe Thivierge
- Center for Neural Dynamics and School of Psychology, University of Ottawa, Ontario K1N 6N5, Canada
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13
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Pernice V, da Silveira RA. Interpretation of correlated neural variability from models of feed-forward and recurrent circuits. PLoS Comput Biol 2018; 14:e1005979. [PMID: 29408930 PMCID: PMC5833435 DOI: 10.1371/journal.pcbi.1005979] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Revised: 03/01/2018] [Accepted: 01/10/2018] [Indexed: 11/18/2022] Open
Abstract
Neural populations respond to the repeated presentations of a sensory stimulus with correlated variability. These correlations have been studied in detail, with respect to their mechanistic origin, as well as their influence on stimulus discrimination and on the performance of population codes. A number of theoretical studies have endeavored to link network architecture to the nature of the correlations in neural activity. Here, we contribute to this effort: in models of circuits of stochastic neurons, we elucidate the implications of various network architectures—recurrent connections, shared feed-forward projections, and shared gain fluctuations—on the stimulus dependence in correlations. Specifically, we derive mathematical relations that specify the dependence of population-averaged covariances on firing rates, for different network architectures. In turn, these relations can be used to analyze data on population activity. We examine recordings from neural populations in mouse auditory cortex. We find that a recurrent network model with random effective connections captures the observed statistics. Furthermore, using our circuit model, we investigate the relation between network parameters, correlations, and how well different stimuli can be discriminated from one another based on the population activity. As such, our approach allows us to relate properties of the neural circuit to information processing. The response of neurons to a stimulus is variable across trials. A natural solution for reliable coding in the face of noise is the averaging across a neural population. The nature of this averaging depends on the structure of noise correlations in the neural population. In turn, the correlation structure depends on the way noise and correlations are generated in neural circuits. It is in general difficult to identify the origin of correlations from the observed population activity alone. In this article, we explore different theoretical scenarios of the way in which correlations can be generated, and we relate these to the architecture of feed-forward and recurrent neural circuits. Analyzing population recordings of the activity in mouse auditory cortex in response to sound stimuli, we find that population statistics are consistent with those generated in a recurrent network model. Using this model, we can then quantify the effects of network properties on average population responses, noise correlations, and the representation of sensory information.
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Affiliation(s)
- Volker Pernice
- Department of Physics, Ecole Normale Supérieure, Paris, France
- Laboratoire de Physique Statistique, Ecole Normale Supérieure, PSL Research University; Université Paris Diderot Sorbonne Paris-Cité, Sorbonne Universités UPMC Univ Paris 06; CNRS, Paris, France
| | - Rava Azeredo da Silveira
- Department of Physics, Ecole Normale Supérieure, Paris, France
- Laboratoire de Physique Statistique, Ecole Normale Supérieure, PSL Research University; Université Paris Diderot Sorbonne Paris-Cité, Sorbonne Universités UPMC Univ Paris 06; CNRS, Paris, France
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America
- * E-mail:
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14
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Ioffe ML, Berry MJ. The structured 'low temperature' phase of the retinal population code. PLoS Comput Biol 2017; 13:e1005792. [PMID: 29020014 PMCID: PMC5654267 DOI: 10.1371/journal.pcbi.1005792] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2016] [Revised: 10/23/2017] [Accepted: 09/26/2017] [Indexed: 11/19/2022] Open
Abstract
Recent advances in experimental techniques have allowed the simultaneous recordings of populations of hundreds of neurons, fostering a debate about the nature of the collective structure of population neural activity. Much of this debate has focused on the empirical findings of a phase transition in the parameter space of maximum entropy models describing the measured neural probability distributions, interpreting this phase transition to indicate a critical tuning of the neural code. Here, we instead focus on the possibility that this is a first-order phase transition which provides evidence that the real neural population is in a 'structured', collective state. We show that this collective state is robust to changes in stimulus ensemble and adaptive state. We find that the pattern of pairwise correlations between neurons has a strength that is well within the strongly correlated regime and does not require fine tuning, suggesting that this state is generic for populations of 100+ neurons. We find a clear correspondence between the emergence of a phase transition, and the emergence of attractor-like structure in the inferred energy landscape. A collective state in the neural population, in which neural activity patterns naturally form clusters, provides a consistent interpretation for our results.
