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Krishna V Shenoy - One of the best experts on this subject based on the ideXlab platform.
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computation through Neural Population dynamics
Annual Review of Neuroscience, 2020Co-Authors: Saurabh Vyas, Matthew D Golub, David Sussillo, Krishna V ShenoyAbstract:Significant experimental, computational, and theoretical work has identified rich structure within the coordinated activity of interconnected Neural Populations. An emerging challenge now is to uncover the nature of the associated computations, how they are implemented, and what role they play in driving behavior. We term this computation through Neural Population dynamics. If successful, this framework will reveal general motifs of Neural Population activity and quantitatively describe how Neural Population dynamics implement computations necessary for driving goal-directed behavior. Here, we start with a mathematical primer on dynamical systems theory and analytical tools necessary to apply this perspective to experimental data. Next, we highlight some recent discoveries resulting from successful application of dynamical systems. We focus on studies spanning motor control, timing, decision-making, and working memory. Finally, we briefly discuss promising recent lines of investigation and future directions for the computation through Neural Population dynamics framework.
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single neuron firing rate statistics in motor cortex during execution and observation of movement
International Conference of the IEEE Engineering in Medicine and Biology Society, 2018Co-Authors: Xiyuan Jiang, Krishna V ShenoyAbstract:Mirror neurons, which fire during both the execution and observation of movement, are believed to play an important role in motor processing and learning. However, much work still remains to understand the similarities and differences in how these neurons compute in the motor cortex during movement execution and observation. Here, we performed experiments where a monkey both executes and observes a center-out-and-back task within the same experimental session. By recording from putatively the same Neural Population, we were able to analyze and compare single neuron statistics between movement execution and observation. We found that a majority of neurons in the primary motor cortex (M1) and dorsal premotor cortex (PMd) have statistically different firing rate statistics between movement execution and observation. As a result of this difference, we then wondered if neurons during movement observation exhibited a similar characteristic to those during movement execution: changing of preferred directions as a function of movement speed. Interestingly, we found that while observed movement speed is encoded in the Neural Population, it only alters a small proportion of the neuron’s firing rate statistics. These results suggest that Neural Populations in Ml and PMd process information related to movement differently between execution and observation.
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Population dynamics of choice representation in dorsal premotor and primary motor cortex
bioRxiv, 2018Co-Authors: Diogo Peixoto, Krishna V Shenoy, Roozbeh Kiani, Chandramouli Chandrasekara, Stephe I Ryu, William T NewsomeAbstract:Studies in multiple species have revealed the existence of Neural signals that lawfully co-vary with different aspects of the decision-making process, including choice, sensory evidence that supports the choice, and reaction time. These signals, often interpreted as the representation of a decision variable (DV), have been identified in several motor preparation circuits and provide insight about mechanisms underlying the decision-making process. However, single-trial dynamics of this process or its representation at the Neural Population level remain poorly understood. Here, we examine the representation of the DV in simultaneously recorded Neural Populations of dorsal premotor (PMd) and primary motor (M1) cortices of monkeys performing a random dots direction discrimination task with arm movements as the behavioral report. We show that single-trial DVs covary with stimulus difficulty in both areas but are stronger and appear earlier in PMd compared to M1 when the stimulus duration is fixed and predictable. When temporal uncertainty is introduced by making the stimulus duration variable, single-trial DV dynamics are accelerated across the board and the two areas become largely indistinguishable throughout the entire trial. These effects are not trivially explained by the faster emergence of motor kinematic signals in PMd and M1. All key aspects of the data were replicated by a computational model that relies on progressive recruitment of units with stable choice-related modulation of Neural Population activity. In contrast with several recent results in rodents, decision signals in PMd and M1 are not carried by short sequences of activity in non-overlapping groups of neurons but are instead distributed across many neurons, which once recruited, represent the decision stably during individual behavioral epochs of the trial.
