Memristor-Based Conditioned Inhibition Neural Network Circuit With Blocking Generalization and Differentiation

Signaling activity in the cerebral cortex is corrected by inhibition and blocking to make neural response processes more precise and efficient, inhibition and blocking are positive neural processes. In this article, a memristor-based conditioned inhibition neural network circuit with blocking generalization and differentiation is proposed. The designed circuit consists of the voltage control module, synapse neuron module, inhibition module, blocking module, and generalization module. Through the cooperative action of the neuronal associative learning and the inhibition module, the neutral conditioned stimulus is transformed into a conditioned stimulus with inhibitory properties to achieve conditioned inhibition. The influence of unconditioned stimulus on synaptic weight is considered during associative learning, and unblocking is realized by using the blocking module. Based on the blocking module and the generalization module, blocking, generalization, and differentiation are combined to realize blocking generalization and differentiation. The PSPICE simulation verifies the feasibility of the above functions. Conditioned inhibition neural network circuit with blocking generalization and differentiation provides a reference for the further development of brain-like technology.