Modeling Light‐Induced Synaptic Behavior and Visual Memory of Azopolymeric Compounds

Adaptive and intelligent matter is becoming a cornerstone in the development of unconventional computing paradigms, as it allows to process and store information at the matter level in a way similar to biological neuronal systems. A crucial requirement for testing the implementation of neuromorphic-type of data processing is the ability to model and predict the material response under different stimulation conditions. In this work, we develop a modeling framework able to describe the dynamics of light-responsive organic materials when stimulated by light. By correlating parameters of a rate-balance based model with physical quantities, we highlight the basic mechanisms concurring to the temporal evolution of the material's internal state. The model accurately predicts the response to timely correlated signals, capturing the typical synaptic-like behavior of the light responsive material and reveals the contribution to the observed dynamic of both long-term and short-term memory components. To further explore the potential of the model, we use a spatially distributed gray scale intensity pattern to stimulate the material and show that the model accurately captures the spatio-temporal evolution of the material's internal state, unveiling the potential for visual memory applications.