  The backpropagation algorithm is often debated for its biological
plausibility. However, various learning methods for neural architecture have
been proposed in search of more biologically plausible learning. Most of them
have tried to solve the "weight transport problem" and try to propagate errors
backward in the architecture via some alternative methods. In this work, we
investigated a slightly different approach that uses only the local information
which captures spike timing information with no propagation of errors. The
proposed learning rule is derived from the concepts of spike timing dependant
plasticity and neuronal association. A preliminary evaluation done on the
binary classification of MNIST and IRIS datasets with two hidden layers shows
comparable performance with backpropagation. The model learned using this
method also shows a possibility of better adversarial robustness against the
FGSM attack compared to the model learned through backpropagation of
cross-entropy loss. The local nature of learning gives a possibility of large
scale distributed and parallel learning in the network. And finally, the
proposed method is a more biologically sound method that can probably help in
understanding how biological neurons learn different abstractions.