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Affiliation(s)
- Mark L. Ioffe
- Department of Physics, Princeton University, Princeton, New Jersey, United States of America
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America
- * E-mail:
| | - Michael J. Berry
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America
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15
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Bejjanki VR, da Silveira RA, Cohen JD, Turk-Browne NB. Noise correlations in the human brain and their impact on pattern classification. PLoS Comput Biol 2017; 13:e1005674. [PMID: 28841641 PMCID: PMC5589258 DOI: 10.1371/journal.pcbi.1005674] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2015] [Revised: 09/07/2017] [Accepted: 07/05/2017] [Indexed: 11/27/2022] Open
Abstract
Multivariate decoding methods, such as multivoxel pattern analysis (MVPA), are highly effective at extracting information from brain imaging data. Yet, the precise nature of the information that MVPA draws upon remains controversial. Most current theories emphasize the enhanced sensitivity imparted by aggregating across voxels that have mixed and weak selectivity. However, beyond the selectivity of individual voxels, neural variability is correlated across voxels, and such noise correlations may contribute importantly to accurate decoding. Indeed, a recent computational theory proposed that noise correlations enhance multivariate decoding from heterogeneous neural populations. Here we extend this theory from the scale of neurons to functional magnetic resonance imaging (fMRI) and show that noise correlations between heterogeneous populations of voxels (i.e., voxels selective for different stimulus variables) contribute to the success of MVPA. Specifically, decoding performance is enhanced when voxels with high vs. low noise correlations (measured during rest or in the background of the task) are selected during classifier training. Conversely, voxels that are strongly selective for one class in a GLM or that receive high classification weights in MVPA tend to exhibit high noise correlations with voxels selective for the other class being discriminated against. Furthermore, we use simulations to show that this is a general property of fMRI data and that selectivity and noise correlations can have distinguishable influences on decoding. Taken together, our findings demonstrate that if there is signal in the data, the resulting above-chance classification accuracy is modulated by the magnitude of noise correlations. A central challenge in cognitive neuroscience is decoding mental representations from patterns of brain activity. With functional magnetic resonance imaging (fMRI), multivariate decoding methods like multivoxel pattern analysis (MVPA) have produced numerous discoveries about the brain. However, what information these methods draw upon remains the subject of debate. Typically, each voxel is thought to contribute information through its selectivity (i.e., how differently it responds to the classes being decoded), with improved sensitivity reflecting the aggregation of selectivity across voxels. We show that this interpretation downplays an important factor: MVPA is also highly attuned to noise correlations between voxels with opposite selectivity. Across several analyses of an fMRI dataset, we demonstrate a positive relationship between the magnitude of noise correlations and multivariate decoding performance. Indeed, voxels more selective for one class, or heavily weighted in MVPA, tend to be more strongly correlated with voxels selective for the opposite class. Furthermore, using a model to simulate different levels of selectivity and noise correlations, we find that the benefit of noise correlations for decoding is a general property of fMRI data. These findings help elucidate the computational underpinnings of multivariate decoding in cognitive neuroscience and provide insight into the nature of neural representations.
