  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.
