scholarly journals Rotational dynamics versus sequence-like responses

2020 ◽  
Author(s):  
Mikhail A Lebedev ◽  
Ivan Ninenko ◽  
Alexei Ossadtchi
Keyword(s):  
Arm Movement ◽  
Population Data ◽  
Neuronal Population ◽  
Rotational Dynamics ◽  
Neural Population ◽  
Response Patterns ◽  
Neuronal Patterns ◽  
Low Dimensional ◽  
Response Timing ◽  
Temporal Sequences

AbstractIn a recent review, Vyas et al. commented on our previous observations regarding the presence of response sequences in the activity of cortical neuronal population and the contribution of such sequences to rotational dynamics patterns revealed with jPCA. Vyas et al. suggested that rotations generated from sequence-like responses are different from the ones arising from empirical neuronal patterns, which are highly heterogeneous across motor conditions in terms of response timing and shape. Here we extend our previous findings with new results showing that empirical population data contain plentiful neuronal responses whose shape and timing persist across arm-movement conditions. The more complex, heterogeneous responses can be also found; these response patterns also contain temporal sequences, which are evident from the analysis of cross-condition variance. Combined with simulation results, these observations show that both consistent and heterogeneous responses contribute to rotational patterns revealed with jPCA. We suggest that the users of jPCA should consider these two contributions when interpreting their results. Overall, we do not see any principal contradiction between the neural population dynamics framework and our results pertaining to sequence-like responses. Yet, questions remain regarding the conclusions that can be drawn from the analysis of low-dimensional representations of neuronal population data.

scholarly journals Neural population dynamics in motor cortex are different for reach and grasp

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Aneesha K Suresh ◽  
James M Goodman ◽  
Elizaveta V Okorokova ◽  
Matthew Kaufman ◽  
Nicholas G Hatsopoulos ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Population Level ◽  
Basic Structure ◽  
Neuronal Population ◽  
Neural Population ◽  
Population Activity ◽  
Muscle Activations ◽  
Low Dimensional ◽  
Analytical Approaches

Low-dimensional linear dynamics are observed in neuronal population activity in primary motor cortex (M1) when monkeys make reaching movements. This population-level behavior is consistent with a role for M1 as an autonomous pattern generator that drives muscles to give rise to movement. In the present study, we examine whether similar dynamics are also observed during grasping movements, which involve fundamentally different patterns of kinematics and muscle activations. Using a variety of analytical approaches, we show that M1 does not exhibit such dynamics during grasping movements. Rather, the grasp-related neuronal dynamics in M1 are similar to their counterparts in somatosensory cortex, whose activity is driven primarily by afferent inputs rather than by intrinsic dynamics. The basic structure of the neuronal activity underlying hand control is thus fundamentally different from that underlying arm control.

scholarly journals NEURAL POPULATION DYNAMICS IN MOTOR CORTEX ARE DIFFERENT FOR REACH AND GRASP

2019 ◽  
Author(s):  
Aneesha K. Suresh ◽  
James M. Goodman ◽  
Elizaveta V. Okorokova ◽  
Matthew T. Kaufman ◽  
Nicholas G. Hatsopoulos ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Population Level ◽  
Pattern Generator ◽  
Neuronal Population ◽  
Rotational Dynamics ◽  
Neural Population ◽  
Population Activity ◽  
Reach And Grasp ◽  
Smooth Dynamics

AbstractRotational dynamics are observed in neuronal population activity in primary motor cortex (M1) when monkeys make reaching movements. This population-level behavior is consistent with a role for M1 as an autonomous pattern generator that drives muscles to produce movement. Here, we show that M1 does not exhibit smooth dynamics during grasping movements, suggesting a more input-driven circuit.

scholarly journals Demixed principal component analysis of neural population data

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Dmitry Kobak ◽  
Wieland Brendel ◽  
Christos Constantinidis ◽  
Claudia E Feierstein ◽  
Adam Kepecs ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Population Data ◽  
Component Analysis ◽  
Neural Representation ◽  
Neural Population ◽  
Population Activity ◽  
Cortical Areas ◽  
Dimensionality Reduction Technique ◽  
Single Figure

Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure.

Dense Circuit Reconstruction to Understand Neuronal Computation: Focus on Zebrafish

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.

scholarly journals Inference of a Mesoscopic Population Model from Population Spike Trains

2020 ◽  
Vol 32 (8) ◽  
pp. 1448-1498 ◽  
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.

Three-dimensional plotting of neuronal population response patterns

1978 ◽  
Vol 3 (4) ◽  
pp. 299-303
Author(s):  
Gernot S. Doetsch ◽  
Robert J. Haggard ◽  
Leonard K. Sweatman
Keyword(s):  
Three Dimensional ◽  
Neuronal Population ◽  
Response Patterns ◽  
Population Response

scholarly journals What, if anything, is the true neurophysiological significance of “rotational dynamics”?

