Biophysical learning : Training flow networks via chemical signaling

Biological systems have the capability to modify their individual components to achieve task-specific global functionality. Consider, for example, slime mold modifying its structure to optimize for transport between food sources, brain modifying its synapses to improve behaviour or to compensate for loss of functionality in a damaged region. Our work explores how modifications in a physical system using locally available information can give rise to emergent global functionality. Inspired by how slime mold modifies its structure using chemical signals, we propose a mechanism to adjust the resistances of a flow network using chemical signaling. We demonstrate that such a mechanism can train a simple flow network to classify three species of iris flowers with significant accuracy by using the information of the sizes of the flower components as input, thus exhibiting 'machine learning-like' behavior. We also show that this mechanism optimizes a loss function by gradient descent. These simple biophysical learning systems may ultimately help us understand how more complex biological networks, such as the brain, exhibit emergent functionality.