scholarly journals Multiscale low-dimensional motor cortical state dynamics predict naturalistic reach-and-grasp behavior

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hamidreza Abbaspourazad ◽  
Mahdi Choudhury ◽  
Yan T. Wong ◽  
Bijan Pesaran ◽  
Maryam M. Shanechi
Keyword(s):  
Neural Control ◽  
Multiple Scales ◽  
Network Activity ◽  
Neural Dynamics ◽  
Population Activity ◽  
Reach And Grasp ◽  
Spatiotemporal Scales ◽  
Motor Cortical ◽  
Low Dimensional ◽  
Low Dimensional Dynamics

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.

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

2020 ◽  
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.

scholarly journals A spiral attractor network drives rhythmic locomotion

2017 ◽  
Author(s):  
Angela M. Bruno ◽  
William N. Frost ◽  
Mark D. Humphries
Keyword(s):  
Movement Control ◽  
High Dimensional ◽  
Fictive Locomotion ◽  
Pedal Ganglion ◽  
Joint Activity ◽  
Population Activity ◽  
Neural Populations ◽  
Low Dimensional ◽  
Circuit Function ◽  
Low Dimensional Dynamics

AbstractThe joint activity of neural populations is high dimensional and complex. One strategy for reaching a tractable understanding of circuit function is to seek the simplest dynamical system that can account for the population activity. By imaging Aplysia’s pedal ganglion during fictive locomotion, here we show that its population-wide activity arises from a low-dimensional spiral attractor. Evoking locomotion moved the population into a low-dimensional, periodic, decaying orbit −a spiral −in which it behaved as a true attractor, converging to the same orbit when evoked, and returning to that orbit after transient perturbation. We found the same attractor in every preparation, and could predict motor output directly from its orbit, yet individual neurons’ participation changed across consecutive locomotion bouts. From these results, we propose that only the low-dimensional dynamics for movement control, and not the high-dimensional population activity, are consistent within and between nervous systems.

scholarly journals Prefrontal neural dynamics for behavioral decisions and attentional control

2020 ◽  
Author(s):  
Yaara Erez ◽  
Mikiko Kadohisa ◽  
Philippe Petrov ◽  
Natasha Sigala ◽  
Mark J. Buckley ◽  
...  
Keyword(s):  
Principal Component ◽  
Dimensional Subspace ◽  
Neural Dynamics ◽  
Population Activity ◽  
Appropriate Behavior ◽  
Track Dynamics ◽  
Cell Sample ◽  
Object Coding ◽  
Low Dimensional ◽  
Decision Axis

ABSTRACTComplex neural dynamics in the prefrontal cortex contribute to context-dependent decisions and attentional competition. To analyze these dynamics, we apply demixed principal component analysis to activity of a primate prefrontal cell sample recorded in a cued target detection task. The results track dynamics of cue and object coding, feeding into movements along a target present-absent decision axis in a low-dimensional subspace of population activity. For a single stimulus, object and cue coding are seen mainly in the contralateral hemisphere. Later, a developing decision code in both hemispheres may reflect interhemispheric communication. With a target in one hemifield and a competing distractor in the other, each hemisphere initially encodes the contralateral object, but finally, decision coding is dominated by the task-relevant target. Tracking complex neural events in a low-dimensional activity subspace illuminates information flow towards task-appropriate behavior, unravelling mechanisms of prefrontal computation.

scholarly journals A spiral attractor network drives rhythmic locomotion

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Angela M Bruno ◽  
William N Frost ◽  
Mark D Humphries
Keyword(s):  
Movement Control ◽  
High Dimensional ◽  
Fictive Locomotion ◽  
Pedal Ganglion ◽  
Joint Activity ◽  
Population Activity ◽  
Neural Populations ◽  
Low Dimensional ◽  
Circuit Function ◽  
Low Dimensional Dynamics

The joint activity of neural populations is high dimensional and complex. One strategy for reaching a tractable understanding of circuit function is to seek the simplest dynamical system that can account for the population activity. By imaging Aplysia’s pedal ganglion during fictive locomotion, here we show that its population-wide activity arises from a low-dimensional spiral attractor. Evoking locomotion moved the population into a low-dimensional, periodic, decaying orbit - a spiral - in which it behaved as a true attractor, converging to the same orbit when evoked, and returning to that orbit after transient perturbation. We found the same attractor in every preparation, and could predict motor output directly from its orbit, yet individual neurons’ participation changed across consecutive locomotion bouts. From these results, we propose that only the low-dimensional dynamics for movement control, and not the high-dimensional population activity, are consistent within and between nervous systems.

scholarly journals Predictive learning as a network mechanism for extracting low-dimensional latent space representations

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Stefano Recanatesi ◽  
Matthew Farrell ◽  
Guillaume Lajoie ◽  
Sophie Deneve ◽  
Mattia Rigotti ◽  
...  
Keyword(s):  
Latent Variables ◽  
Latent Structure ◽  
Network Activity ◽  
Semantic Organization ◽  
Sequential Processing ◽  
Predictive Learning ◽  
Neural Representations ◽  
Latent Space ◽  
Low Dimensional ◽  
Sensory Prediction

