  The backpropagation algorithm has experienced remarkable success in training
large-scale artificial neural networks; however, its biological plausibility
has been strongly criticized, and it remains an open question whether the brain
employs supervised learning mechanisms akin to it. Here, we propose correlative
information maximization between layer activations as an alternative normative
approach to describe the signal propagation in biological neural networks in
both forward and backward directions. This new framework addresses many
concerns about the biological-plausibility of conventional artificial neural
networks and the backpropagation algorithm. The coordinate descent-based
optimization of the corresponding objective, combined with the mean square
error loss function for fitting labeled supervision data, gives rise to a
neural network structure that emulates a more biologically realistic network of
multi-compartment pyramidal neurons with dendritic processing and lateral
inhibitory neurons. Furthermore, our approach provides a natural resolution to
the weight symmetry problem between forward and backward signal propagation
paths, a significant critique against the plausibility of the conventional
backpropagation algorithm. This is achieved by leveraging two alternative, yet
equivalent forms of the correlative mutual information objective. These
alternatives intrinsically lead to forward and backward prediction networks
without weight symmetry issues, providing a compelling solution to this
long-standing challenge.
