An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity.
To efficiently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error backpropagation algorithm. However, in this algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of presynaptic and postsynaptic neurons. Several models have been proposed that approximate the backpropagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. Furthermore, for certain parameters, the weight change in the predictive coding model converges to that of the backpropagation algorithm. This suggests that it is possible for cortical networks with simple Hebbian synaptic plasticity to implement efficient learning algorithms in which synapses in areas on multiple levels of hierarchy are modified to minimize the error on the output.
MRC Brain Network Dynamics Unit, University of Oxford, Oxford, OX1 3TH, U.K., and FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, U.K. james.whittington@ndcn.ox.ac.uk.
James C R
JCR
Whittington
MRC Brain Network Dynamics Unit, University of Oxford, Oxford OX1 3TH, U.K., and Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, U.K. rafal.bogacz@ndcn.ox.ac.uk.
Rafal
R
Bogacz
D000465:Algorithms; D002540:Cerebral Cortex / Q000166:cytology*; D025461:Feedback, Physiological / Q000502:physiology*; D006801:Humans; D007858:Learning; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology*; D009474:Neurons / Q000502:physiology*