Predictive Coding as Stimulus Avoidance in Spiking Neural Networks
This addresses the challenge of autonomous learning in neuromorphic computing, but it appears incremental as it builds on prior work on stimulation avoidance.
The study tackled the problem of enabling spiking neural networks to learn temporal sequence predictions autonomously, and the result showed that networks with random structures spontaneously learned these predictions using spike-timing dependent plasticity without external guidance.
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.