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Population adaptation in efficient balanced networks.

Gabrielle J Gutierrez^1,2, Sophie Denève²

¹Department of Applied Mathematics, University of Washington, Seattle, United States.

|September 25, 2019

Summary

This summary is machine-generated.

Neurons maintain stable sensory representations by optimizing efficiency across a population, not individually. This population-level adaptation balances neural activity costs and accuracy, ensuring reliable stimulus representation.

Keywords:

computational biology efficient encoding neural tuning neuroscience none perception spike-frequency adaptation systems biology visual encoding

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Area of Science:

Neuroscience
Computational Neuroscience
Systems Neuroscience

Background:

Sensory neurons exhibit adaptation, a phenomenon crucial for efficient neural coding.
The mechanisms by which neurons maintain stable stimulus representations despite history-dependent responses are not fully understood.

Purpose of the Study:

To investigate how neural populations maintain stable representations of external stimuli.
To explore the role of population-level efficiency optimization in neural coding.

Main Methods:

Developed a computational framework modeling neural network adaptation.
Analyzed the interplay between spike-frequency adaptation, E/I balance, and recurrent connectivity.
Simulated an orientation coding network to reconcile neural and behavioral data.

Main Results:

Neural network efficiency optimized at the population level ensures stable stimulus representation.
Spike-frequency adaptation and E/I balanced recurrent connectivity arise from a global cost-accuracy tradeoff.
Sensory responses are redistributed within the network, maintaining population-level representation despite individual neuron dynamics.

Conclusions:

Population-level optimization is key to stable neural representations.
Adaptation mechanisms balance metabolic cost and representational accuracy.
This framework unifies diverse neural adaptation phenomena and explains reliable sensory coding.