Predictive Coding as Stimulus Avoidance in Spiking Neural Networks

2019/11/21 by Atsushi Masumori, Lana Sinapayen, Masumori, Atsushi +3
Engineering · Neuroscience · #Advanced Memory and Neural Computing #FOS: Biological sciences #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE) #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #Neuroscience and Neural Engineering

paper · pdf · doi:10.48550/arxiv.1911.09230

openalex publication_date 2019/11/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Predictive coding can be regarded as a function which reduces the error between an input signal and a top-down prediction. If reducing the error is equivalent to reducing the influence of stimuli from the environment, predictive coding can be regarded as stimulation avoidance by prediction. Our previous studies showed that action and selection for stimulation avoidance emerge in spiking neural networks through spike-timing dependent plasticity (STDP). In this study, we demonstrate that spiking neural networks with random structure spontaneously learn to predict temporal sequences of stimuli based solely on STDP.

Citations

• Simple model of spiking neurons

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