Design and evaluation of analog predictive coding networks based on the free-energy principle

Abstract

The free-energy principle (FEP) is a new theory proposed in the field of theoretical neuroscience to mimic the processing of brain information. This FEP posits that the brain functions to minimize a single cost function called variational free energy, suggesting a relationship with existing brain theories, such as predictive coding (PC). By leveraging this principle, a top-down approach could be adopted to mimic the overall information processing in the brain, in contrast to the bottom-up approach of traditional neuromorphic computing components. In this study, we designed a PC network based on this principle using classical analog circuits and evaluated its performance using the circuit simulator NGSPICE. Analog circuits are more energy-efficient, integrate better, and more accurately mimic biological information processing than digital circuits. Consequently, the circuit demonstrated a performance similar to that of simulations conducted in Python, indicating the potential for creating novel brain-inspired hardware.