Institute for Neural Computation, Ruhr University Bochum, Bochum, Germany.
Neurophysiology, Medical Faculty, Ruhr University Bochum, Bochum, Germany.
Sci Rep. 2021 Feb 1;11(1):2713. doi: 10.1038/s41598-021-81157-z.
The context-dependence of extinction learning has been well studied and requires the hippocampus. However, the underlying neural mechanisms are still poorly understood. Using memory-driven reinforcement learning and deep neural networks, we developed a model that learns to navigate autonomously in biologically realistic virtual reality environments based on raw camera inputs alone. Neither is context represented explicitly in our model, nor is context change signaled. We find that memory-intact agents learn distinct context representations, and develop ABA renewal, whereas memory-impaired agents do not. These findings reproduce the behavior of control and hippocampal animals, respectively. We therefore propose that the role of the hippocampus in the context-dependence of extinction learning might stem from its function in episodic-like memory and not in context-representation per se. We conclude that context-dependence can emerge from raw visual inputs.
灭绝学习的语境相关性已经得到了很好的研究，需要海马体的参与。然而，其潜在的神经机制仍不清楚。使用记忆驱动的强化学习和深度神经网络，我们开发了一种模型，该模型可以仅基于原始相机输入，在具有生物逼真度的虚拟现实环境中自主导航。在我们的模型中，既没有显式表示上下文，也没有发出上下文变化的信号。我们发现，记忆完好的智能体学习到不同的上下文表示，并发展出 ABA 再生，而记忆受损的智能体则不会。这些发现分别再现了对照和海马体动物的行为。因此，我们提出，海马体在灭绝学习的语境相关性中的作用可能源于其在类似情景记忆中的功能，而不是语境本身的表示。我们得出结论，语境相关性可以从原始视觉输入中产生。
