3 research outputs found
Emergent learning in physical systems as feedback-based aging in a glassy landscape
By training linear physical networks to learn linear transformations, we
discern how their physical properties evolve due to weight update rules. Our
findings highlight a striking similarity between the learning behaviors of such
networks and the processes of aging and memory formation in disordered and
glassy systems. We show that the learning dynamics resembles an aging process,
where the system relaxes in response to repeated application of the feedback
boundary forces in presence of an input force, thus encoding a memory of the
input-output relationship. With this relaxation comes an increase in the
correlation length, which is indicated by the two-point correlation function
for the components of the network. We also observe that the square root of the
mean-squared error as a function of epoch takes on a non-exponential form,
which is a typical feature of glassy systems. This physical interpretation
suggests that by encoding more detailed information into input and feedback
boundary forces, the process of emergent learning can be rather ubiquitous and,
thus, serve as a very early physical mechanism, from an evolutionary
standpoint, for learning in biological systems.Comment: 11 pages, 7 figure
Rectification of Random Walkers Induced by Energy Flow at Boundaries
We explore rectification phenomena in a system where two-dimensional random
walkers interact with a funnel-shaped ratchet under two distinct classes of
reflection rules. The two classes include the angle of reflection exceeding the
angle of incidence (), or vice versa
(). These generalized boundary reflection
rules are indicative of non-equilibrium conditions due to the introduction of
energy flows at the boundary. Our findings reveal that the nature of such
particle-wall interactions dictates the system's behavior: the funnel either
acts as a pump, directing flow, or as a collector, demonstrating a ratchet
reversal. Importantly, we provide a geometric proof elucidating the underlying
mechanism of rectification, thereby offering insights into why certain
interactions lead to directed motion, while others do not.Comment: 5 pages, 6 figure
Learning by non-interfering feedback chemical signaling in physical networks
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. Here, we propose a 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.ISSN:2643-156