Biologically Motivated Algorithms for Propagating Local Target Representations
Biologically Motivated Algorithms for Propagating Local Target Representations
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. …