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Title: On the relationship between predictive coding and backpropagation
Artificial neural networks are often interpreted as abstract models of biological neuronal networks, but they are typically trained using the biologically unrealistic backpropagation algorithm and its variants. Predictive coding has been proposed as a potentially more biologically realistic alternative to backpropagation for training neural networks. This manuscript reviews and extends recent work on the mathematical relationship between predictive coding and backpropagation for training feedforward artificial neural networks on supervised learning tasks. Implications of these results for the interpretation of predictive coding and deep neural networks as models of biological learning are discussed along with a repository of functions, Torch2PC, for performing predictive coding with PyTorch neural network models.  more » « less
Award ID(s):
1654268 1707400
NSF-PAR ID:
10335394
Author(s) / Creator(s):
Editor(s):
Cymbalyuk, Gennady S.
Date Published:
Journal Name:
PLOS ONE
Volume:
17
Issue:
3
ISSN:
1932-6203
Page Range / eLocation ID:
e0266102
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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