Physical learning at nonzero temperatures

Biological processes in nature are remarkably robust to noise. One such example is protein allostery, in which the binding of a molecule at one site of the protein induces a nonlocal response at a distant site. The long-ranged communication required for this interaction to occur persists despite thermal fluctuations. Although such communication pathways are not yet fully understood, allosteric mechanisms have been successfully encoded at effectively zero temperature in computational models as well as physical realizations. Here, by examining the role of temperature on physical learning, we investigate whether these long-ranged interactions are as robust to thermal fluctuations as their biological counterparts. We use elastic networks as simple, mechanical analogs of physical systems and examine the ability to retain allosteric and auxetic responses that were encoded at zero temperature. We observe that target responses trained using local learning rules are robust to thermal fluctuations up to a certain temperature. Finally, we explore the extent to which the learning itself can occur at nonzero temperature.