Supervised learning with predictive coding (Whittington & Bogacz 2017)

"To effciently 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 back-propagation algorithm. However, in the back-propagation 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 pre-synaptic and post-synaptic neurons. Several models have been proposed that approximate the back-propagation 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. ..."

Model Type: Predictive Coding Network

Model Concept(s): Learning; Hebbian plasticity; Synaptic Plasticity

Simulation Environment: MATLAB

Implementer(s): Whittington, James C.R. [jcrwhittington at]


Whittington JCR, Bogacz R. (2017). An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity. Neural computation. 29 [PubMed]

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