Synaptic Plasticity as Agent Communication

Neurons are considered as main computational units of the human brain, are working together with millions of synapses to convey information. The processes of information decoding and neurons’ communication mechanisms are still in a debate. Apart from the numerous researches into those areas, significant attention has given to the synaptic plasticity, which is suspected to have direct relationship with information processing of neurons. As per the biology, synaptic computation can be mainly divided into three plasticity processes, homeostasis, short-term and long-term. The long-term plasticity is considered as the main phenomena related to learning and memory formation; the roles of short-term plasticity and homeostasis plasticity have direct influences to synaptic efficacy and thereby to long-term plasticity. A few researches are being carried out to in cooperate the homeostasis plasticity to Artificial Neural Networks, are still unable to find real integrated mechanism without damaging to learning process. This paper proposes a new model for synaptic computation. In our approach, we understand the neurons as agents consisting of large number of constituent agents those play the roles of synapses, as transmitters or receivers. The statuses of these constituent agents are subjected to homeostasis and short-term plasticity. The number of active transmitters is an in-parameter for the learning processes. With the proposed model, through the active number of transmitters, learning can be explained as integrated process of three plasticity processes.