Faramarz Faghihi, Homa Samani and Ahmed A Moustafa
University of Gottingen, Germany
University of Isfahan, Iran
Western Sydney University, Australia
Posters & Accepted Abstracts: J Neurol Neurophysiol
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 a spiking neural network capable of pattern separation in imbalanced excitation/inhibition ratios caused by different levels of stimulations or network damage. This work presents a novel theory on the cellular mechanisms of robustness to damages to synapses and connectivity of neurons in dentate gyrus those 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.
