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Topic: Cognitive Map

Seminar
3 seminars
Seminar · Computational Neuroscience

The hippocampus, spatial planning, generative models and memory consolidation

Neil Burgess · University College London

Wed, Feb 25, 2026 · 16:00 UTC

Much is known about the neural representations of current environmental location and direction within the hippocampal formation, but use of such a “cognitive map” requires the online representation of desired locations and how to get there, and the neural basis for this function has been more elusive. I will discuss how “theta sweeps” of place and grid cell firing encode the current location (at early phases of each theta cycle) while, at later phases, sampling around the forward direction during exploration and indicating the direction to desired locations during goal-directed navigation. I w

Seminar · Computational Neuroscience

Building and Using a Cognitive Map

Adrien Peyrache · Mc Gill

Wed, Jan 21, 2026 · 16:00 UTC

The hippocampus is thought to build a cognitive map that supports navigation, memory, and planning, but what defines such a map and how it is used remain debated. In this talk, I will present computational models in which hippocampal-like representations emerge in recurrent neural networks trained to predict sequences of sensory observations. While spatially tuned units reliably arise, they are not sufficient to form a cognitive map. Instead, map-like representations emerge when recurrent dynamics support multi-step prediction, yielding a population-level encoding of environmental geometry. On

Seminar · Computational Neuroscience

Neural mechanisms of memory linking and replay: inhibition matters

Tomoki Fukai · Okinawa Institute of Science and Technology

Wed, May 14, 2025 · 15:00 UTC

My talk will consist of three subtopics. The brain remembers episodes not in isolation but with their contextual relationships, such as spatial or temporal proximity. This is an essential feature of the brain’s memory, but the underlying mechanism is yet to be explored. Cell assemblies, or engrams, may provide neural representations for such relationships. First, I will show a class of associative memory models that encode and retrieve multiple memory contents linked by an arbitrary graph structure through experience and demonstrate the crucial role of the balance between two inhibitory subnet

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