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

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Published on: March 2, 2015

Neural heterogeneity controls computations in spiking neural networks.

Richard Gast^1,2, Sara A Solla¹, Ann Kennedy^1,2

¹Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611.

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

|January 10, 2024

Summary

Neural heterogeneity in the brain significantly impacts computation. Varying spike thresholds in neurons allows for flexible neural circuit tuning, enhancing signal gating, encoding, and decoding for diverse computational tasks.

Keywords:

heterogeneity mean-field models neural computation neural dynamics recurrent neural networks

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Area of Science:

Computational neuroscience
Neural network dynamics
Systems neuroscience

Background:

The brain's complex neural networks exhibit significant heterogeneity in neuronal physiology and spiking.
Understanding how this heterogeneity influences macroscopic neural dynamics and computation is crucial.

Purpose of the Study:

To investigate the role of neural heterogeneity in computational functions using a mean-field model.
To analyze how spike threshold heterogeneity affects signal gating, encoding, and decoding in neural populations.

Main Methods:

Utilized a mean-field modeling approach to simulate neural networks.
Examined the impact of varying spike threshold heterogeneity in both excitatory and inhibitory neurons.

Main Results:

Heterogeneity in inhibitory interneurons enables effective gating of neural signals and preserves excitatory neuron function.
Heterogeneity in excitatory neurons enhances neural dynamics dimensionality, improving decoding capabilities.
Homogeneous networks show limitations in function generation but excel at signal encoding through multistable dynamics.

Conclusions:

Intra-cell-type heterogeneity acts as a mechanism to sculpt computational properties of neural circuits.
Canonical microcircuits can be tuned for diverse computational tasks by modulating neuronal heterogeneity.