ePoster · Computational Neuroscience
Complex spatial representations and computations emerge in a memory-augmented network that learns to navigate
Xiangshuai Zeng, Laurenz Wiskott, Sen Cheng · Bernstein Conference 2024
There has been a long-lasting debate on whether the function of the hippocampal formation is to store and retrieve episodic memory, supported by experiments in humans, or to code for space, suggested by spatially tuned cells (place cells, grid cells, etc.) recorded in nonhuman animals. In an attempt to reconcile the two views, we use a memory-augmented neural network in a reinforcement learning setup to solve a navigation task in a simulated maze. The key feature of the model is that it learns autonomously what and when to store into, and retrieve from, an external memory buffer. Once the mode