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

Abstract

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.

Ultrasoft and High-Adhesion Block Copolymers for Neuromorphic Computing

“…In contrast to short-term plasticity (STP), long-term plasticity (LTP) plays a pivotal role in memory and learning, instigating enduring changes in synaptic strength. , Prolonged and repetitive stimulation can convert STP into LTP . As depicted in Figure E, a single pulse (−1.5 V, 0.1 s) produced I d at 0.35 times the initial current after 33 s, whereas 10 consecutive pulses yielded an I d at approximately 3.4 times the initial, signifying the shift from STP to LTP.…”

Information bottleneck-based Hebbian learning rule naturally ties working memory and synaptic updates

“…The most common devices are memristors (Yan et al, 2023 ) and resistive memory (Bianchi et al, 2023 ). Less common substrates based on metal-organic transistors (Wang et al, 2023 ) and thermal-guiding structures (Loke et al, 2016 ) exist as well. These devices are designed to mimic various functions of a biological synapse especially its plastic conductance.…”