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Affiliation(s)
- Vikranth R. Bejjanki
- Department of Psychology, Princeton University, Princeton, NJ, United States of America
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States of America
- Department of Psychology, Hamilton College, Clinton, NY, United States of America
- * E-mail:
| | - Rava Azeredo da Silveira
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States of America
- Department of Physics, Ecole Normale Supérieure, Paris, France
- Laboratoire de Physique Statistique, École Normale Supérieure, PSL Research University; Université Paris Diderot Sorbonne Paris-Cité; Sorbonne Universités UPMC Univ Paris 06; CNRS, Paris, France
| | - Jonathan D. Cohen
- Department of Psychology, Princeton University, Princeton, NJ, United States of America
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States of America
| | - Nicholas B. Turk-Browne
- Department of Psychology, Princeton University, Princeton, NJ, United States of America
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States of America
- Department of Psychology, Yale University, New Haven, CT, United States of America
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16
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When do correlations increase with firing rates in recurrent networks? PLoS Comput Biol 2017; 13:e1005506. [PMID: 28448499 PMCID: PMC5426798 DOI: 10.1371/journal.pcbi.1005506] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Revised: 05/11/2017] [Accepted: 04/07/2017] [Indexed: 02/04/2023] Open
Abstract
A central question in neuroscience is to understand how noisy firing patterns are used to transmit information. Because neural spiking is noisy, spiking patterns are often quantified via pairwise correlations, or the probability that two cells will spike coincidentally, above and beyond their baseline firing rate. One observation frequently made in experiments, is that correlations can increase systematically with firing rate. Theoretical studies have determined that stimulus-dependent correlations that increase with firing rate can have beneficial effects on information coding; however, we still have an incomplete understanding of what circuit mechanisms do, or do not, produce this correlation-firing rate relationship. Here, we studied the relationship between pairwise correlations and firing rates in recurrently coupled excitatory-inhibitory spiking networks with conductance-based synapses. We found that with stronger excitatory coupling, a positive relationship emerged between pairwise correlations and firing rates. To explain these findings, we used linear response theory to predict the full correlation matrix and to decompose correlations in terms of graph motifs. We then used this decomposition to explain why covariation of correlations with firing rate—a relationship previously explained in feedforward networks driven by correlated input—emerges in some recurrent networks but not in others. Furthermore, when correlations covary with firing rate, this relationship is reflected in low-rank structure in the correlation matrix. A central question in neuroscience is to understand how noisy firing patterns are used to transmit information. We quantify spiking patterns by using pairwise correlations, or the probability that two cells will spike coincidentally, above and beyond their baseline firing rate. One observation frequently made in experiments is that correlations can increase systematically with firing rate. Recent studies of a type of output cell in mouse retina found this relationship; furthermore, they determined that the increase of correlation with firing rate helped the cells encode information, provided the correlations were stimulus-dependent. Several theoretical studies have explored this basic structure, and found that it is generally beneficial to modulate correlations in this way. However—aside from mouse retinal cells referenced here—we do not yet have many examples of real neural circuits that show this correlation-firing rate pattern, so we do not know what common features (or mechanisms) might occur between them. In this study, we address this question via a computational model. We set up a computational model with features representative of a generic cortical network, to see whether correlations would increase with firing rate. To produce different firing patterns, we varied excitatory coupling. We found that with stronger excitatory coupling, there was a positive relationship between pairwise correlations and firing rates. We used a network linear response theory to show why correlations could increase with firing rates in some networks, but not in others; this could be explained by how cells responded to fluctuations in inhibitory conductances.
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17
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Zylberberg J, Pouget A, Latham PE, Shea-Brown E. Robust information propagation through noisy neural circuits. PLoS Comput Biol 2017; 13:e1005497. [PMID: 28419098 PMCID: PMC5413111 DOI: 10.1371/journal.pcbi.1005497] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Revised: 05/02/2017] [Accepted: 04/03/2017] [Indexed: 12/31/2022] Open
Abstract
Sensory neurons give highly variable responses to stimulation, which can limit the amount of stimulus information available to downstream circuits. Much work has investigated the factors that affect the amount of information encoded in these population responses, leading to insights about the role of covariability among neurons, tuning curve shape, etc. However, the informativeness of neural responses is not the only relevant feature of population codes; of potentially equal importance is how robustly that information propagates to downstream structures. For instance, to quantify the retina’s performance, one must consider not only the informativeness of the optic nerve responses, but also the amount of information that survives the spike-generating nonlinearity and noise corruption in the next stage of processing, the lateral geniculate nucleus. Our study identifies the set of covariance structures for the upstream cells that optimize the ability of information to propagate through noisy, nonlinear circuits. Within this optimal family are covariances with “differential correlations”, which are known to reduce the information encoded in neural population activities. Thus, covariance structures that maximize information in neural population codes, and those that maximize the ability of this information to propagate, can be very different. Moreover, redundancy is neither necessary nor sufficient to make population codes robust against corruption by noise: redundant codes can be very fragile, and synergistic codes can—in some cases—optimize robustness against noise. Information about the outside world, which originates in sensory neurons, propagates through multiple stages of processing before reaching the neural structures that control behavior. While much work in neuroscience has investigated the factors that affect the amount of information contained in peripheral sensory areas, very little work has asked how much of that information makes it through subsequent processing stages. That’s the focus of this paper, and it’s an important issue because information that fails to propagate cannot be used to affect decision-making. We find a tradeoff between information content and information transmission: neural codes which contain a large amount of information can transmit that information poorly to subsequent processing stages. Thus, the problem of robust information propagation—which has largely been overlooked in previous research—may be critical for determining how our sensory organs communicate with our brains. We identify the conditions under which information propagates well—or poorly—through multiple stages of neural processing.