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Neural Population dynamics underlying motor learning transfer
Neuron, 2018Co-Authors: Saurabh Vyas, Stephen I Ryu, Sergey D Stavisky, Paul Nuyujukian, Nir Evenchen, Krishna V ShenoyAbstract:Covert motor learning can sometimes transfer to overt behavior. We investigated the Neural mechanism underlying transfer by constructing a two-context paradigm. Subjects performed cursor movements either overtly using arm movements, or covertly via a brain-machine interface that moves the cursor based on motor cortical activity (in lieu of arm movement). These tasks helped evaluate whether and how cortical changes resulting from "covert rehearsal" affect overt performance. We found that covert learning indeed transfers to overt performance and is accompanied by systematic Population-level changes in motor preparatory activity. Current models of motor cortical function ascribe motor preparation to achieving initial conditions favorable for subsequent movement-period Neural dynamics. We found that covert and overt contexts share these initial conditions, and covert rehearsal manipulates them in a manner that persists across context changes, thus facilitating overt motor learning. This transfer learning mechanism might provide new insights into other covert processes like mental rehearsal.
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accurate estimation of Neural Population dynamics without spike sorting
bioRxiv, 2017Co-Authors: Eric Trautmann, Stephen I Ryu, Sergey D Stavisky, Subhaneil Lahiri, Katherine Cora Ames, Matthew T Kaufman, Surya Ganguli, Krishna V ShenoyAbstract:A central goal of systems neuroscience is to relate an organism's Neural activity to behavior. Neural Population analysis often begins by reducing the dimensionality of the data to focus on the patterns most relevant to a given task. A major practical hurdle to data analysis is spike sorting, and this problem is growing rapidly as the number of neurons measured increases. Here, we investigate whether spike sorting is necessary to estimate Neural dynamics. The theory of random projections suggests that we can accurately estimate the geometry of low-dimensional manifolds from a small number of linear projections of the data. We re-analyzed data from three previous studies and found that Neural dynamics and scientific conclusions are quite similar using multi-unit threshold crossings in place of sorted neurons. This finding unlocks existing data for new analyses and informs the design and use of new electrode arrays for laboratory and clinical use.
John P Cunningham - One of the best experts on this subject based on the ideXlab platform.
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deep random splines for point process intensity estimation of Neural Population data
Neural Information Processing Systems, 2019Co-Authors: Gabriel Loaizaganem, Sean M Perkins, Karen E Schroeder, Mark M Churchland, John P CunninghamAbstract:Gaussian processes are the leading class of distributions on random functions, but they suffer from well known issues including difficulty scaling and inflexibility with respect to certain shape constraints (such as nonnegativity). Here we propose Deep Random Splines, a flexible class of random functions obtained by transforming Gaussian noise through a deep Neural network whose output are the parameters of a spline. Unlike Gaussian processes, Deep Random Splines allow us to readily enforce shape constraints while inheriting the richness and tractability of deep generative models. We also present an observational model for point process data which uses Deep Random Splines to model the intensity function of each point process and apply it to Neural Population data to obtain a low-dimensional representation of spiking activity. Inference is performed via a variational autoencoder that uses a novel recurrent encoder architecture that can handle multiple point processes as input. We use a newly collected dataset where a primate completes a pedaling task, and observe better dimensionality reduction with our model than with competing alternatives.
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structure in Neural Population recordings an expected byproduct of simpler phenomena
Nature Neuroscience, 2017Co-Authors: Gamaleldin F Elsayed, John P CunninghamAbstract:Neuroscientists increasingly analyze the joint activity of multineuron recordings to identify Population-level structures believed to be significant and scientifically novel. Claims of significant Population structure support hypotheses in many brain areas. However, these claims require first investigating the possibility that the Population structure in question is an expected byproduct of simpler features known to exist in data. Classically, this critical examination can be either intuited or addressed with conventional controls. However, these approaches fail when considering Population data, raising concerns about the scientific merit of Population-level studies. Here we develop a framework to test the novelty of Population-level findings against simpler features such as correlations across times, neurons and conditions. We apply this framework to test two recent Population findings in prefrontal and motor cortices, providing essential context to those studies. More broadly, the methodologies we introduce provide a general Neural Population control for many Population-level hypotheses.
