Evaluation of memorization brain function using a spatio-temporal artificial neural network (ANN) model

Recent biological experimental findings have announced so, me interesting results concerned with evaluation of brain functions (teaming an it memory). Herein main attention delivered to Construct an ANN model to simulate the activity of transference short term memory (STM) into long term memory (LTM). This memorization function basically depends upon the adaptation and selectivity of synaptic plasticity. That observed as two phenomena of long term potentiation LTP, and long term depression LTD. This paper introduces a modified version model of an over- simplified memorization function model (Grand mother cells network). The proposed model is spatial- temporal and characterized by some biological features such as dynamical adaptation, selectivity and fault tolerance. So, it seems to simulate realistically the role performed by transcription gene factor namely CREB. That as it selects some synapses to be strengthened and neglect others on the basis of LTP and LTD respectively. More over, the effect of forgetting factor on presented model performance is evaluated. Finally, However, the proposed model seems to be will inspired by biology, this paper opens future research work to construct more biologically plausible models based on pulsed neural networks or equivalently spike- timing- dependent- plasticity.