Biology subjects

Wiskott, L.

Publications and source records attributed to Wiskott, L..

3 recordsLinked to original sources

bioRxiv2025

Unifying spatial and episodic representations in the hippocampus through flexible memory use

A key question in neuroscience is why the hippocampus is essential for episodic memory in humans, while dominantly exhibiting spatial representations in a number of other species. Some accounts suggest that spatial representation is the primary hippocampal function. Here, we propose that the primary function is storing and retrieving episodic memories, and spatial representations emerge due to this memory function. To demonstrate this, we adopt a computational model that autonomously learns to store information in memory and retrieve it to solve a variety of tasks. In memory tasks, the model develops associations and categorical representations akin to concept cells. In navigation tasks, the model forms representations of the spatial structure, performs geometric computations, and even learns representations of unique events similar to recently discovered barcodes. Our model predicts that the hippocampus represents any task-relevant variable, if the animal learns the task, suggesting that space is not special for the hippocampus.

neuroscience↗

bioRxiv2021

The computational benefits of episodic memory in spatial learning

AO_SCPLOWBSTRACTC_SCPLOWEpisodic memory has been studied extensively in the past few decades, but so far little is understood about how it drives behavior. Here we propose that episodic memory can facilitate learning in two fundamentally different modes: retrieval and replay. We study their properties by comparing three learning paradigms using computational modeling based on visually-driven reinforcement learning. Firstly, episodic memory is retrieved to learn from single experiences (one-shot learning); secondly, episodic memory is replayed to facilitate learning of statistical regularities (replay learning); and, thirdly, learning occurs online as experiences arise with no access to past experiences (online learning). We found that episodic memory benefits spatial learning in a broad range of conditions, but the performance difference is meaningful only when the task is sufficiently complex and the number of learning trials is limited. Furthermore, the two modes of accessing episodic memory affect spatial learning distinctly. One-shot learning is initially faster than replay learning, but the latter reaches a better asymptotic performance. Our model accounts for experimental results where replay is inhibited, but the hippocampus, and hence episodic memory, is intact during learning. Understanding how episodic memory drives behavior will be an important step towards elucidating the nature of episodic memory.

neuroscience↗

bioRxiv2020

Context-dependent extinction learning emerging from raw sensory inputs: A reinforcement learning approach

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 VR 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.

neuroscience↗