Learning fast changing slow in spiking neural networks

arXiv:2402.10069v1 Announce Type: cross
Abstract: Reinforcement learning (RL) faces substantial challenges when applied to real-life problems, primarily stemming from the scarcity of available data due to limited interactions with the environment. This limitation is exacerbated by the fact that RL often demands a considerable volume of data for effective learning. The complexity escalates further when implementing RL in recurrent spiking networks, where inherent noise introduced by spikes adds a layer of difficulty. Life-long learning machines must inherently resolve the plasticity-stability …

abstract arxiv challenges complexity cs.lg cs.ne data environment interactions life networks neural networks reinforcement reinforcement learning spiking neural networks stemming the environment type