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Updated: Jun 28, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Chaotic neural dynamics facilitate probabilistic computations through sampling.

Yu Terada^1,2,3, Taro Toyoizumi^1,4

¹Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, Saitama 351-0198, Japan.

Proceedings of the National Academy of Sciences of the United States of America

|April 22, 2024

Summary

This summary is machine-generated.

Chaotic neural dynamics, learned through synaptic plasticity, enable recurrent neural networks to perform sensory integration. This chaotic activity models brain function as a Bayesian generative model, explaining neural variability.

Keywords:

Bayesian computation chaos computational neuroscience cue integration recurrent neural networks

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Area of Science:

Computational neuroscience
Neural dynamics
Bayesian inference

Background:

Cortical neurons display significant response variability across trials and time.
This variability is theoretically linked to chaotic dynamics in recurrent neural networks.
Understanding the computational basis of this variability is crucial for neuroscience.

Purpose of the Study:

To demonstrate that chaotic neural dynamics, induced by synaptic learning, facilitate sensory cue integration.
To explore how these dynamics support sampling-based computations for static and dynamic variables.
To investigate the role of spontaneous activity in representing priors and computing marginal distributions.

Main Methods:

Utilized recurrent neural networks with biologically plausible synaptic learning rules.
Simulated network dynamics to observe emergent chaotic behavior.
Assessed the networks' ability to perform sensory cue integration and inference tasks.
Analyzed spontaneous activity for representational content.

Main Results:

Emergent chaotic dynamics were successfully induced through synaptic learning.
Networks demonstrated effective sensory cue integration using a sampling-based approach.
Chaotic dynamics enabled the generation of samples for both static and dynamic variables.
Networks generalized learned stimulus-evoked samples for inference, even with incomplete sensory information.

Conclusions:

Chaotic neural dynamics provide a substrate for sampling-based sensory integration and inference.
Learned chaotic dynamics can implement a Bayesian generative model in neural networks.
Spontaneous activity in chaotic networks may represent priors and facilitate computation of marginal distributions.
This work offers a computational framework for understanding neural variability and brain function.