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Affiliation(s)
- Joel Zylberberg
- Department of Physiology and Biophysics, Center for Neuroscience, and Computational Bioscience Program, University of Colorado School of Medicine, Aurora, Colorado, United States of America
- Department of Applied Mathematics, University of Colorado, Boulder, Colorado, United States of America
- Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America
- Learning in Machines and Brains Program, Canadian Institute For Advanced Research, Toronto, Ontario, Canada
- * E-mail:
| | - Alexandre Pouget
- Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
- Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom
| | - Peter E. Latham
- Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom
| | - Eric Shea-Brown
- Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America
- Department of Physiology and Biophysics, Program in Neuroscience, University of Washington Institute for Neuroengineering, and Center for Sensorimotor Neural Engineering, University of Washington, Seattle, Washington, United States of America
- Allen Institute for Brain Science, Seattle, Washington, United States of America
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18
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Error-Robust Modes of the Retinal Population Code. PLoS Comput Biol 2016; 12:e1005148. [PMID: 27855154 PMCID: PMC5113862 DOI: 10.1371/journal.pcbi.1005148] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2015] [Accepted: 09/15/2016] [Indexed: 01/23/2023] Open
Abstract
Across the nervous system, certain population spiking patterns are observed far more frequently than others. A hypothesis about this structure is that these collective activity patterns function as population codewords–collective modes–carrying information distinct from that of any single cell. We investigate this phenomenon in recordings of ∼150 retinal ganglion cells, the retina’s output. We develop a novel statistical model that decomposes the population response into modes; it predicts the distribution of spiking activity in the ganglion cell population with high accuracy. We found that the modes represent localized features of the visual stimulus that are distinct from the features represented by single neurons. Modes form clusters of activity states that are readily discriminated from one another. When we repeated the same visual stimulus, we found that the same mode was robustly elicited. These results suggest that retinal ganglion cells’ collective signaling is endowed with a form of error-correcting code–a principle that may hold in brain areas beyond retina. Neurons in most parts of the nervous system represent and process information in a collective fashion, yet the nature of this collective code is poorly understood. An important constraint placed on any such collective processing comes from the fact that individual neurons’ signaling is prone to corruption by noise. The information theory and engineering literatures have studied error-correcting codes that allow individual noise-prone coding units to “check” each other, forming an overall representation that is robust to errors. In this paper, we have analyzed the population code of one of the best-studied neural systems, the retina, and found that it is structured in a manner analogous to error-correcting schemes. Indeed, we found that the complex activity patterns over ~150 retinal ganglion cells, the output neurons of the retina, could be mapped onto collective code words, and that these code words represented precise visual information while suppressing noise. In order to analyze this coding scheme, we introduced a novel quantitative model of the retinal output that predicted neural activity patterns more accurately than existing state-of-the-art approaches.
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19
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Singh V, Tchernookov M, Nemenman I. Effects of receptor correlations on molecular information transmission. Phys Rev E 2016; 94:022425. [PMID: 27627350 DOI: 10.1103/physreve.94.022425] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Indexed: 11/07/2022]
Abstract
Cells measure concentrations of external ligands by capturing ligand molecules with cell surface receptors. The numbers of molecules captured by different receptors co-vary because they depend on the same extrinsic ligand fluctuations. However, these numbers also counter-vary due to the intrinsic stochasticity of chemical processes because a single molecule randomly captured by a receptor cannot be captured by another. Such structure of receptor correlations is generally believed to lead to an increase in information about the external signal compared to the case of independent receptors. We analyze a solvable model of two molecular receptors and show that, contrary to this widespread expectation, the correlations have a small and negative effect on the information about the ligand concentration. Further, we show that measurements that average over multiple receptors are almost as informative as those that track the states of every individual one.
