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Reward-optimizing learning using stochastic release plasticity
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DOI：10.3389/fncir.2025.1618506.png)
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Abstract
En 中文
Synaptic plasticity underlies adaptive learning in neural systems; offering a biologically plausible framework for reward-driven learning. However; a question remains: how can plasticity rules achieve robustness and effectiveness comparable to error backpropagation? In this study; we introduce Reward-Optimized Stochastic Release Plasticity (RSRP); a learning framework where synaptic release is modeled as a parameterized distribution. Utilizing natural gradient estimation; we derive a synaptic plasticity learning rule that effectively adapts to maximize reward signals. Our approach achieves competitive performance and demonstrates stability in reinforcement learning; comparable to Proximal Policy Optimization (PPO); while attaining accuracy comparable with error backpropagation in digit classification. Additionally; we identify reward regularization as a key stabilizing mechanism and validate our method in biologically plausible networks. Our findings suggest that RSRP offers a robust and effective plasticity learning rule; especially in a discontinuous reinforcement learning paradigm; with potential implications for both artificial intelligence and experimental neuroscience.
Keywords:
synaptic plasticity
reinforcement learning
reward optimization
natural gradient
biologically plausible networks