Toward a Biologically Plausible SNN-Based Associative Memory with Context-Dependent Hebbian Connectivity.

In this paper, we propose a spiking neural network model with Hebbian connectivity for implementing energy-efficient associative memory, whose activity is determined by input stimuli. The model consists of three interacting layers of Hodgkin-Huxley-Mainen spiking neurons with excitatory and inhibitory synaptic connections. Information patterns are stored in memory using a symmetric Hebbian matrix and can be retrieved in response to a specific stimulus pattern. Binary images are encoded using in-phase and anti-phase oscillations relative to a global clock signal. Utilizing the phase-locking effect allows for cluster synchronization of neurons (both on the input and output layers). Interneurons in the intermediate layer filter signal propagation pathways depending on the context of the input layer, effectively engaging only a portion of the synaptic connections within the Hebbian matrix for recognition. The stability of the oscillation phase is investigated for both in-phase and anti-phase synchronization modes when recognizing direct and inverse images. This context-dependent effect opens promising avenues for the development of analog hardware circuits for energy-efficient neurocomputing applications, potentially leading to breakthroughs in artificial intelligence and cognitive computing.