Learning Fast Changing Slow In Spiking Neural Networks

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 paradox. Striking a balance between acquiring new knowledge and maintaining stability is crucial for artificial agents. To address this challenge, we draw inspiration from machine learning technology and introduce a biologically plausible implementation of proximal policy optimization, referred to as lf-cs (learning fast changing slow). Our approach results in two notable advancements: firstly, the capacity to assimilate new information into a new

Stats

• citations2
• S2 citations—
• github stars0
• HF likes0
• heat score3.58
• arxiv keycapone2024learning

Related papers

• Deep Reinforcement Learning With Spiking Q-learning (2022)0.00
• Reinforcement Learning With A Network Of Spiking Agents (2019)0.00
• Adaptive Surrogate Gradients For Sequential Reinforcement Learning In Spiking Neural Networks (2025)0.00
• Learning First-to-spike Policies For Neuromorphic Control Using Policy Gradients (2018)8.60
• Reinforcement Learning With Brain-inspired Modulation Can Improve Adaptation To Environmental Changes (2022)0.00
• Fully Spiking Actor Network With Intra-layer Connections For Reinforcement Learning (2024)0.00
• A Low Latency Adaptive Coding Spiking Framework For Deep Reinforcement Learning (2022)7.16
• Demystifying Reinforcement Learning In Time-varying Systems (2022)0.00