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Affiliation(s)
- Vijay Singh
- Department of Physics, Emory University, Atlanta, Georgia 30322, USA.,Computational Neuroscience Initiative, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Martin Tchernookov
- Department of Physics, Emory University, Atlanta, Georgia 30322, USA.,Department of Physics, Lamar University, Beaumont, Texas 77710, USA
| | - Ilya Nemenman
- Department of Physics, Emory University, Atlanta, Georgia 30322, USA.,Department of Biology, Emory University, Atlanta, Georgia 30322, USA
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20
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Structures of Neural Correlation and How They Favor Coding. Neuron 2016; 89:409-22. [PMID: 26796692 DOI: 10.1016/j.neuron.2015.12.037] [Citation(s) in RCA: 97] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2014] [Revised: 08/15/2015] [Accepted: 12/21/2015] [Indexed: 01/11/2023]
Abstract
The neural representation of information suffers from "noise"-the trial-to-trial variability in the response of neurons. The impact of correlated noise upon population coding has been debated, but a direct connection between theory and experiment remains tenuous. Here, we substantiate this connection and propose a refined theoretical picture. Using simultaneous recordings from a population of direction-selective retinal ganglion cells, we demonstrate that coding benefits from noise correlations. The effect is appreciable already in small populations, yet it is a collective phenomenon. Furthermore, the stimulus-dependent structure of correlation is key. We develop simple functional models that capture the stimulus-dependent statistics. We then use them to quantify the performance of population coding, which depends upon interplays of feature sensitivities and noise correlations in the population. Because favorable structures of correlation emerge robustly in circuits with noisy, nonlinear elements, they will arise and benefit coding beyond the confines of retina.
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21
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Zylberberg J, Cafaro J, Turner MH, Shea-Brown E, Rieke F. Direction-Selective Circuits Shape Noise to Ensure a Precise Population Code. Neuron 2016; 89:369-383. [PMID: 26796691 DOI: 10.1016/j.neuron.2015.11.019] [Citation(s) in RCA: 84] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2014] [Revised: 08/15/2015] [Accepted: 10/26/2015] [Indexed: 12/29/2022]
Abstract
Neural responses are noisy, and circuit structure can correlate this noise across neurons. Theoretical studies show that noise correlations can have diverse effects on population coding, but these studies rarely explore stimulus dependence of noise correlations. Here, we show that noise correlations in responses of ON-OFF direction-selective retinal ganglion cells are strongly stimulus dependent, and we uncover the circuit mechanisms producing this stimulus dependence. A population model based on these mechanistic studies shows that stimulus-dependent noise correlations improve the encoding of motion direction 2-fold compared to independent noise. This work demonstrates a mechanism by which a neural circuit effectively shapes its signal and noise in concert, minimizing corruption of signal by noise. Finally, we generalize our findings beyond direction coding in the retina and show that stimulus-dependent correlations will generally enhance information coding in populations of diversely tuned neurons.
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Affiliation(s)
- Joel Zylberberg
- Department of Applied Mathematics, University of Washington, Seattle, Washington 98195, USA
| | - Jon Cafaro
- Department of Physiology and Biophysics, University of Washington, Seattle, Washington 98195, USA.,Department of Neurobiology, Duke University, Durham, North Carolina 27708, USA
| | - Maxwell H Turner
- Department of Physiology and Biophysics, University of Washington, Seattle, Washington 98195, USA
| | - Eric Shea-Brown
- Department of Applied Mathematics, University of Washington, Seattle, Washington 98195, USA.,Department of Physiology and Biophysics, University of Washington, Seattle, Washington 98195, USA
| | - Fred Rieke
- Department of Physiology and Biophysics, University of Washington, Seattle, Washington 98195, USA.,Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195, USA
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22
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Abstract
Brain function involves the activity of neuronal populations. Much recent effort has been devoted to measuring the activity of neuronal populations in different parts of the brain under various experimental conditions. Population activity patterns contain rich structure, yet many studies have focused on measuring pairwise relationships between members of a larger population-termed noise correlations. Here we review recent progress in understanding how these correlations affect population information, how information should be quantified, and what mechanisms may give rise to correlations. As population coding theory has improved, it has made clear that some forms of correlation are more important for information than others. We argue that this is a critical lesson for those interested in neuronal population responses more generally: Descriptions of population responses should be motivated by and linked to well-specified function. Within this context, we offer suggestions of where current theoretical frameworks fall short.
