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 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.

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.

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

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.

scholarly journals Efficient decoding of large-scale neural population responses with Gaussian-process multiclass regression

2021 ◽  
Author(s):  
C. Daniel Greenidge ◽  
Benjamin Scholl ◽  
Jacob Yates ◽  
Jonathan W. Pillow
Keyword(s):  
Gaussian Process ◽  
Large Scale ◽  
Multinomial Logistic Regression ◽  
Neural Population ◽  
High Dimensional ◽  
Neural Decoding ◽  
Population Activity ◽  
Population Codes ◽  
Low Dimensional ◽  
Dimensional Variable

Neural decoding methods provide a powerful tool for quantifying the information content of neural population codes and the limits imposed by correlations in neural activity. However, standard decoding methods are prone to overfitting and scale poorly to high-dimensional settings. Here, we introduce a novel decoding method to overcome these limitations. Our approach, the Gaussian process multi-class decoder (GPMD), is well-suited to decoding a continuous low-dimensional variable from high-dimensional population activity, and provides a platform for assessing the importance of correlations in neural population codes. The GPMD is a multinomial logistic regression model with a Gaussian process prior over the decoding weights. The prior includes hyperparameters that govern the smoothness of each neuron's decoding weights, allowing automatic pruning of uninformative neurons during inference. We provide a variational inference method for fitting the GPMD to data, which scales to hundreds or thousands of neurons and performs well even in datasets with more neurons than trials. We apply the GPMD to recordings from primary visual cortex in three different species: monkey, ferret, and mouse. Our decoder achieves state-of-the-art accuracy on all three datasets, and substantially outperforms independent Bayesian decoding, showing that knowledge of the correlation structure is essential for optimal decoding in all three species.

DETECTING LOW DIMENSIONAL DYNAMICS IN BIOLOGICAL EXPERIMENTS

1996 ◽  
Vol 07 (04) ◽  
pp. 429-435 ◽  
Author(s):  
XING PEI ◽  
FRANK MOSS
Keyword(s):  
Action Potentials ◽  
Sensory System ◽  
Random Processes ◽  
High Dimensional ◽  
Time Intervals ◽  
Dynamical Behaviors ◽  
Caudal Photoreceptor ◽  
The Times ◽  
Low Dimensional ◽  
Low Dimensional Dynamics

We discuss the well-known problems associated with efforts to detect and characterize chaos and other low dimensional dynamics in biological settings. We propose a new method which shows promise for addressing these problems, and we demonstrate its effectiveness in an experiment with the crayfish sensory system. Recordings of action potentials in this system are the data. We begin with a pair of assumptions: that the times of firings of neural action potentials are largely determined by high dimensional random processes or “noise”; and that most biological files are non stationary, so that only relatively short files can be obtained under approximately constant conditions. The method is thus statistical in nature. It is designed to recognize individual “events” in the form of particular sequences of time intervals between action potentials which are the signatures of certain well defined dynamical behaviors. We show that chaos can be distinguished from limit cycles, even when the dynamics is heavily contaminated with noise. Extracellular recordings from the crayfish caudal photoreceptor, obtained while hydrodynamically stimulating the array of hair receptors on the tailfan, are used to illustrate the method.

On-Line Chatter Detection Using Wavelet-Based Parameter Estimation

2003 ◽  
Vol 125 (1) ◽  
pp. 21-28 ◽  
Author(s):  
Taejun Choi ◽  
Yung C. Shin
Keyword(s):  
Parameter Estimation ◽  
Estimation Algorithm ◽  
High Dimensional ◽  
Detection Accuracy ◽  
Chatter Detection ◽  
Ml Estimation ◽  
Fractal Patterns ◽  
On Line ◽  
Low Dimensional ◽  
Low Dimensional Dynamics

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 observation that cutting signals contain fractal patterns, a wavelet-based maximum likelihood (ML) estimation algorithm is applied to 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.

Predicting Time Series from Short-Term High-Dimensional Data

2014 ◽  
Vol 24 (12) ◽  
pp. 1430033 ◽  
Author(s):  
Huanfei Ma ◽  
Tianshou Zhou ◽  
Kazuyuki Aihara ◽  
Luonan Chen
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
High Dimensional Data ◽  
Series Data ◽  
High Dimensional ◽  
Short Term ◽  
Low Dimensional ◽  
Low Dimensional Dynamics ◽  
Term Data

The prediction of future values of time series is a challenging task in many fields. In particular, making prediction based on short-term data is believed to be difficult. Here, we propose a method to predict systems' low-dimensional dynamics from high-dimensional but short-term data. Intuitively, it can be considered as a transformation from the inter-variable information of the observed high-dimensional data into the corresponding low-dimensional but long-term data, thereby equivalent to prediction of time series data. Technically, this method can be viewed as an inverse implementation of delayed embedding reconstruction. Both methods and algorithms are developed. To demonstrate the effectiveness of the theoretical result, benchmark examples and real-world problems from various fields are studied.

scholarly journals Mapping Low-Dimensional Dynamics to High-Dimensional Neural Activity: A Derivation of the Ring Model From the Neural Engineering Framework

2021 ◽  
Vol 33 (3) ◽  
pp. 827-852
Author(s):  
Omri Barak ◽  
Sandro Romani
Keyword(s):  
Neural Network ◽  
Neural Activity ◽  
Failure Modes ◽  
Neural Population ◽  
High Dimensional ◽  
Angular Variable ◽  
Neural Engineering ◽  
Ring Model ◽  
Low Dimensional ◽  
Low Dimensional Dynamics

Empirical estimates of the dimensionality of neural population activity are often much lower than the population size. Similar phenomena are also observed in trained and designed neural network models. These experimental and computational results suggest that mapping low-dimensional dynamics to high-dimensional neural space is a common feature of cortical computation. Despite the ubiquity of this observation, the constraints arising from such mapping are poorly understood. Here we consider a specific example of mapping low-dimensional dynamics to high-dimensional neural activity—the neural engineering framework. We analytically solve the framework for the classic ring model—a neural network encoding a static or dynamic angular variable. Our results provide a complete characterization of the success and failure modes for this model. Based on similarities between this and other frameworks, we speculate that these results could apply to more general scenarios.

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