Abstract
Spiking neural network, inspired by the human brain, consisting of spiking neurons and plastic synapses, is a promising solution for highly efficient data processing in neuromorphic computing. Recently, memristor-based neurons and synapses are becoming intriguing candidates to build spiking neural networks in hardware, owing to the close resemblance between their device dynamics and the biological counterparts. However, the functionalities of memristor-based neurons are currently very limited, and a hardware demonstration of fully memristor-based spiking neural networks supporting in-situ learning is very challenging. Here, a hybrid spiking neuron combining a memristor with simple digital circuits is designed and implemented in hardware to enhance neuron functions. The hybrid neuron with memristive dynamics not only realizes the basic leaky integrate-and-fire neuron function but also enables the in-situ tuning of the connected synaptic weights. Finally, a fully hardware spiking neural network with the hybrid neurons and memristive synapses is experimentally demonstrated for the first time, and in-situ Hebbian learning is achieved with this network. This work opens up a way towards the implementation of spiking neurons, supporting in-situ learning for future neuromorphic computing systems.
Highlights
Inspired by the human brain, the spiking neural networks (SNNs) encode timing signals into the computing process, where the spike-based temporal processing allows sparse and efficient information transfer, conversion, and storage[1,2]
The axon-terminals and dendrite-terminals form the synapses whose strength dictates the intensity of the signal passing from the preneurons to the post-neurons
The synaptic weight can be in situ modified according to the relative timing of pre- and postsynaptic spikes (spike-timing-dependent plasticity (STDP) learning rule)[36,37,38], which is believed to be one of the key mechanisms for organisms to learn and dynamically adapt to the external environment
Summary
Inspired by the human brain, the spiking neural networks (SNNs) encode timing signals into the computing process, where the spike-based temporal processing allows sparse and efficient information transfer, conversion, and storage[1,2]. When the TS device fires, the output signal from L1 activates the depression module and lifts the potential of node 1 (see Supplementary Fig. 3), depressing the synapses whose inputs are zero.
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