A neural-astrocyte architecture implements a hybrid automaton for evidence accumulation

2026-09-14

AI Summary

研究通过构建神经-星形胶质细胞网络，利用奖励诱导的分岔和吸引子浅度机制解决强化学习中上下文推理问题。

Abstract

Astrocytes are non-neuronal glial cells that are receiving widespread attention due to their emerging role in neural computation. In this paper, we propose and study dynamical mechanisms by which astrocytes may augment the ability of neural networks to infer context in reinforcement learning (RL) settings. We construct a biologically inspired, two-level dynamical neural-astrocyte network with distinct spatial and temporal organization. We train this model on a hierarchical multi-context task that requires the agent to infer changes in latent task rules based on derived rewards. We find that in this setting, astrocytes enable evidence accumulation of changes in context and subsequent context-specific modulation of neural dynamics. We show that these functions are implemented via two dynamical mechanisms: (i) reward-induced bifurcations that relocate an asymptotically stable attractor into different, context-specific regions of state space, and (ii) the relative shallowness of these attractors, mediated by the entropy of the environment, giving rise to behavioral stickiness. Together, these mechanisms amount to a hybrid automaton, in which uncertainty accumulates until, eventually, the neural dynamics are switched to a new context. This model provides a neuro-dynamic schema, compatible with neural-astrocyte biology and prior empirical observations, for how astrocytes may integrate information from the periphery and drive contextual changes in neural circuits.

Problem

• astrocytes
• neural networks
• reinforcement learning
• context inference
• evidence accumulation

Innovation

• neural-astrocyte architecture
• reward-induced bifurcations
• contextual changes
• evidence accumulation
• hybrid automaton