scholarly journals Interactions of encoding and decoding problems to understand motor control

2019 ◽  
Author(s):  
Shixian Wen ◽  
Allen Yin ◽  
Li Zheng ◽  
Laurent Itti
Keyword(s):  
Motor Control ◽  
Spike Trains ◽  
Data Driven ◽  
Computer Interface ◽  
Neural Dynamics ◽  
Neural Data ◽  
Useful Knowledge ◽  
Data Encoding ◽  
Generative Encoding ◽  
Previous Subject

AbstractLearning a map from movement to neural data (Encoding Problem) and vice versa (Decoding Problem) are crucial to understanding motor control. A principled encoding model that understands underlying neural dynamics can help better solve the decoding problem. Here, we develop a new generative encoding model leveraging deep learning that autonomously captures neural dynamics. After training, the model can synthesize spike trains given any observed kinematics, under the guidance of the learned neural dynamics. When neural data from other sessions or subjects are limited, synthesized spike trains can improve cross-session and cross-subject decoding performance of a Brain Computer Interface decoder. For cross-subject, even with ample data for both subjects, neural dynamics learned from a previous subject can transfer useful knowledge that improves the best achievable decoding performance for the new subject. The approach is general and fully data-driven, and hence could apply to neuroscience encoding and decoding problems beyond motor control.

scholarly journals Effortless Retaliation: the Neural Dynamics of Interpersonal Intentions in a Chicken Game Using Brain-Computer Interface

2021 ◽  
Author(s):  
Yiwen Wang ◽  
Yuxiao Lin ◽  
Chao Fu ◽  
Zhihua Huang ◽  
Rongjun Yu ◽  
...  
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Neural Mechanisms ◽  
Computer Interface ◽  
Neural Dynamics ◽  
Neural Signals ◽  
Chicken Game ◽  
Common Response ◽  
Tit For Tat ◽  
Novel Method

Abstract The desire for retaliation is a common response across a majority of human societies. However, the neural mechanisms underlying aggression and retaliation remain unclear. Previous studies on social intentions are confounded by low-level response related brain activity. Using an EEG-based brain-computer interface (BCI) combined with the Chicken Game, our study examined the neural dynamics of aggression and retaliation after controlling for nonessential response related neural signals. Our results show that aggression is associated with reduced alpha event-related desynchronization (ERD), indicating reduced mental effort. Moreover, retaliation and tit-for-tat strategy use are also linked with smaller alpha-ERD. Our study provides a novel method to minimize motor confounds and demonstrates that choosing aggression and retaliation is less effortful in social conflicts.

scholarly journals Data-driven filtration and segmentation of mesoscale neural dynamics

2021 ◽  
Author(s):  
Sydney C. Weiser ◽  
Brian R. Mullen ◽  
Desiderio Ascencio ◽  
James B. Ackman
Keyword(s):  
Time Series ◽  
Information Flow ◽  
Background Noise ◽  
Activity Patterns ◽  
Data Driven ◽  
Neural Dynamics ◽  
Cortical Surface ◽  
Cerebral Function ◽  
Spatial Segmentation ◽  
Video Recordings

Recording neuronal group activity across the cortical hemispheres from awake, behaving mice is essential for understanding information flow across cerebral networks. Video recordings of cerebral function comes with challenges, including optical and movement-associated vessel artifacts, and limited references for time series extraction. Here we present a data-driven workflow that isolates artifacts from calcium activity patterns, and segments independent functional units across the cortical surface. Independent Component Analysis utilizes the statistical interdependence of pixel activation to completely unmix signals from background noise, given sufficient spatial and temporal samples. We also utilize isolated signal components to produce segmentations of the cortical surface, unique to each individual’s functional patterning. Time series extraction from these maps maximally represent the underlying signal in a highly compressed format. These improved techniques for data pre-processing, spatial segmentation, and time series extraction result in optimal signals for further analysis.

The Neural Dynamics Approach to Sensory-Motor Control

1997 ◽  
pp. 153-194 ◽  
Author(s):  
Paolo Gaudiano ◽  
Frank H. Guenther ◽  
Eduardo Zalama
Keyword(s):  
Motor Control ◽  
Neural Dynamics ◽  
Sensory Motor

Investigation of the Neural Dynamics of Human Motor Learning Using an Intracortical Brain Computer Interface

2017 ◽  
Vol 98 (12) ◽  
pp. e163
Author(s):  
David Lin ◽  
Marco Vilela ◽  
David Brandman ◽  
Tommy Hosman ◽  
Jad Saab ◽  
...  
Keyword(s):  
Motor Learning ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Neural Dynamics ◽  
Human Motor

Robust Common Spatial Filters with a Maxmin Approach

2014 ◽  
Vol 26 (2) ◽  
pp. 349-376 ◽  
Author(s):  
Motoaki Kawanabe ◽  
Wojciech Samek ◽  
Klaus-Robert Müller ◽  
Carmen Vidaurre
Keyword(s):  
State Of The Art ◽  
Minimum Variance ◽  
Covariance Matrices ◽  
Data Driven ◽  
Computer Interface ◽  
Variance Ratio ◽  
Spatial Filters ◽  
Data Set ◽  
Data Driven Approach ◽  
World Brain

Electroencephalographic signals are known to be nonstationary and easily affected by artifacts; therefore, their analysis requires methods that can deal with noise. In this work, we present a way to robustify the popular common spatial patterns (CSP) algorithm under a maxmin approach. In contrast to standard CSP that maximizes the variance ratio between two conditions based on a single estimate of the class covariance matrices, we propose to robustly compute spatial filters by maximizing the minimum variance ratio within a prefixed set of covariance matrices called the tolerance set. We show that this kind of maxmin optimization makes CSP robust to outliers and reduces its tendency to overfit. We also present a data-driven approach to construct a tolerance set that captures the variability of the covariance matrices over time and shows its ability to reduce the nonstationarity of the extracted features and significantly improve classification accuracy. We test the spatial filters derived with this approach and compare them to standard CSP and a state-of-the-art method on a real-world brain-computer interface (BCI) data set in which we expect substantial fluctuations caused by environmental differences. Finally we investigate the advantages and limitations of the maxmin approach with simulations.

scholarly journals Training deep neural density estimators to identify mechanistic models of neural dynamics

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Pedro J Gonçalves ◽  
Jan-Matthis Lueckmann ◽  
Michael Deistler ◽  
Marcel Nonnenmacher ◽  
Kaan Öcal ◽  
...  
Keyword(s):  
Receptive Fields ◽  
Rhythmic Activity ◽  
Mechanistic Modeling ◽  
Neural Dynamics ◽  
Model Parameters ◽  
Initial Training ◽  
Neural Data ◽  
Compensation Mechanisms ◽  
Underlying Causes ◽  
Density Estimators

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators—trained using model simulations—to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin–Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics.

Sign in / Sign up

Export Citation Format

Share Document