Towards truly local gradients with CLAPP: Contrastive, Local And Predictive Plasticity
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Back-propagation (BP) is costly to implement in hardware and implausible as a learning rule implemented in the brain. However, BP is surprisingly successful in explaining neuronal activity patterns found along the cortical processing stream. We propose a locally implementable, unsupervised learning algorithm, CLAPP, which minimizes a simple, layer-specific loss function, and thus does not need to back-propagate error signals. The weight updates only depend on state variables of the pre- and post-synaptic neurons and a layer-wide third factor. Networks trained with CLAPP build deep hierarchical representations of images and speech.
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