Neural Manifolds for the Control of Movement

Gallego, Juan Álvaro; Perich, Matthew G.; Miller, Lee E.; Solla, Sara A.

doi:10.1016/j.neuron.2017.05.025

Cited by 529 publications

(597 citation statements)

References 68 publications

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“…Our work is also of potential relevance to study another timely issue in neuroscience, that is the relevance for perception of the information about sensory variables carried by neural activity [19,45]. This is a crucial issue to resolve the diatribe about the nature of the neural code, that is the set of symbols used by neurons to encode information and produce brain function [46][47][48]. Addressing this problem requires mapping the information in the multivariate distribution of variables such as the stimuli presented to the subject, the neural activity elicited by the presentation of such stimuli, and the behavioral reports of the subject's perception.…”

Section: Potential Implications For Systems Biology and Systems Neuromentioning

confidence: 91%

Invariant Components of Synergy, Redundancy, and Unique Information among Three Variables

Pica

Piasini

Chicharro

et al. 2017

Entropy

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Abstract:In a system of three stochastic variables, the Partial Information Decomposition (PID) of Williams and Beer dissects the information that two variables (sources) carry about a third variable (target) into nonnegative information atoms that describe redundant, unique, and synergistic modes of dependencies among the variables. However, the classification of the three variables into two sources and one target limits the dependency modes that can be quantitatively resolved, and does not naturally suit all systems. Here, we extend the PID to describe trivariate modes of dependencies in full generality, without introducing additional decomposition axioms or making assumptions about the target/source nature of the variables. By comparing different PID lattices of the same system, we unveil a finer PID structure made of seven nonnegative information subatoms that are invariant to different target/source classifications and that are sufficient to describe the relationships among all PID lattices. This finer structure naturally splits redundant information into two nonnegative components: the source redundancy, which arises from the pairwise correlations between the source variables, and the non-source redundancy, which does not, and relates to the synergistic information the sources carry about the target. The invariant structure is also sufficient to construct the system's entropy, hence it characterizes completely all the interdependencies in the system.

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Section: Potential Implications For Systems Biology and Systems Neuromentioning

confidence: 91%

Invariant Components of Synergy, Redundancy, and Unique Information among Three Variables

Pica

Piasini

Chicharro

et al. 2017

Entropy

View full text Add to dashboard Cite

show abstract

“…To this purpose we exploited the concept of neural manifolds (Gallego et al, 2017) finding a holding subspace where neuronal trajectories of the correct-stop trials, as well in the initial part of no-stop and wrong-stop trials, are confined. The escape of neuronal trajectories from this subspace determines the transition from the prepare-and-hold to the movement generation.…”

Section: Movement Control In a State-space Frameworkmentioning

confidence: 99%

Neuronal population dynamics during motor plan cancellation in non-human primates

Pani

Giamundo

Giarrocco

et al. 2019

Preprint

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To understand the cortical neuronal dynamics behind movement generation and control most studies focused on tasks where actions were planned and then executed, using different instances of visuomotor transformations. However, to fully understand the dynamics related to movement control one must also study how movements are actively inhibited. Inhibition, indeed, represents the first level of control both when different alternatives are available and only one solution could be adopted and when is necessary to maintain the current position.We recorded neuronal activity from a multielectrode array in the dorsal premotor (PMd) cortex of monkeys performing a countermanding reaching task that requires, in a subset of trials, to cancel a potentially planned movement before its onset. In the analysis of the neuronal state-spaces of PMd we found a subspace in which activities conveying temporal information were confined during active inhibition and position holding. Movement execution required activities to escape from the plane toward an orthogonal subspace and, furthermore, surpass a threshold associated to the maturation of the motor plan.These results revealed further details in the neuronal dynamics underlying movement control extending the hypothesis that neuronal computation confined in an output-null subspace does not produce movements.

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“…Our analysis of neural data is based on conceptualizing the collective activity of neurons as a point in a high-dimensional neural state space, where each coordinate is the activity level of one neuron. It has been observed that in many scenarios, the neural state seemed confined to a lower-dimensional region of the neural state space, termed the "neural manifold" [14][15][16][17][18][19][20][21][22][23][24][25] . We analyzed the geometrical structure of the neural manifold by examining two types of state-space directions, defined by the neural encoding and decoding of the above-mentioned task variables as explained below.…”

Section: Introductionmentioning

confidence: 99%

Sequential and efficient neural-population coding of complex task information

Koay

Thiberge

Brody

et al. 2019

Preprint

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Recent work has highlighted that many types of variables are represented in each neocortical area. How can these many neural representations be organized together without interference, and coherently maintained/updated through time? We recorded from large neural populations in posterior cortices as mice performed a complex, dynamic task involving multiple interrelated variables. The neural encoding implied that correlated task variables were represented by uncorrelated modes in an information-coding subspace. We show via theory that this can enable optimal decoding directions to be insensitive to neural noise levels. Across posterior cortex, principles of efficient coding thus applied to task-specific information, with neural-population modes as the encoding unit. Remarkably, this encoding function was multiplexed with rapidly changing, sequential neural dynamics, yet reliably followed slow changes in task-variable correlations through time. We can explain this as due to a mathematical property of high-dimensional spaces that the brain might exploit as a temporal scaffold.

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Neural Manifolds for the Control of Movement

Cited by 529 publications

References 68 publications

Invariant Components of Synergy, Redundancy, and Unique Information among Three Variables

Invariant Components of Synergy, Redundancy, and Unique Information among Three Variables

Neuronal population dynamics during motor plan cancellation in non-human primates

Sequential and efficient neural-population coding of complex task information

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