Pairwise correlation graphs from hippocampal population activity have highly non-random, low-dimensional clique topology

AbstractMotor function depends on neural dynamics spanning multiple spatiotemporal scales of population activity, from spiking of neurons to larger-scale local field potentials (LFP). How multiple scales of low-dimensional population dynamics are related in control of movements remains unknown. Multiscale neural dynamics are especially important to study in naturalistic reach-and-grasp movements, which are relatively under-explored. We learn novel multiscale dynamical models for spike-LFP network activity in monkeys performing naturalistic reach-and-grasps. We show low-dimensional dynamics of spiking and LFP activity exhibited several principal modes, each with a unique decay-frequency characteristic. One principal mode dominantly predicted movements. Despite distinct principal modes existing at the two scales, this predictive mode was multiscale and shared between scales, and was shared across sessions and monkeys, yet did not simply replicate behavioral modes. Further, this multiscale mode’s decay-frequency explained behavior. We propose that multiscale, low-dimensional motor cortical state dynamics reflect the neural control of naturalistic reach-and-grasp behaviors.

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Dense Circuit Reconstruction to Understand Neuronal Computation: Focus on Zebrafish

Annual Review of Neuroscience ◽

10.1146/annurev-neuro-110220-013050 ◽

2021 ◽

Vol 44 (1) ◽

Author(s):

Rainer W. Friedrich ◽

Adrian A. Wanner

Keyword(s):

Neuronal Population ◽

Higher Order ◽

Order Structure ◽

Annual Review ◽

Publication Date ◽

Population Activity ◽

Stimulus Features ◽

Processing And Storage ◽

Low Dimensional ◽

Neuronal Computation

The dense reconstruction of neuronal wiring diagrams from volumetric electron microscopy data has the potential to generate fundamentally new insights into mechanisms of information processing and storage in neuronal circuits. Zebrafish provide unique opportunities for dynamical connectomics approaches that combine reconstructions of wiring diagrams with measurements of neuronal population activity and behavior. Such approaches have the power to reveal higher-order structure in wiring diagrams that cannot be detected by sparse sampling of connectivity and that is essential for neuronal computations. In the brain stem, recurrently connected neuronal modules were identified that can account for slow, low-dimensional dynamics in an integrator circuit. In the spinal cord, connectivity specifies functional differences between premotor interneurons. In the olfactory bulb, tuning-dependent connectivity implements a whitening transformation that is based on the selective suppression of responses to overrepresented stimulus features. These findings illustrate the potential of dynamical connectomics in zebrafish to analyze the circuit mechanisms underlying higher-order neuronal computations. Expected final online publication date for the Annual Review of Neuroscience, Volume 44 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Inference of a Mesoscopic Population Model from Population Spike Trains

Neural Computation ◽

10.1162/neco_a_01292 ◽

2020 ◽

Vol 32 (8) ◽

pp. 1448-1498 ◽

Cited By ~ 3

Author(s):

Alexandre René ◽

André Longtin ◽

Jakob H. Macke

Keyword(s):

Population Model ◽

Single Neuron ◽

Simulated Data ◽

Population Data ◽

Model Parameters ◽

Population Activity ◽

Mcmc Sampling ◽

Wide Range ◽

Low Dimensional ◽

Aggregate Population

Understanding how rich dynamics emerge in neural populations requires models exhibiting a wide range of behaviors while remaining interpretable in terms of connectivity and single-neuron dynamics. However, it has been challenging to fit such mechanistic spiking networks at the single-neuron scale to empirical population data. To close this gap, we propose to fit such data at a mesoscale, using a mechanistic but low-dimensional and, hence, statistically tractable model. The mesoscopic representation is obtained by approximating a population of neurons as multiple homogeneous pools of neurons and modeling the dynamics of the aggregate population activity within each pool. We derive the likelihood of both single-neuron and connectivity parameters given this activity, which can then be used to optimize parameters by gradient ascent on the log likelihood or perform Bayesian inference using Markov chain Monte Carlo (MCMC) sampling. We illustrate this approach using a model of generalized integrate-and-fire neurons for which mesoscopic dynamics have been previously derived and show that both single-neuron and connectivity parameters can be recovered from simulated data. In particular, our inference method extracts posterior correlations between model parameters, which define parameter subsets able to reproduce the data. We compute the Bayesian posterior for combinations of parameters using MCMC sampling and investigate how the approximations inherent in a mesoscopic population model affect the accuracy of the inferred single-neuron parameters.