AbstractArtificial neural networks have recently achieved many successes in solving sequential processing and planning tasks. Their success is often ascribed to the emergence of the task’s low-dimensional latent structure in the network activity – i.e., in the learned neural representations. Here, we investigate the hypothesis that a means for generating representations with easily accessed low-dimensional latent structure, possibly reflecting an underlying semantic organization, is through learning to predict observations about the world. Specifically, we ask whether and when network mechanisms for sensory prediction coincide with those for extracting the underlying latent variables. Using a recurrent neural network model trained to predict a sequence of observations we show that network dynamics exhibit low-dimensional but nonlinearly transformed representations of sensory inputs that map the latent structure of the sensory environment. We quantify these results using nonlinear measures of intrinsic dimensionality and linear decodability of latent variables, and provide mathematical arguments for why such useful predictive representations emerge. We focus throughout on how our results can aid the analysis and interpretation of experimental data.

Rescaling Biology: Increasing Integration Across Biological Scales and Subdisciplines to Enhance Understanding and Prediction

2021 ◽  
Author(s):  
Colette St. Mary ◽  
Thomas H Q Powell ◽  
John S Kominoski ◽  
Emily Weinert
Keyword(s):  
Multiple Scales ◽  
Genomic Variation ◽  
Biological Processes ◽  
Explicit Integration ◽  
Fully Integrated ◽  
Community Or ◽  
Complex Processes ◽  
Spatiotemporal Scales ◽  
Patterns And Processes ◽  
Vast Range

Synopsis The organization of the living world covers a vast range of spatiotemporal scales, from molecules to the biosphere, seconds to centuries. Biologists working within specialized subdisciplines tend to focus on different ranges of scales. Therefore, developing frameworks that enable testing questions and predictions of scaling requires sufficient understanding of complex processes across biological subdisciplines and spatiotemporal scales. Frameworks that enable scaling across subdisciplines would ideally allow us to test hypotheses about the degree to which explicit integration across spatiotemporal scales is needed for predicting the outcome of biological processes. For instance, how does genomic variation within populations allow us to explain community structure? How do the dynamics of cellular metabolism translate to our understanding of whole-ecosystem metabolism? Do patterns and processes operate seamlessly across biological scales, or are there fundamental laws of biological scaling that limit our ability to make predictions from one scale to another? Similarly, can sub-organismal structures and processes be sufficiently understood in isolation of potential feedbacks from the population, community, or ecosystem levels? And can we infer the sub-organismal processes from data on the population, community, or ecosystem scale? Concerted efforts to develop more cross-disciplinary frameworks will open doors to a more fully integrated field of biology. In this paper, we discuss how we might integrate across scales, specifically by (1) identifying scales and boundaries, (2) determining analogous units and processes across scales, (3) developing frameworks to unite multiple scales, and (4) extending frameworks to new empirical systems.

Evidence of low-dimensional dynamics in a traffic noise time series

2001 ◽  
Vol 12 (5) ◽  
pp. 859-864
Author(s):  
V.K. Jain ◽  
A.K. Srivastava ◽  
Anup Das ◽  
Vikas Rai
Keyword(s):  
Time Series ◽  
Traffic Noise ◽  
Low Dimensional ◽  
Low Dimensional Dynamics

Low-dimensional dynamics of a turbulent wall flow

2001 ◽  
Vol 435 ◽  
pp. 81-91 ◽  
Author(s):  
JAVIER JIMÉNEZ ◽  
MARK P. SIMENS
Keyword(s):  
Numerical Simulations ◽  
Single Copy ◽  
Streamwise Vortices ◽  
Low Velocity ◽  
Wall Distance ◽  
Wall Flow ◽  
Low Dimensional ◽  
Turbulent Wall ◽  
Low Dimensional Dynamics ◽  
Masking Function

The low-dimensional dynamics of the structures in a turbulent wall flow are studied by means of numerical simulations. These are made both ‘minimal’, in the sense that they contain a single copy of each relevant structure, and ‘autonomous’ in the sense that there is no outer turbulent flow with which they can interact. The interaction is prevented by a numerical mask that damps the flow above a given wall distance, and the flow behaviour is studied as a function of the mask height. The simplest case found is a streamwise wave that propagates without change. It takes the form of a single wavy low-velocity streak flanked by two counter-rotating staggered quasi-streamwise vortices, and is found when the height of the numerical masking function is less than δ+1 ≈ 50. As the mask height is increased, this solution bifurcates into an almost-perfect limit cycle, a two-frequency torus, weak chaos, and full-edged bursting turbulence. The transition is essentially complete when δ+1 ≈ 70, even if the wall-parallel dimensions of the computational box are small enough for bursting turbulence to be metastable, lasting only for a few bursting cycles. Similar low-dimensional dynamics are found in somewhat larger boxes, containing two copies of the basic structures, in which the bursting turbulence is self-sustaining.

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