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The backpropagation algorithm is often debated for its biologicalplausibility. However, various learning methods for neural architecture havebeen proposed in search of more biologically plausible learning. Most of themhave tried to solve the "weight transport problem" and try to propagate errorsbackward in the architecture via some alternative methods. In this work, weinvestigated a slightly different approach that uses only the local informationwhich captures spike timing information with no propagation of errors. Theproposed learning rule is derived from the concepts of spike timing dependantplasticity and neuronal association. A preliminary evaluation done on thebinary classification of MNIST and IRIS datasets with two hidden layers showscomparable performance with backpropagation. The model learned using thismethod also shows a possibility of better adversarial robustness against theFGSM attack compared to the model learned through backpropagation ofcross-entropy loss. The local nature of learning gives a possibility of largescale distributed and parallel learning in the network. And finally, theproposed method is a more biologically sound method that can probably help inunderstanding how biological neurons learn different abstractions.

A More Biologically Plausible Local Learning Rule for ANNs