2D Heterostructure for High‐Order Spatiotemporal Information Processing

Abstract

The Bienenstock, Cooper, and Munro (BCM) theory of synaptic plasticity is regarded as the most precise model of the synapse, and is more compatible with neuromorphic computing. However, the development in BCM synaptic modification is rather limited since the memristive devices used to emulate the BCM lack tunable forgetting rate. Compared with memristors, memtransistors provide another gate-tunable freedom degree, which will help to modulate the forgetting rate. In this work, the authors demonstrate a perfect BCM learning rule based on the 2D heterostructure memtransistor through using triplet-spike timing dependent plasticity model. Two critical characteristics of the BCM rule, sliding frequency threshold and enhanced depression effect, are perfectly presented due to their spontaneous/gate-assistant forgetting effect. The experimental results are extremely consistent with the BCM learning rule and suggest the potential application of 2D memtransistors in high-order spatiotemporal recognition.

Stretchable Temperature-Responsive Multimodal Neuromorphic Electronic Skin with Spontaneous Synaptic Plasticity Recovery

“…Synaptic plasticity of neural system reflects the adjustable character of synaptic strength that connects nerve cells . It is believed to be the biological basis of cellular learning and memory, and is essential for the proper maintenance of synaptic function . Short-term plasticity can modulate the balance between cortical excitation and inhibition, forming temporal and spatial features of neural activity .…”

“…31 It is believed to be the biological basis of cellular learning and memory, and is essential for the proper maintenance of synaptic function. 32 Short-term plasticity can modulate the balance between cortical excitation and inhibition, forming temporal and spatial features of neural activity. 33 EPSC and IPSC are emulated in the artificial synapse by applying separate negative and positive spikes to the gate (Figure 2, panels a and b), respectively.…”

Memristive, Spintronic, and 2D‐Materials‐Based Devices to Improve and Complement Computing Hardware

“…In fact, several mechanisms governing resistive switching in memtransistors have been demonstrated, such as grain boundary migration, [112] FE switching, [118] and gate-controlled vdW heterojunctions. [119] Of course, with any of these technologies, due to the analog nature of computations, the idealized vector-matrix computation in Figure 5a is often difficult to achieve. First, it may be challenging to set devices to the desired values of conductances G i,j .…”

“…In fact, several mechanisms governing resistive switching in memtransistors have been demonstrated, such as grain boundary migration, [ 111 ] FE switching, [ 117 ] and gate‐controlled vdW heterojunctions. [ 118 ]…”

Back‐End‐of‐Line SiC‐Based Memristor for Resistive Memory and Artificial Synapse

“…[2] These include technologies such as ferroelectric memory, [3] phase change memory, [4] flash memory, [5] ionic transistor, [6] and memtransistor. [7] Compared with these techno logies, the two-terminal memristor is widely accepted to be the most promising candidate electronic device to mimic the synapse in human brain. [8][9][10][11][12] Memristor is defined as the 2-terminal resistive switching memory device where the resistance can be switched between high and low states via the application of electrical pulses.…”