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 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 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 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 Finite-dimensional boundary uniform stabilization of the Boussinesq system in Besov spaces by critical use of Carleman estimate-based inverse theory

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Irena Lasiecka ◽  
Buddhika Priyasad ◽  
Roberto Triggiani
Keyword(s):  
Besov Space ◽  
Internal Control ◽  
Initial Conditions ◽  
Fluid Velocity ◽  
Critical Role ◽  
Boussinesq System ◽  
Heat Equations ◽  
Fluid Equation ◽  
Low Regularity ◽  
Finite Dimensional

Abstract We consider the 𝑑-dimensional Boussinesq system defined on a sufficiently smooth bounded domain and subject to a pair { v , u } \{v,\boldsymbol{u}\} of controls localized on { Γ ~ , ω } \{\widetilde{\Gamma},\omega\} . Here, 𝑣 is a scalar Dirichlet boundary control for the thermal equation, acting on an arbitrarily small connected portion Γ ~ \widetilde{\Gamma} of the boundary Γ = ∂ ⁡ Ω \Gamma=\partial\Omega . Instead, 𝒖 is a 𝑑-dimensional internal control for the fluid equation acting on an arbitrarily small collar 𝜔 supported by Γ ~ \widetilde{\Gamma} . The initial conditions for both fluid and heat equations are taken of low regularity. We then seek to uniformly stabilize such Boussinesq system in the vicinity of an unstable equilibrium pair, in the critical setting of correspondingly low regularity spaces, by means of an explicitly constructed, finite-dimensional feedback control pair { v , u } \{v,\boldsymbol{u}\} localized on { Γ ~ , ω } \{\widetilde{\Gamma},\omega\} . In addition, they will be minimal in number and of reduced dimension; more precisely, 𝒖 will be of dimension ( d - 1 ) (d-1) , to include necessarily its 𝑑-th component, and 𝑣 will be of dimension 1. The resulting space of well-posedness and stabilization is a suitable, tight Besov space for the fluid velocity component (close to L 3 ⁢ ( Ω ) \boldsymbol{L}^{3}(\Omega) for d = 3 d=3 ) and a corresponding Besov space for the thermal component, q > d q>d . Unique continuation inverse theorems for suitably over-determined adjoint static problems play a critical role in the constructive solution. Their proof rests on Carleman-type estimates, a topic pioneered by M. V. Klibanov since the early 80s.

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.

On-Line Chatter Detection Using Wavelet-Based Parameter Estimation

2000 ◽  
Author(s):  
Taejun Choi ◽  
Yung C. Shin
Keyword(s):  
Parameter Estimation ◽  
Estimation Algorithm ◽  
Cutting Process ◽  
High Dimensional ◽  
Detection Accuracy ◽  
Chatter Detection ◽  
Ml Estimation ◽  
On Line ◽  
Low Dimensional ◽  
Low Dimensional Dynamics

Abstract A new method for on-line chatter detection is presented. The proposed method characterizes the significant transition from high dimensional to low dimensional dynamics in the cutting process at the onset of chatter. Based on the likeness of the cutting process to the nearly-1/f process, this wavelet-based maximum likelihood (ML) estimation algorithm is applied for on-line chatter detection. The presented chatter detection index γ is independent of the cutting conditions and gives excellent detection accuracy and permissible computational efficiency, which makes it suitable for on-line implementation. The validity of the proposed method is demonstrated through the tests with extensive actual data obtained from turning and milling processes.

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.

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