A biologically plausible implementation of back-propagation for prediction tasks in simple recurrent networks

  • André Grüning (SISSA, Trieste, Italy)
A3 01 (Sophus-Lie room)


Recurrent neural networks in time series prediction tasks are traditionally trained with a gradient decent based learning algorithm, notably with back-propagation (BP) through time. A major drawback for the biological plausibility of BP is that it is a supervised scheme in which a teacher has to provide a fully specified target answer to the network. Yet, agents in natural environments often receive a summary feed-back about the degree of success or failure only, a view adopted in reinforcement learning schemes.

In this work we show that for simple recurrent networks in prediction tasks for which there is a probability interpretation of the network's output vector, Elman back-propagation can be implemented as a reinforcement learning scheme for which the expected weight updates agree with the ones from traditional Elman BP, using ideas from the AGREL learning scheme (van Ooyen and Roelfsema 2003).

While there are other draw-backs of BP learning schemes, which we are going to discuss, we hope to contribute to making the widely used BP training scheme more acceptable from a biological point of view.