Biologically Motivated Algorithms for Propagating Local Target Representations

Understand

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.
• In addition, we propose an improved variant of Difference Target Propagation, another procedure that comes from the same family of algorithms as LRA-E.
• We compare our procedures to several other biologically-motivated algorithms, including two feedback alignment algorithms and Equilibrium Propagation.