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single trial dynamics of motor cortex and their applications to brain machine interfaces
Nature Communications, 2015Co-Authors: Paul Nuyujukian, John P Cunningham, Mark M Churchland, Krishna V ShenoyAbstract:Increasing evidence suggests that Neural Population responses have their own internal drive, or dynamics, that describe how the Neural Population evolves through time. An important prediction of Neural dynamical models is that previously observed Neural activity is informative of noisy yet-to-be-observed activity on single-trials, and may thus have a denoising effect. To investigate this prediction, we built and characterized dynamical models of single-trial motor cortical activity. We find these models capture salient dynamical features of the Neural Population and are informative of future Neural activity on single trials. To assess how Neural dynamics may beneficially denoise single-trial Neural activity, we incorporate Neural dynamics into a brain–machine interface (BMI). In online experiments, we find that a Neural dynamical BMI achieves substantially higher performance than its non-dynamical counterpart. These results provide evidence that Neural dynamics beneficially inform the temporal evolution of Neural activity on single trials and may directly impact the performance of BMIs.
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Neural Population dynamics during reaching
Nature, 2012Co-Authors: John P Cunningham, Mark M Churchland, Matthew T Kaufman, Justin D Foster, Paul Nuyujukian, Krishna V ShenoyAbstract:Most theories of motor cortex have assumed that Neural activity represents movement parameters. This view derives from what is known about primary visual cortex, where Neural activity represents patterns of light. Yet it is unclear how well the analogy between motor and visual cortex holds. Single-neuron responses in motor cortex are complex, and there is marked disagreement regarding which movement parameters are represented. A better analogy might be with other motor systems, where a common principle is rhythmic Neural activity. Here we find that motor cortex responses during reaching contain a brief but strong oscillatory component, something quite unexpected for a non-periodic behaviour. Oscillation amplitude and phase followed naturally from the preparatory state, suggesting a mechanistic role for preparatory Neural activity. These results demonstrate an unexpected yet surprisingly simple structure in the Population response. This underlying structure explains many of the confusing features of individual Neural responses. It has long been thought that individual neurons in the motor and premotor cortex are tuned for parameters of movements such as direction. But despite decades of work, the exact nature of the represented parameters is still unclear, as are the mechanisms through which such representations could support the complex movements made in the course of every day life. Here, Churchland et al. propose an alternative theory — that Population dynamics could underlie motor control. They show that reaching movements are associated with oscillatory Population activity in the monkey motor cortex, despite there being no periodic component to this behaviour. The amplitude and phase of the oscillation followed naturally from the preceding state, suggesting a role for preparatory Neural activity in reaching.
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gaussian process factor analysis for low dimensional single trial analysis of Neural Population activity
Neural Information Processing Systems, 2008Co-Authors: John P Cunningham, G Santhanam, Stephen I Ryu, Krishna V Shenoy, Maneesh SahaniAbstract:We consider the problem of extracting smooth, low-dimensional Neural trajectories that summarize the activity recorded simultaneously from tens to hundreds of neurons on individual experimental trials. Current methods for extracting Neural trajectories involve a two-stage process: the data are first "denoised" by smoothing over time, then a static dimensionality reduction technique is applied. We first describe extensions of the two-stage methods that allow the degree of smoothing to be chosen in a principled way, and account for spiking variability that may vary both across neurons and across time. We then present a novel method for extracting Neural trajectories, Gaussian-process factor analysis (GPFA), which unifies the smoothing and dimensionality reduction operations in a common probabilistic framework. We applied these methods to the activity of 61 neurons recorded simultaneously in macaque premotor and motor cortices during reach planning and execution. By adopting a goodness-of-fit metric that measures how well the activity of each neuron can be predicted by all other recorded neurons, we found that GPFA provided a better characterization of the Population activity than the two-stage methods.
Matthew T Kaufman - One of the best experts on this subject based on the ideXlab platform.