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Optimal anticipatory control as a theory of motor preparation: a thalamo-cortical circuit model

10.1101/2020.02.02.931246 ◽

2020 ◽

Cited By ~ 2

Author(s):

Ta-Chu Kao ◽

Mahdieh S. Sadabadi ◽

Guillaume Hennequin

Keyword(s):

Internal Control ◽

Initial Conditions ◽

Motor Preparation ◽

Anticipatory Control ◽

Subsequent Evolution ◽

Population Activity ◽

Prospective Control ◽

Cortical Circuit ◽

Low Dimensional ◽

Low Dimensional Dynamics

SummaryAcross a range of motor and cognitive tasks, cortical activity can be accurately described by low-dimensional dynamics unfolding from specific initial conditions on every trial. These “preparatory states” largely determine the subsequent evolution of both neural activity and behaviour, and their importance raises questions regarding how they are — or ought to be — set. Here, we formulate motor preparation as optimal prospective control of future movements. The solution is a form of internal control of cortical circuit dynamics, which can be implemented as a thalamo-cortical loop gated by the basal ganglia. Critically, optimal control predicts selective quenching of variability in components of preparatory population activity that have future motor consequences, but not in others. This is consistent with recent perturbation experiments performed in mice, and with our novel analysis of monkey motor cortex activity during reaching. Together, these results suggest optimal anticipatory control of movement.

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Neuronal population activity dynamics reveal a low-dimensional signature of operant learning

10.1101/2021.12.06.471434 ◽

2021 ◽

Author(s):

Renan M. Costa ◽

Douglas A. Baxter ◽

John H. Byrne

Keyword(s):

Single Neuron ◽

Brain Regions ◽

Neuronal Population ◽

High Dimensional ◽

Operant Learning ◽

Population Activity ◽

Temporal Shift ◽

Voltage Imaging ◽

Low Dimensional ◽

Activity Dynamics

AbstractLearning engages a high-dimensional neuronal population space spanning multiple brain regions. We identified a low-dimensional signature associated with operant conditioning, a ubiquitous form of learning in which animals learn from the consequences of behavior. Using single-neuron resolution voltage imaging, we identified two low-dimensional motor modules in the neuronal population underlying Aplysia feeding. Our findings point to a temporal shift in module recruitment as the primary signature of operant learning.

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Conserved structures of neural activity in sensorimotor cortex of freely moving rats allow cross-subject decoding

10.1101/2021.03.04.433869 ◽

2021 ◽

Author(s):

Svenja Melbaum ◽

David Eriksson ◽

Thomas Brox ◽

Ilka Diester

Keyword(s):

Sensorimotor Cortex ◽

Activity Patterns ◽

Population Activity ◽

Electrophysiological Recordings ◽

Primary Sensory Cortex ◽

Behavioral Paradigm ◽

Structure Of Population ◽

Freely Moving ◽

Low Dimensional ◽

Neuronal Code

Our knowledge about neuronal activity in the sensorimotor cortex relies primarily on stereotyped movements which are strictly controlled via the experimental settings. It remains unclear how results can be carried over to less constrained behavior, i.e. freely moving subjects. Towards this goal, we developed a self-paced behavioral paradigm which encouraged rats to conduct different types of movements. Via bilateral electrophysiological recordings across the entire sensorimotor cortex and simultaneous paw tracking, we identified behavioral coupling of neurons with lateralization and an anterior-posterior gradient from premotor to primary sensory cortex. The structure of population activity patterns was conserved across animals, in spite of severe undersampling of the total number of neurons and variations of electrode positions across individuals. Via alignments of low-dimensional neural manifolds, we demonstrate cross-subject and cross-session generalization in a decoding task arguing for a conserved neuronal code.

