Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 33, Issue 12, Pages 7621-7631Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2021.3085966
Keywords
Neurons; Biology; Convolution; Biological neural networks; Tuning; Membrane potentials; Kernel; Biologically plausible computing; neuronal dynamics; reward propagation; spiking neural network (SNN)
Categories
Funding
- National Key Research and Development Program of China [2020AAA0104305]
- National Natural Science Foundation of China [61806195]
- Strategic Priority Research Program of the Chinese Academy of Sciences [XDB32070100, XDA27010404]
- Beijing Brain Science Project [Z181100001518006]
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Spiking neural networks (SNNs) are considered the third generation of artificial neural networks (ANNs) and have more biologically realistic structures. To address the dynamic characteristics of SNNs, a biologically plausible reward propagation (BRP) algorithm is proposed for training SNNs, which has shown comparable accuracy to state-of-the-art BP-based SNNs while saving computational cost. This approach provides insights toward a better understanding of the intelligent nature of biological systems.
Spiking neural networks (SNNs) contain more biologically realistic structures and biologically inspired learning principles than those in standard artificial neural networks (ANNs). SNNs are considered the third generation of ANNs, powerful on the robust computation with a low computational cost. The neurons in SNNs are nondifferential, containing decayed historical states and generating event-based spikes after their states reaching the firing threshold. These dynamic characteristics of SNNs make it difficult to be directly trained with the standard backpropagation (BP), which is also considered not biologically plausible. In this article, a biologically plausible reward propagation (BRP) algorithm is proposed and applied to the SNN architecture with both spiking-convolution (with both 1-D and 2-D convolutional kernels) and full-connection layers. Unlike the standard BP that propagates error signals from postsynaptic to presynaptic neurons layer by layer, the BRP propagates target labels instead of errors directly from the output layer to all prehidden layers. This effort is more consistent with the top-down reward-guiding learning in cortical columns of the neocortex. Synaptic modifications with only local gradient differences are induced with pseudo-BP that might also be replaced with the spike-timing-dependent plasticity (STDP). The performance of the proposed BRP-SNN is further verified on the spatial (including MNIST and Cifar-10) and temporal (including TIDigits and DvsGesture) tasks, where the SNN using BRP has reached a similar accuracy compared to other state-of-the-art (SOTA) BP-based SNNs and saved 50% more computational cost than ANNs. We think that the introduction of biologically plausible learning rules to the training procedure of biologically realistic SNNs will give us more hints and inspiration toward a better understanding of the biological system's intelligent nature.
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