A biologically-plausible learning rule using reciprocal feedback connections
Mia Cameron, Yusi Chen, Terrence Sejnowski · COSYNE 2025
Artificial neural networks are frequently used to model neural systems, and have been shown to recapitulate features of biological networks, including in the hippocampus [1] and visual cortex [2] [3]. However, despite it’s success in neural modelling, the predominant learning algorithm, backpropagation, has long been considered biologically implausible [4]. One reason is the weight-transport problem - or the problem of how a global error signal can be accurately transmitted across the network to minimize the error resulting from each layer’s parameters. Previous solutions to the weight-transpo
ePoster · Neuroscience