Learning by non-interfering feedback chemical signaling in physical networks
This addresses the challenge of efficient learning in physical networks, offering a biologically plausible alternative to existing methods like equilibrium propagation and coupled learning.
The paper tackles the problem of learning in physical systems by proposing a new algorithm based on chemical signaling that avoids storing two states, achieving 93% accuracy on the Iris dataset and proving it performs gradient descent.
Both non-neural and neural biological systems can learn. So rather than focusing on purely brain-like learning, efforts are underway to study learning in physical systems. Such efforts include equilibrium propagation (EP) and coupled learning (CL), which require storage of two different states-the free state and the perturbed state-during the learning process to retain information about gradients. Inspired by slime mold, we propose a new learning algorithm rooted in chemical signaling that does not require storage of two different states. Rather, the output error information is encoded in a chemical signal that diffuses into the network in a similar way as the activation/feedforward signal. The steady state feedback chemical concentration, along with the activation signal, stores the required gradient information locally. We apply our algorithm using a physical, linear flow network and test it using the Iris data set with 93% accuracy. We also prove that our algorithm performs gradient descent. Finally, in addition to comparing our algorithm directly with EP and CL, we address the biological plausibility of the algorithm.