We present MEMprop, the adoption of gradient-based learning to train fully
memristive spiking neural networks (MSNNs). Our approach harnesses intrinsic
device dynamics to trigger naturally arising voltage spikes. These spikes
emitted by memristive dynamics are analog in nature, and thus fully
differentiable, which eliminates the need for surrogate gradient methods that
are prevalent in the spiking neural network (SNN) literature. Memristive neural
networks typically either integrate memristors as synapses that map
offline-trained networks, or otherwise rely on associative learning mechanisms
to train networks of memristive neurons. We instead apply the backpropagation
through time (BPTT) training algorithm directly on analog SPICE models of
memristive neurons and synapses. Our implementation is fully memristive, in
that synaptic weights and spiking neurons are both integrated on resistive RAM
(RRAM) arrays without the need for additional circuits to implement spiking
dynamics, e.g., analog-to-digital converters (ADCs) or thresholded comparators.
As a result, higher-order electrophysical effects are fully exploited to use
the state-driven dynamics of memristive neurons at run time. By moving towards
non-approximate gradient-based learning, we obtain highly competitive accuracy
amongst previously reported lightweight dense fully MSNNs on several
benchmarks.

采用 MEMprop 算法的梯度学习方法，通过基于模拟 SPICE 模型的脉冲神经元和突触直接应用 BPTT 训练算法，实现了全电子脉冲神经网络的训练，省略了寻找峰值的操作，进而提高精度。