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Discovery of Salient Low-Dimensional Dynamical Structure in Neuronal Population Activity Using Hopfield Networks

10.1007/978-3-319-24261-3_16 ◽

2015 ◽

pp. 199-208 ◽

Cited By ~ 2

Author(s):

Felix Effenberger ◽

Christopher Hillar

Keyword(s):

Neuronal Population ◽

Dynamical Structure ◽

Hopfield Networks ◽

Population Activity ◽

Low Dimensional

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Continuous Attractor Neural Networks: Candidate of a Canonical Model for Neural Information Representation

F1000Research ◽

10.12688/f1000research.7387.1 ◽

2016 ◽

Vol 5 ◽

pp. 156 ◽

Cited By ~ 13

Author(s):

Si Wu ◽

K Y Michael Wong ◽

C C Alan Fung ◽

Yuanyuan Mi ◽

Wenhao Zhang

Keyword(s):

Neural Networks ◽

Dimensional Space ◽

Spatial Location ◽

Canonical Model ◽

Neural Population ◽

Information Representation ◽

Population Activity ◽

Neural Information ◽

Complex Features ◽

Low Dimensional

Owing to its many computationally desirable properties, the model of continuous attractor neural networks (CANNs) has been successfully applied to describe the encoding of simple continuous features in neural systems, such as orientation, moving direction, head direction, and spatial location of objects. Recent experimental and computational studies revealed that complex features of external inputs may also be encoded by low-dimensional CANNs embedded in the high-dimensional space of neural population activity. The new experimental data also confirmed the existence of the M-shaped correlation between neuronal responses, which is a correlation structure associated with the unique dynamics of CANNs. This body of evidence, which is reviewed in this report, suggests that CANNs may serve as a canonical model for neural information representation.

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Gaussian-Process Factor Analysis for Low-Dimensional Single-Trial Analysis of Neural Population Activity

Journal of Neurophysiology ◽

10.1152/jn.90941.2008 ◽

2009 ◽

Vol 102 (1) ◽

pp. 614-635 ◽

Cited By ~ 238

Author(s):

Byron M. Yu ◽

John P. Cunningham ◽

Gopal Santhanam ◽

Stephen I. Ryu ◽

Krishna V. Shenoy ◽

...

Keyword(s):

Factor Analysis ◽

Gaussian Process ◽

Predictive Ability ◽

Neural Population ◽

Single Trial ◽

Two Stage ◽

Population Activity ◽

Process Factor ◽

Low Dimensional ◽

Neural Population Activity

We consider the problem of extracting smooth, low-dimensional neural trajectories that summarize the activity recorded simultaneously from many neurons on individual experimental trials. Beyond the benefit of visualizing the high-dimensional, noisy spiking activity in a compact form, such trajectories can offer insight into the dynamics of the neural circuitry underlying the recorded activity. Current methods for extracting neural trajectories involve a two-stage process: the spike trains are first smoothed 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 that account for spiking variability, which 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 the proposed extensions improved the predictive ability of the two-stage methods. The predictive ability was further improved by going to GPFA. From the extracted trajectories, we directly observed a convergence in neural state during motor planning, an effect that was shown indirectly by previous studies. We then show how such methods can be a powerful tool for relating the spiking activity across a neural population to the subject's behavior on a single-trial basis. Finally, to assess how well the proposed methods characterize neural population activity when the underlying time course is known, we performed simulations that revealed that GPFA performed tens of percent better than the best two-stage method.

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Bridging neuronal correlations and dimensionality reduction

10.1101/2020.12.04.383604 ◽

2020 ◽

Author(s):

Akash Umakantha ◽

Rudina Morina ◽

Benjamin R. Cowley ◽

Adam C. Snyder ◽

Matthew A. Smith ◽

...

Keyword(s):

Dimensionality Reduction ◽

Study Trial ◽

Spike Count ◽

Cautionary Tale ◽

Empirical Relationships ◽

Population Activity ◽

Pairwise Correlation ◽

Approaches To Study

AbstractTwo commonly used approaches to study interactions among neurons are spike count correlation, which describes pairs of neurons, and dimensionality reduction, applied to a population of neurons. While both approaches have been used to study trial-to-trial correlated neuronal variability, they are often used in isolation and have not been directly related. We first established concrete mathematical and empirical relationships between pairwise correlation and metrics of population-wide covariability based on dimensionality reduction. Applying these insights to macaque V4 population recordings, we found that the previously reported decrease in mean pairwise correlation associated with attention stemmed from three distinct changes in population-wide covariability. Overall, our work builds the intuition and formalism to bridge between pairwise correlation and population-wide covariability and presents a cautionary tale about the inferences one can make about population activity by using a single statistic, whether it be mean pairwise correlation or dimensionality.

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