Pattern Separation in a Spiking Neural Network of Hippocampus Robust to Imbalanced Excitation/Inhibition

TL;DR

This paper presents a novel spiking neural network model inspired by the hippocampus that effectively performs pattern separation despite imbalanced excitation and inhibition, demonstrating robustness to network damage.

Contribution

The study introduces a new neural network model with simplified cellular mechanisms that maintains pattern separation under excitation-inhibition imbalance and damage.

Findings

The model achieves efficient pattern separation across various stimulation levels.

It demonstrates robustness to synaptic and connectivity damage.

The approach can be applied in cognitive robotics.

Abstract

Efficient pattern separation in dentate gyrus plays an important role in storing information in the hippocampus. Current knowledge of the structure and function of the hippocampus, entorhinal cortex and dentate gyrus, in pattern separation are incorporated in our model. A three-layer feedforward spiking neural network inspired by the rodent hippocampus an equipped with simplified synaptic and molecular mechanisms is developed. The aim of the study is to make an spiking neural network capable of pattern separation in imbalanced excitation/inhibition ratios caused by different levels of stimulations or network damage. This work present a novel theory on the cellular mechanisms of robustness to damges to synapses and connectivity of neurons in dentate gyrus that results in imbalanced excitation-inhibition activity of neurons. This spiking neural network uses simplified molecular and cellular hypothetical mechanisms and demonstrates efficient storing of information in different levels of stimulation and can be implemented in cognitive robotics.