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Affiliation(s)
- Adam Kohn
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York 10461; .,Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, New York 10461
| | - Ruben Coen-Cagli
- Department of Basic Neuroscience, University of Geneva, CH-1211 Geneva, Switzerland; ,
| | - Ingmar Kanitscheider
- Department of Basic Neuroscience, University of Geneva, CH-1211 Geneva, Switzerland; , .,Center of Learning and Memory, The University of Texas at Austin, Austin, Texas 78712; .,Department of Neuroscience, The University of Texas at Austin, Austin, Texas 78712
| | - Alexandre Pouget
- Department of Basic Neuroscience, University of Geneva, CH-1211 Geneva, Switzerland; , .,Department of Brain and Cognitive Sciences, University of Rochester, Rochester, New York 14627.,Gatsby Computational Neuroscience Unit, University College London, W1T 4JG London, United Kingdom
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23
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Lyzwa D, Herrmann JM, Wörgötter F. Natural Vocalizations in the Mammalian Inferior Colliculus are Broadly Encoded by a Small Number of Independent Multi-Units. Front Neural Circuits 2016; 9:91. [PMID: 26869890 PMCID: PMC4740783 DOI: 10.3389/fncir.2015.00091] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2015] [Accepted: 12/28/2015] [Indexed: 11/18/2022] Open
Abstract
How complex natural sounds are represented by the main converging center of the auditory midbrain, the central inferior colliculus, is an open question. We applied neural discrimination to determine the variation of detailed encoding of individual vocalizations across the best frequency gradient of the central inferior colliculus. The analysis was based on collective responses from several neurons. These multi-unit spike trains were recorded from guinea pigs exposed to a spectrotemporally rich set of eleven species-specific vocalizations. Spike trains of disparate units from the same recording were combined in order to investigate whether groups of multi-unit clusters represent the whole set of vocalizations more reliably than only one unit, and whether temporal response correlations between them facilitate an unambiguous neural representation of the vocalizations. We found a spatial distribution of the capability to accurately encode groups of vocalizations across the best frequency gradient. Different vocalizations are optimally discriminated at different locations of the best frequency gradient. Furthermore, groups of a few multi-unit clusters yield improved discrimination over only one multi-unit cluster between all tested vocalizations. However, temporal response correlations between units do not yield better discrimination. Our study is based on a large set of units of simultaneously recorded responses from several guinea pigs and electrode insertion positions. Our findings suggest a broadly distributed code for behaviorally relevant vocalizations in the mammalian inferior colliculus. Responses from a few non-interacting units are sufficient to faithfully represent the whole set of studied vocalizations with diverse spectrotemporal properties.
