Weak electric fields detectability in a noisy neural network.

AI: Melanie Research RF Safe Research Library Jan 1, 2017 NEUTRAL LOW

This modeling study examines how a noisy spiking neural network detects a weak applied electric field using an Izhikevich neuron framework with excitatory/inhibitory neurons and conduction delays. The authors report stochastic resonance driven by white noise that enhances detectability, and that this effect largely vanishes when network connections are removed. They also report that multiple network parameters shape detectability and that the model exhibits an optimal sensitivity region.

Key points

The work is a computational neural network study based on the Izhikevich neuron model.
Noise intensity is reported to modulate detectability of a weak electric field signal.
White-noise-induced stochastic resonance is reported when the weak electric field is applied.
Removing synaptic connections reportedly nearly eliminates the stochastic resonance effect.
Connection probability and synaptic coupling strength are reported to affect detectability.
Population size and neuron heterogeneity are reported to influence detectability.
An optimal parameter region for model sensitivity/detectability is reported.

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