Abstract
Lateral predictive coding is a recurrent neural network that.creates energy-efficient internal representations by exploiting statis-tical regularity in sensory inputs.Here,we analytically investigate the trade-off between information robustness and energy in a linear model of lateral predictive coding and numerically minimize a free energy quantity.We observed several phase transi-tions in the synaptic weight matrix,particularly a continuous transition that breaks reciprocity and permutation symmetry and builds cyclic dominance and a discontinuous transition with the associated sudden emergence of tight balance between excitatory and inhibitory interactions.The optimal network follows an ideal gas law over an extended temperature range and saturates the efficiency upper bound of energy use.These results provide theoretical insights into the emergence and evolution of complex internal models in predictive processing systems.
基金项目
National Natural Science Foundation of China(12047503)
National Natural Science Foundation of China(11747601)
National Natural Science Foundation of China(12247104)
National Innovation Institute of Defense Technology(22TQ0904ZT01025)
维普
万方数据