2019 ◽  
Author(s):  
Mikhail A. Lebedev ◽  
Alexei Ossadtchi ◽  
Nil Adell Mill ◽  
Núria Armengol Urpí ◽  
Maria R. Cervera ◽  
...  
Keyword(s):  
Neuronal Population ◽  
Rotational Dynamics ◽  
Neuronal Responses ◽  
Linear Transformations ◽  
Experimental Conditions ◽  
Temporal Shift ◽  
Firing Rates ◽  
Motor Behaviors ◽  
Neuronal Populations ◽  
Neuronal Mechanisms

AbstractBack in 2012, Churchland and his colleagues proposed that “rotational dynamics”, uncovered through linear transformations of multidimensional neuronal data, represent a fundamental type of neuronal population processing in a variety of organisms, from the isolated leech central nervous system to the primate motor cortex. Here, we evaluated this claim using Churchland’s own data and simple simulations of neuronal responses. We observed that rotational patterns occurred in neuronal populations when (1) there was a temporal shift in peak firing rates exhibited by individual neurons, and (2) the temporal sequence of peak rates remained consistent across different experimental conditions. Provided that such a temporal order of peak firing rates existed, rotational patterns could be easily obtained using a rather arbitrary computer simulation of neural activity; modeling of any realistic properties of motor cortical responses was not needed. Additionally, arbitrary traces, such as Lissajous curves, could be easily obtained from Churchland’s data with multiple linear regression. While these observations suggest that temporal sequences of neuronal responses could be visualized as rotations with various methods, we express doubt about Churchland et al.’s exaggerated assessment that such rotations are related to “an unexpected yet surprisingly simple structure in the population response”, which “explains many of the confusing features of individual neural responses.” Instead, we argue that their approach provides little, if any, insight on the underlying neuronal mechanisms employed by neuronal ensembles to encode motor behaviors in any species.

scholarly journals A latent variable approach to decoding neural population activity

2020 ◽  
Author(s):  
Matthew R Whiteway ◽  
Bruno Averbeck ◽  
Daniel A Butts
Keyword(s):  
Latent Variables ◽  
Population Variability ◽  
Model Organisms ◽  
Neural Population ◽  
Simple Extension ◽  
Linear Discriminant ◽  
Variable Approach ◽  
Wide Range ◽  
Neural Populations ◽  
Low Dimensional

AbstractDecoding is a powerful approach for measuring the information contained in the activity of neural populations. As a result, decoding analyses are now used across a wide range of model organisms and experimental paradigms. However, typical analyses employ general purpose decoding algorithms that do not explicitly take advantage of the structure of neural variability, which is often low-dimensional and can thus be effectively characterized using latent variables. Here we propose a new decoding framework that exploits the low-dimensional structure of neural population variability by removing correlated variability that is unrelated to the decoded variable, then decoding the resulting denoised activity. We demonstrate the efficacy of this framework using simulated data, where the true upper bounds for decoding performance are known. A linear version of our decoder provides an estimator for the decoded variable that can be more efficient than other commonly used linear estimators such as linear discriminant analysis. In addition, our proposed decoding framework admits a simple extension to nonlinear decoding that compares favorably to standard feed-forward neural networks. By explicitly modeling shared population variability, the success of the resulting linear and nonlinear decoders also offers a new perspective on the relationship between shared variability and information contained in large neural populations.

scholarly journals Skill-specific changes in cortical preparatory activity during motor learning

2020 ◽  
Author(s):  
Xulu Sun ◽  
Daniel J. O’Shea ◽  
Matthew D. Golub ◽  
Eric M. Trautmann ◽  
Saurabh Vyas ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Motor Skills ◽  
Rhesus Monkeys ◽  
Activity Patterns ◽  
Arm Movement ◽  
Neural Population ◽  
Motor Memory ◽  
Short Term ◽  
Preparatory Activity ◽  
Open Question

AbstractAnimals have a remarkable capacity to learn new motor skills, but it remains an open question as to how learning changes neural population dynamics underlying movement1. Specifically, we asked whether changes in neural population dynamics relate purely to newly learned movements or if additional patterns are generated that facilitate learning without matching motor output. We trained rhesus monkeys to learn a curl force field2 task that elicited new arm-movement kinetics for some but not all reach directions3,4. We found that along certain neural dimensions, preparatory activity in motor cortex reassociated existing activity patterns with new movements. These systematic changes were observed only for learning-altered reaches. Surprisingly, we also found prominent shifts of preparatory activity along a nearly orthogonal neural dimension. These changes in preparatory activity were observed uniformly for all reaches including those unaltered by learning. This uniform shift during learning implies formation of new neural activity patterns, which was not observed in other short-term learning contexts5–8. During a washout period when the curl field was removed, movement kinetics gradually reverted, but the learning-induced uniform shift of preparatory activity persisted and a second, orthogonal uniform shift occurred. This persistent shift may retain a motor memory of the learned field9–11, consistent with faster relearning of the same curl field observed behaviorally and neurally. When multiple different curl fields were learned sequentially, we found distinct uniform shifts, each reflecting the identity of the field applied and potentially separating the associated motor memories12,13. The neural geometry of these shifts in preparatory activity could serve to organize skill-specific changes in movement production, facilitating the acquisition and retention of a broad motor repertoire.

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