Asynchronous eventdriven computation and communication using spikes facilitate the realization of spiking neural networks (SNN) to be massively parallel, extremely energy efficient and highly robust on specialized neuromorphic hardware. However, the lack of a unified robust learning algorithm limits the SNN to shallow networks with low accuracies. Artificial neural networks (ANN), however, have the backpropagation algorithm which can utilize gradient descent to train networks which are locally robust universal function approximators. But backpropagation algorithm is neither biologically plausible nor neuromorphic implementation friendly because it requires: 1) separate backward and forward passes, 2) differentiable neurons, 3) highprecision propagated errors, 4) coherent copy of weight matrices at feedforward weights and the backward pass, and 5) nonlocal weight update. Thus, we propose an approximation of the backpropagation algorithm completely with spiking neurons and extend it to a local weight update rule which resembles a biologically plausible learning rule spiketimingdependent plasticity (STDP). This will enable error propagation through spiking neurons for a more biologically plausible and neuromorphic implementation friendly backpropagation algorithm for SNNs. We test the proposed algorithm on various traditional and nontraditional benchmarks with competitive results.
Two Routes to Scalable Credit Assignment without Weight Symmetry, International Conference on Machine
The neural plausibility of backpropagation has long been disputed, primarily for its use of nonlocal weight transport — the biologically dubious requirement that one neuron instantaneously measure the synaptic weights of another. Until recently, attempts to create local learning rules that avoid weight transport have typically failed in the largescale learning scenarios where backpropagation shines, e.g. ImageNet categorization with deep convolutional networks. Here, we investigate a recently proposed local learning rule that yields competitive performance with backpropagation and find that it is highly sensitive to metaparameter choices, requiring laborious tuning that does not transfer across network architecture. Our analysis indicates the underlying mathematical reason for this instability, allowing us to identify a more robust local learning rule that better transfers without metaparameter tuning. Nonetheless, we find a performance and stability gap between this local rule and backpropagation that widens with increasing model depth. We then investigate several nonlocal learning rules that relax the need for instantaneous weight transport into a more biologicallyplausible "weight estimation" process, showing that these rules match stateoftheart performance on deep networks and operate effectively in the presence of noisy updates. Taken together, our results suggest two routes towards the discovery of neural implementations for credit assignment more »
 Award ID(s):
 1845166
 Publication Date:
 NSFPAR ID:
 10291295
 Journal Name:
 Proceedings of Machine Learning Research
 Volume:
 37
 ISSN:
 26403498
 Sponsoring Org:
 National Science Foundation
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