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accurate estimation of Neural Population dynamics without spike sorting
Neuron, 2019Co-Authors: Eric Trautmann, Sergey D Stavisky, Subhaneil Lahiri, Katherine Cora Ames, Matthew T Kaufman, Daniel J Oshea, Saurabh VyasAbstract:Summary A central goal of systems neuroscience is to relate an organism’s Neural activity to behavior. Neural Population analyses often reduce the data dimensionality to focus on relevant activity patterns. A major hurdle to data analysis is spike sorting, and this problem is growing as the number of recorded neurons increases. Here, we investigate whether spike sorting is necessary to estimate Neural Population dynamics. The theory of random projections suggests that we can accurately estimate the geometry of low-dimensional manifolds from a small number of linear projections of the data. We recorded data using Neuropixels probes in motor cortex of nonhuman primates and reanalyzed data from three previous studies and found that Neural dynamics and scientific conclusions are quite similar using multiunit threshold crossings rather than sorted neurons. This finding unlocks existing data for new analyses and informs the design and use of new electrode arrays for laboratory and clinical use.
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inferring single trial Neural Population dynamics using sequential auto encoders
Nature Methods, 2018Co-Authors: Chethan Pandarinath, Eric Trautmann, Sergey D Stavisky, Matthew T Kaufman, Daniel J Oshea, Jasmine Collins, Rafal Jozefowicz, Leigh R HochbergAbstract:Neuroscience is experiencing a revolution in which simultaneous recording of thousands of neurons is revealing Population dynamics that are not apparent from single-neuron responses. This structure is typically extracted from data averaged across many trials, but deeper understanding requires studying phenomena detected in single trials, which is challenging due to incomplete sampling of the Neural Population, trial-to-trial variability, and fluctuations in action potential timing. We introduce latent factor analysis via dynamical systems, a deep learning method to infer latent dynamics from single-trial Neural spiking data. When applied to a variety of macaque and human motor cortical datasets, latent factor analysis via dynamical systems accurately predicts observed behavioral variables, extracts precise firing rate estimates of Neural dynamics on single trials, infers perturbations to those dynamics that correlate with behavioral choices, and combines data from non-overlapping recording sessions spanning months to improve inference of underlying dynamics.
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accurate estimation of Neural Population dynamics without spike sorting
bioRxiv, 2017Co-Authors: Eric Trautmann, Stephen I Ryu, Sergey D Stavisky, Subhaneil Lahiri, Katherine Cora Ames, Matthew T Kaufman, Surya Ganguli, Krishna V ShenoyAbstract:A central goal of systems neuroscience is to relate an organism's Neural activity to behavior. Neural Population analysis often begins by reducing the dimensionality of the data to focus on the patterns most relevant to a given task. A major practical hurdle to data analysis is spike sorting, and this problem is growing rapidly as the number of neurons measured increases. Here, we investigate whether spike sorting is necessary to estimate Neural dynamics. The theory of random projections suggests that we can accurately estimate the geometry of low-dimensional manifolds from a small number of linear projections of the data. We re-analyzed data from three previous studies and found that Neural dynamics and scientific conclusions are quite similar using multi-unit threshold crossings in place of sorted neurons. This finding unlocks existing data for new analyses and informs the design and use of new electrode arrays for laboratory and clinical use.
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inferring single trial Neural Population dynamics using sequential auto encoders
bioRxiv, 2017Co-Authors: Chethan Pandarinath, Eric Trautmann, Sergey D Stavisky, Matthew T Kaufman, Daniel J Oshea, Jasmine Collins, Rafal Jozefowicz, Leigh R Hochberg, Jaimie M Henderson, Krishna V ShenoyAbstract:Neuroscience is experiencing a data revolution in which simultaneous recording of many hundreds or thousands of neurons is revealing structure in Population activity that is not apparent from single-neuron responses. This structure is typically extracted from trial-averaged data. Single-trial analyses are challenging due to incomplete sampling of the Neural Population, trial-to-trial variability, and fluctuations in action potential timing. Here we introduce Latent Factor Analysis via Dynamical Systems (LFADS), a deep learning method to infer latent dynamics from single-trial Neural spiking data. LFADS uses a nonlinear dynamical system (a recurrent Neural network) to infer the dynamics underlying observed Population activity and to extract ‘de-noised’ single-trial firing rates from Neural spiking data. We apply LFADS to a variety of monkey and human motor cortical datasets, demonstrating its ability to predict observed behavioral variables with unprecedented accuracy, extract precise estimates of Neural dynamics on single trials, infer perturbations to those dynamics that correlate with behavioral choices, and combine data from non-overlapping recording sessions (spanning months) to improve inference of underlying dynamics. In summary, LFADS leverages all observations of a Neural Population's activity to accurately model its dynamics on single trials, opening the door to a detailed understanding of the role of dynamics in performing computation and ultimately driving behavior.
