A digital neuromorphic system for working memory based on spiking neuron-astrocyte network

Among various types of memory, working memory (WM) plays a crucial role in reasoning, decision-making, and behavior regulation. Neuromorphic computing is a well-established engineering approach that offers promising avenues for advancing our understanding of WM processes by mimicking the structure and operation of the human brain using electronic technology. In this work, a digital neuromorphic system is proposed and then implemented in hardware to illustrate the real-time WM process based on the spiking neuron-astrocyte network (SNAN). The implemented SNAN utilizes a bidirectional neuron-astrocyte interaction to realize the WM process, allowing for a more brain-like memory emulation. Various hardware optimization methods, including piecewise linear approximation, double buffering, and time multiplexing are recruited to minimize the area and power consumption and facilitate the implementation of the WM concept on a single field programmable gate array (FPGA) chip. The proposed neuromorphic system is evaluated by testing its capacity for multi-item memory formation, an essential characteristic of human WM. The results show that the time duration between the store and recall phases is a critical parameter for acceptable retrieval performance. Additionally, the results demonstrate that the proposed neuromorphic system for WM is resilient to noise. Finally, the design modularity of the system facilitates easy extension for implementing larger networks and adapting to real-world applications.