Biologically Motivated Algorithms for Propagating Local Target Representations
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Finding biologically plausible alternatives to back-propagation of errors is a fundamentally important challenge in artificial neural network research. In this paper, we propose a simple learning algorithm called error-driven Local Representation Alignment, which has strong connections to predictive coding, a theory that offers a mechanistic way of describing neurocomputational machinery. In addition, we propose an improved variant of Difference Target Propagation, another algorithm that comes from the same family of algorithms as Local Representation Alignment. We compare our learning procedures to several other biologically-motivated algorithms, including two feedback alignment algorithms and Equilibrium Propagation. In two benchmark datasets, we find that both of our proposed learning algorithms yield stable performance and strong generalization abilities in comparison to other competing back-propagation alternatives when training deeper, highly nonlinear networks, with Local Representation Alignment performing the best overall.
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