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Neural Population dynamics during reaching
Nature, 2012Co-Authors: John P Cunningham, Mark M Churchland, Matthew T Kaufman, Justin D Foster, Paul Nuyujukian, Krishna V ShenoyAbstract:Most theories of motor cortex have assumed that Neural activity represents movement parameters. This view derives from what is known about primary visual cortex, where Neural activity represents patterns of light. Yet it is unclear how well the analogy between motor and visual cortex holds. Single-neuron responses in motor cortex are complex, and there is marked disagreement regarding which movement parameters are represented. A better analogy might be with other motor systems, where a common principle is rhythmic Neural activity. Here we find that motor cortex responses during reaching contain a brief but strong oscillatory component, something quite unexpected for a non-periodic behaviour. Oscillation amplitude and phase followed naturally from the preparatory state, suggesting a mechanistic role for preparatory Neural activity. These results demonstrate an unexpected yet surprisingly simple structure in the Population response. This underlying structure explains many of the confusing features of individual Neural responses. It has long been thought that individual neurons in the motor and premotor cortex are tuned for parameters of movements such as direction. But despite decades of work, the exact nature of the represented parameters is still unclear, as are the mechanisms through which such representations could support the complex movements made in the course of every day life. Here, Churchland et al. propose an alternative theory — that Population dynamics could underlie motor control. They show that reaching movements are associated with oscillatory Population activity in the monkey motor cortex, despite there being no periodic component to this behaviour. The amplitude and phase of the oscillation followed naturally from the preceding state, suggesting a role for preparatory Neural activity in reaching.
Mark M Churchland - One of the best experts on this subject based on the ideXlab platform.
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deep random splines for point process intensity estimation of Neural Population data
Neural Information Processing Systems, 2019Co-Authors: Gabriel Loaizaganem, Sean M Perkins, Karen E Schroeder, Mark M Churchland, John P CunninghamAbstract:Gaussian processes are the leading class of distributions on random functions, but they suffer from well known issues including difficulty scaling and inflexibility with respect to certain shape constraints (such as nonnegativity). Here we propose Deep Random Splines, a flexible class of random functions obtained by transforming Gaussian noise through a deep Neural network whose output are the parameters of a spline. Unlike Gaussian processes, Deep Random Splines allow us to readily enforce shape constraints while inheriting the richness and tractability of deep generative models. We also present an observational model for point process data which uses Deep Random Splines to model the intensity function of each point process and apply it to Neural Population data to obtain a low-dimensional representation of spiking activity. Inference is performed via a variational autoencoder that uses a novel recurrent encoder architecture that can handle multiple point processes as input. We use a newly collected dataset where a primate completes a pedaling task, and observe better dimensionality reduction with our model than with competing alternatives.
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single trial dynamics of motor cortex and their applications to brain machine interfaces
Nature Communications, 2015Co-Authors: Paul Nuyujukian, John P Cunningham, Mark M Churchland, Krishna V ShenoyAbstract:Increasing evidence suggests that Neural Population responses have their own internal drive, or dynamics, that describe how the Neural Population evolves through time. An important prediction of Neural dynamical models is that previously observed Neural activity is informative of noisy yet-to-be-observed activity on single-trials, and may thus have a denoising effect. To investigate this prediction, we built and characterized dynamical models of single-trial motor cortical activity. We find these models capture salient dynamical features of the Neural Population and are informative of future Neural activity on single trials. To assess how Neural dynamics may beneficially denoise single-trial Neural activity, we incorporate Neural dynamics into a brain–machine interface (BMI). In online experiments, we find that a Neural dynamical BMI achieves substantially higher performance than its non-dynamical counterpart. These results provide evidence that Neural dynamics beneficially inform the temporal evolution of Neural activity on single trials and may directly impact the performance of BMIs.
