Synaptic transistor with multiple biological functions based on metal-organic frameworks combined with the LIF model of a spiking neural network to recognize temporal information

Spiking neural networks (SNNs) have immense potential due to their utilization of synaptic plasticity and ability to take advantage of temporal correlation and low power consumption. The leaky integration and firing (LIF) model and spike-timing-dependent plasticity (STDP) are the fundamental components of SNNs. Here, a neural device is first demonstrated by zeolitic imidazolate frameworks (ZIFs) as an essential part of the synaptic transistor to simulate SNNs. Significantly, three kinds of typical functions between neurons, the memory function achieved through the hippocampus, synaptic weight regulation and membrane potential triggered by ion migration, are effectively described through short-term memory/long-term memory (STM/LTM), long-term depression/long-term potentiation (LTD/LTP) and LIF, respectively. Furthermore, the update rule of iteration weight in the backpropagation based on the time interval between presynaptic and postsynaptic pulses is extracted and fitted from the STDP. In addition, the postsynaptic currents of the channel directly connect to the very large scale integration (VLSI) implementation of the LIF mode that can convert high-frequency information into spare pulses based on the threshold of membrane potential. The leaky integrator block, firing/detector block and frequency adaptation block instantaneously release the accumulated voltage to form pulses. Finally, we recode the steady-state visual evoked potentials (SSVEPs) belonging to the electroencephalogram (EEG) with filter characteristics of LIF. SNNs deeply fused by synaptic transistors are designed to recognize the 40 different frequencies of EEG and improve accuracy to 95.1%. This work represents an advanced contribution to brain-like chips and promotes the systematization and diversification of artificial intelligence.

Automated summary

Spiking neural networks (SNNs) show great potential in utilizing synaptic plasticity and temporal correlation to achieve low power consumption. In this study, a neural device based on zeolitic imidazolate frameworks (ZIFs) is demonstrated as an essential part of simulating SNNs. The functions between neurons, including memory achieved through the hippocampus, synaptic weight regulation, and membrane potential triggered by ion migration, are effectively described using short-term memory/long-term memory (STM/LTM), long-term depression/long-term potentiation (LTD/LTP), and leaky integration and firing (LIF) model. Furthermore, the update rule of iteration weight in backpropagation based on spike-timing-dependent plasticity (STDP) is extracted. The integration of synaptic transistors in SNNs greatly enhances the recognition accuracy of different frequencies of electroencephalogram (EEG) signals, contributing to the advancement of brain-like chips and the diversification of artificial intelligence.