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Affiliation(s)
- Dominika Lyzwa
- Max Planck Institute for Dynamics and Self-OrganizationGöttingen, Germany
- Institute for Nonlinear Dynamics, Physics Department, Georg-August-UniversityGöttingen, Germany
- Bernstein Focus NeurotechnologyGöttingen, Germany
| | - J. Michael Herrmann
- Bernstein Focus NeurotechnologyGöttingen, Germany
- Institute of Perception, Action and Behavior, School of Informatics, University of EdinburghEdinburgh, UK
| | - Florentin Wörgötter
- Bernstein Focus NeurotechnologyGöttingen, Germany
- Institute for Physics - Biophysics, Georg-August-UniversityGöttingen, Germany
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24
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Zylberberg J, Hyde RA, Strowbridge BW. Dynamics of robust pattern separability in the hippocampal dentate gyrus. Hippocampus 2015; 26:623-32. [PMID: 26482936 DOI: 10.1002/hipo.22546] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Revised: 10/05/2015] [Accepted: 10/09/2015] [Indexed: 11/05/2022]
Abstract
The dentate gyrus (DG) is thought to perform pattern separation on inputs received from the entorhinal cortex, such that the DG forms distinct representations of different input patterns. Neuronal responses, however, are known to be variable, and that variability has the potential to confuse the representations of different inputs, thereby hindering the pattern separation function. This variability can be especially problematic for tissues such as the DG, in which the responses can persist for tens of seconds following stimulation: the long response duration allows for variability from many different sources to accumulate. To understand how the DG can robustly encode different input patterns, we investigated a recently developed in vitro hippocampal DG preparation that generates persistent responses to transient electrical stimulation. For 10-20 s after stimulation, the responses are indicative of the pattern of stimulation that was applied, even though the responses exhibit significant trial-to-trial variability. Analyzing the dynamical trajectories of the evoked responses, we found that, following stimulation, the neural responses follow distinct paths through the space of possible neural activations, with a different path associated with each stimulation pattern. The neural responses' trial-to-trial variability shifts the responses along these paths rather than between them, maintaining the separability of the input patterns. Manipulations that redistributed the variability more isotropically over the space of possible neural activations impeded the pattern separation function. Consequently, we conclude that the confinement of neuronal variability to these one-dimensional paths mitigates the impacts of variability on pattern encoding and, thus, may be an important aspect of the DG's ability to robustly encode input patterns.
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Affiliation(s)
- Joel Zylberberg
- Department of Applied Mathematics, University of Washington, Seattle, Washington.,Department of Physiology and Biophysics, University of Colorado School of Medicine, Aurora, Colorado
| | - Robert A Hyde
- Department of Neurosciences, Case Western Reserve University, Cleveland, Ohio
| | - Ben W Strowbridge
- Department of Neurosciences, Case Western Reserve University, Cleveland, Ohio
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25
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Affiliation(s)
- Gideon Rothschild
- Department of Physiology and Center for Integrative Neuroscience, University of California, San Francisco, California 94158;
| | - Adi Mizrahi
- Department of Neurobiology, Institute of Life Sciences, The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus, 91904 Givat Ram Jerusalem, Israel;
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26
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Cayco-Gajic NA, Zylberberg J, Shea-Brown E. Triplet correlations among similarly tuned cells impact population coding. Front Comput Neurosci 2015; 9:57. [PMID: 26042024 PMCID: PMC4435073 DOI: 10.3389/fncom.2015.00057] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2014] [Accepted: 04/29/2015] [Indexed: 11/18/2022] Open
Abstract
Which statistical features of spiking activity matter for how stimuli are encoded in neural populations? A vast body of work has explored how firing rates in individual cells and correlations in the spikes of cell pairs impact coding. Recent experiments have shown evidence for the existence of higher-order spiking correlations, which describe simultaneous firing in triplets and larger ensembles of cells; however, little is known about their impact on encoded stimulus information. Here, we take a first step toward closing this gap. We vary triplet correlations in small (approximately 10 cell) neural populations while keeping single cell and pairwise statistics fixed at typically reported values. This connection with empirically observed lower-order statistics is important, as it places strong constraints on the level of triplet correlations that can occur. For each value of triplet correlations, we estimate the performance of the neural population on a two-stimulus discrimination task. We find that the allowed changes in the level of triplet correlations can significantly enhance coding, in particular if triplet correlations differ for the two stimuli. In this scenario, triplet correlations must be included in order to accurately quantify the functionality of neural populations. When both stimuli elicit similar triplet correlations, however, pairwise models provide relatively accurate descriptions of coding accuracy. We explain our findings geometrically via the skew that triplet correlations induce in population-wide distributions of neural responses. Finally, we calculate how many samples are necessary to accurately measure spiking correlations of this type, providing an estimate of the necessary recording times in future experiments.