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Neural Population dynamics during reaching
Nature, 2012Co-Authors: John P Cunningham, Mark M Churchland, Matthew T Kaufman, Justin D Foster, Paul Nuyujukian, Krishna V ShenoyAbstract:Most theories of motor cortex have assumed that Neural activity represents movement parameters. This view derives from what is known about primary visual cortex, where Neural activity represents patterns of light. Yet it is unclear how well the analogy between motor and visual cortex holds. Single-neuron responses in motor cortex are complex, and there is marked disagreement regarding which movement parameters are represented. A better analogy might be with other motor systems, where a common principle is rhythmic Neural activity. Here we find that motor cortex responses during reaching contain a brief but strong oscillatory component, something quite unexpected for a non-periodic behaviour. Oscillation amplitude and phase followed naturally from the preparatory state, suggesting a mechanistic role for preparatory Neural activity. These results demonstrate an unexpected yet surprisingly simple structure in the Population response. This underlying structure explains many of the confusing features of individual Neural responses. It has long been thought that individual neurons in the motor and premotor cortex are tuned for parameters of movements such as direction. But despite decades of work, the exact nature of the represented parameters is still unclear, as are the mechanisms through which such representations could support the complex movements made in the course of every day life. Here, Churchland et al. propose an alternative theory — that Population dynamics could underlie motor control. They show that reaching movements are associated with oscillatory Population activity in the monkey motor cortex, despite there being no periodic component to this behaviour. The amplitude and phase of the oscillation followed naturally from the preceding state, suggesting a role for preparatory Neural activity in reaching.
Stefano Panzeri - One of the best experts on this subject based on the ideXlab platform.
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correlations enhance the behavioral readout of Neural Population activity in association cortex
Nature Neuroscience, 2021Co-Authors: Martina Valente, Giuseppe Pica, Giulio Bondanelli, Monica Moroni, Caroline A Runyan, Ari S Morcos, Christopher D Harvey, Stefano PanzeriAbstract:Noise correlations (that is, trial-to-trial covariations in Neural activity for a given stimulus) limit the stimulus information encoded by Neural Populations, leading to the widely held prediction that they impair perceptual discrimination behaviors. However, this prediction neglects the effects of correlations on information readout. We studied how correlations affect both encoding and readout of sensory information. We analyzed calcium imaging data from mouse posterior parietal cortex during two perceptual discrimination tasks. Correlations reduced the encoded stimulus information, but, seemingly paradoxically, were higher when mice made correct rather than incorrect choices. Single-trial behavioral choices depended not only on the stimulus information encoded by the whole Population, but unexpectedly also on the consistency of information across neurons and time. Because correlations increased information consistency, they enhanced the conversion of sensory information into behavioral choices, overcoming their detrimental information-limiting effects. Thus, correlations in association cortex can benefit task performance even if they decrease sensory information.
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correlations enhance the behavioral readout of Neural Population activity in association cortex
bioRxiv, 2020Co-Authors: Martina Valente, Giuseppe Pica, Caroline A Runyan, Ari S Morcos, Christopher D Harvey, Stefano PanzeriAbstract:The spatiotemporal structure of activity in Populations of neurons is critical for accurate perception and behavior. Experimental and theoretical studies have focused on noise correlations (trial-to-trial covariations in Neural activity for a given stimulus) as a key feature of Population activity structure. Much work has shown that these correlations limit the stimulus information encoded by a Population of neurons, leading to the widely held prediction that correlations are detrimental for perceptual discrimination behaviors. However, this prediction relies on an untested assumption: that the Neural mechanisms that read out sensory information to inform behavior depend only on the Population total stimulus information independently of how correlations constrain this information across neurons or time. Here we make the critical advance of simultaneously studying how correlations affect both the encoding and the readout of sensory information. We analyzed calcium imaging data from mouse posterior parietal cortex during two perceptual discrimination tasks. Correlations limited the ability to encode stimulus information, but (seemingly paradoxically) correlations were higher when mice made correct choices than when they made errors. On a single-trial basis, the behavioral choice of the mouse depended not only on the stimulus information in the activity of the Population as a whole, but unexpectedly also on the consistency of information across neurons and time. Because correlations increased information consistency, sensory information was more efficiently converted into a behavioral choice in the presence of correlations. Given this enhanced-by-consistency readout, we estimated that correlations produced a behavioral benefit that compensated or overcame their detrimental information-limiting effects. These results call for a reevaluation of the role of correlated Neural activity, and suggest that correlations in association cortex can benefit task performance even if they decrease sensory information.
