  Despite great advances in explaining synaptic plasticity and neuron function,
a complete understanding of the brain's learning algorithms is still missing.
Artificial neural networks provide a powerful learning paradigm through the
backpropagation algorithm which modifies synaptic weights by using feedback
connections. Backpropagation requires extensive communication of information
back through the layers of a network. This has been argued to be biologically
implausible and it is not clear whether backpropagation can be realized in the
brain. Here we suggest that biophotons guided by axons provide a potential
channel for backward transmission of information in the brain. Biophotons have
been experimentally shown to be produced in the brain, yet their purpose is not
understood. We propose that biophotons can propagate from each post-synaptic
neuron to its pre-synaptic one to carry the required information backward. To
reflect the stochastic character of biophoton emissions, our model includes the
stochastic backward transmission of teaching signals. We demonstrate that a
three-layered network of neurons can learn the MNIST handwritten digit
classification task using our proposed backpropagation-like algorithm with
stochastic photonic feedback. We model realistic restrictions and show that our
system still learns the task for low rates of biophoton emission,
information-limited (one bit per photon) backward transmission, and in the
presence of noise photons. Our results suggest a new functionality for
biophotons and provide an alternate mechanism for backward transmission in the
brain.
