Self-repair is fundamental to biological neural networks. In a neural network with a large number of elements, the probability of failure of each component increases. With the breakdown of each part, there may be a significant difference in the final results that will be completely affected by this defect. The existence of a process for detecting the error and compensating it by recruiting healthy elements leads to improved performance. This is where adjacent synapses proxy faulty synapses to avoid disturbances in the network function, thereby compensating the incurred error. In the present research, a selfrepairing analog circuit is designed based on an astrocyte-neuron interaction and new synapse architecture. The designed circuit builds upon a software model of an astrocyte-neuron network with the proven ability to detect errors and undertake self-repair. The results obtained from our circuit show that, when an error occurs in the synapses associated with a neuron, the currents within functioning synapses of the same neuron increase. This increase is made by receiving feedback from adjacent astrocytes and other synapses. The process maintains the network function, compensating incurred errors in the network, presenting a neural network-based analog circuit with self-repairing capability, while considering the effect of astrocytes. In this paper, extensive simulation results using HSPICE with 0.35 lm CMOS technology are provided for the evaluation of the proposed circuit.
展开▼