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Neural Population coding combining insights from microscopic and mass signals
Trends in Cognitive Sciences, 2015Co-Authors: Stefano Panzeri, Jakob H Macke, Joachim Gross, Christoph KayserAbstract:Behavior relies on the distributed and coordinated activity of Neural Populations. Population activity can be measured using multi-neuron recordings and neuroimaging. Neural recordings reveal how the heterogeneity, sparseness, timing, and correlation of Population activity shape information processing in local networks, whereas neuroimaging shows how long-range coupling and brain states impact on local activity and perception. To obtain an integrated perspective on Neural information processing we need to combine knowledge from both levels of investigation. We review recent progress of how Neural recordings, neuroimaging, and computational approaches begin to elucidate how interactions between local Neural Population activity and large-scale dynamics shape the structure and coding capacity of local information representations, make them state-dependent, and control distributed Populations that collectively shape behavior.
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comparison of the dynamics of Neural interactions between current based and conductance based integrate and fire recurrent networks
Frontiers in Neural Circuits, 2014Co-Authors: Stefano Cavallari, Stefano Panzeri, Alberto MazzoniAbstract:Models of networks of Leaky Integrate-and-Fire neurons (LIF) are a widely used tool for theoretical investigations of brain function. These models have been used both with current- and conductance-based synapses. However, the differences in the dynamics expressed by these two approaches have been so far mainly studied at the single neuron level. To investigate how these synaptic models affect network activity, we compared the single-neuron and Neural Population dynamics of conductance-based networks (COBN) and current-based networks (CUBN) of LIF neurons. These networks were endowed with sparse excitatory and inhibitory recurrent connections, and were tested in conditions including both low- and high-conductance states. We developed a novel procedure to obtain comparable networks by properly tuning the synaptic parameters not shared by the models. The so defined comparable networks displayed an excellent and robust match of first order statistics (average single neuron firing rates and average frequency spectrum of network activity). However, these comparable networks showed profound differences in the second order statistics of Neural Population interactions and in the modulation of these properties by external inputs. The correlation between inhibitory and excitatory synaptic currents and the cross-neuron correlation between synaptic inputs, membrane potentials and spike trains were stronger and more stimulus-sensitive in the COBN. Because of these properties, the spike train correlation carried more information about the strength of the input in the COBN, although the firing rates were equally informative in both network models. Moreover, COBN showed stronger neuronal Population synchronization in the gamma band, and their spectral information about the network input was higher and spread over a broader range of frequencies. These results suggest that second order properties of network dynamics depend strongly on the choice of synaptic model.
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the impact of high order interactions on the rate of synchronous discharge and information transmission in somatosensory cortex
Philosophical Transactions of the Royal Society A, 2009Co-Authors: Fernando Montani, Stefano Panzeri, Robin A A Ince, Riccardo Senatore, Ehsan Arabzadeh, Mathew E DiamondAbstract:Understanding the operations of Neural networks in the brain requires an understanding of whether interactions among neurons can be described by a pairwise interaction model, or whether a higher order interaction model is needed. In this article we consider the rate of synchronous discharge of a local Population of neurons, a macroscopic index of the activation of the Neural network that can be measured experimentally. We analyse a model based on physics’ maximum entropy principle that evaluates whether the probability of synchronous discharge can be described by interactions up to any given order. When compared with real Neural Population activity obtained from the rat somatosensory cortex, the model shows that interactions of at least order three or four are necessary to explain the data. We use Shannon information to compute the impact of high-order correlations on the amount of somatosensory information transmitted by the rate of synchronous discharge, and we find that correlations of higher order progressively decrease the information available through the Neural Population. These results are compatible with the hypothesis that high-order interactions play a role in shaping the dynamics of Neural networks, and that they should be taken into account when computing the representational capacity of Neural Populations.