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Updated: Jun 28, 2025
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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
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Chaotic neural dynamics facilitate probabilistic computations through sampling
Yu Terada1,2,3, Taro Toyoizumi1,4
1Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, Saitama 351-0198, Japan.
Summary
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.
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.
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