Biologically Motivated Algorithms for Propagating Local Target Representations

26 May 2018Alexander G. OrorbiaAnkur Mali

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 learning algorithm called error-driven Local Representation Alignment (LRA-E), which has strong connections to predictive coding, a theory that offers a mechanistic way of describing neurocomputational machinery... (read more)

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