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Affiliation(s)
| | - Joel Zylberberg
- Department of Applied Mathematics, University of Washington Seattle, WA, USA
| | - Eric Shea-Brown
- Department of Applied Mathematics, University of Washington Seattle, WA, USA
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27
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Montijn JS, Vinck M, Pennartz CMA. Population coding in mouse visual cortex: response reliability and dissociability of stimulus tuning and noise correlation. Front Comput Neurosci 2014; 8:58. [PMID: 24917812 PMCID: PMC4040453 DOI: 10.3389/fncom.2014.00058] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2014] [Accepted: 05/14/2014] [Indexed: 11/13/2022] Open
Abstract
The primary visual cortex is an excellent model system for investigating how neuronal populations encode information, because of well-documented relationships between stimulus characteristics and neuronal activation patterns. We used two-photon calcium imaging data to relate the performance of different methods for studying population coding (population vectors, template matching, and Bayesian decoding algorithms) to their underlying assumptions. We show that the variability of neuronal responses may hamper the decoding of population activity, and that a normalization to correct for this variability may be of critical importance for correct decoding of population activity. Second, by comparing noise correlations and stimulus tuning we find that these properties have dissociated anatomical correlates, even though noise correlations have been previously hypothesized to reflect common synaptic input. We hypothesize that noise correlations arise from large non-specific increases in spiking activity acting on many weak synapses simultaneously, while neuronal stimulus response properties are dependent on more reliable connections. Finally, this paper provides practical guidelines for further research on population coding and shows that population coding cannot be approximated by a simple summation of inputs, but is heavily influenced by factors such as input reliability and noise correlation structure.
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Affiliation(s)
- Jorrit S Montijn
- Cognitive and Systems Neuroscience, Faculty of Science, Center for Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam Amsterdam, Netherlands
| | - Martin Vinck
- Cognitive and Systems Neuroscience, Faculty of Science, Center for Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam Amsterdam, Netherlands
| | - Cyriel M A Pennartz
- Cognitive and Systems Neuroscience, Faculty of Science, Center for Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam Amsterdam, Netherlands ; Research Priority Program Brain and Cognition, University of Amsterdam Amsterdam, Netherlands
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28
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The sign rule and beyond: boundary effects, flexibility, and noise correlations in neural population codes. PLoS Comput Biol 2014; 10:e1003469. [PMID: 24586128 PMCID: PMC3937411 DOI: 10.1371/journal.pcbi.1003469] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2013] [Accepted: 12/17/2013] [Indexed: 11/24/2022] Open
Abstract
Over repeat presentations of the same stimulus, sensory neurons show variable responses. This “noise” is typically correlated between pairs of cells, and a question with rich history in neuroscience is how these noise correlations impact the population's ability to encode the stimulus. Here, we consider a very general setting for population coding, investigating how information varies as a function of noise correlations, with all other aspects of the problem – neural tuning curves, etc. – held fixed. This work yields unifying insights into the role of noise correlations. These are summarized in the form of theorems, and illustrated with numerical examples involving neurons with diverse tuning curves. Our main contributions are as follows. (1) We generalize previous results to prove a sign rule (SR) — if noise correlations between pairs of neurons have opposite signs vs. their signal correlations, then coding performance will improve compared to the independent case. This holds for three different metrics of coding performance, and for arbitrary tuning curves and levels of heterogeneity. This generality is true for our other results as well. (2) As also pointed out in the literature, the SR does not provide a necessary condition for good coding. We show that a diverse set of correlation structures can improve coding. Many of these violate the SR, as do experimentally observed correlations. There is structure to this diversity: we prove that the optimal correlation structures must lie on boundaries of the possible set of noise correlations. (3) We provide a novel set of necessary and sufficient conditions, under which the coding performance (in the presence of noise) will be as good as it would be if there were no noise present at all. Sensory systems communicate information to the brain — and brain areas communicate between themselves — via the electrical activities of their respective neurons. These activities are “noisy”: repeat presentations of the same stimulus do not yield to identical responses every time. Furthermore, the neurons' responses are not independent: the variability in their responses is typically correlated from cell to cell. How does this change the impact of the noise — for better or for worse? Our goal here is to classify (broadly) the sorts of noise correlations that are either good or bad for enabling populations of neurons to transmit information. This is helpful as there are many possibilities for the noise correlations, and the set of possibilities becomes large for even modestly sized neural populations. We prove mathematically that, for larger populations, there are many highly diverse ways that favorable correlations can occur. These often differ from the noise correlation structures that are typically identified as beneficial for information transmission – those that follow the so-called “sign rule.” Our results help in interpreting some recent data that seems puzzling from the perspective of this rule.
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