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Daniel Durstewitz

Central Institute of

Mental Health, Mannheim

April 23, 2025

Learning generative dynamical systems models from multi-modal and multi-animal neuro-data

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For decades dynamical systems theory played a pivotal role in theoretical and computational neuroscience, as it links biophysical and biochemical processes to neural computation. In fact, dynamical systems are computationally universal. Rather than hand-crafting computational theories of neural function based on dynamical systems, recent developments in scientific machine learning (ML) and AI suggest that we may be able to infer such dynamical-computational models directly from neurophysiological and behavioral observations. This is called dynamical systems reconstruction (DSR), the learning of generative surrogate models of the underlying dynamics, including its long-term temporal and geometrical properties, from time series data. In my talk I will cover recent ML/AI architectures, training algorithms, and validation procedures for DSR. I will discuss specifically how recent AI architectures for DSR can integrate neuroscience data from multiple modalities (like multiple single-unit recordings and behavioral choices), across diverse time scales, and across many different animals and task designs, into a joint DSR model. This provides first steps toward dynamical systems based AI foundation models for neuroscience.

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April 30, 2025

No Seminar

Mark van Rossum 

University of Nottingham

May 7, 2025

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TBA

Tomoki Fukai

Okinawa Institute

of Science and Technology

May 14, 2025

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Neural mechanisms of memory linking and replay: inhibition matters

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 subnetwork types in the flexible retrieval of relational memories. Secondly, I propose a theoretical framework to generate a cognitive map, i.e., neural representations of relationships between memory items. This framework aims at the predictive function of the hippocampus and is based on successor representations proposed for reinforcement learning. Intriguingly, the model provides a unified account for grid cells in spatial navigation and concept cells in natural language processing. Finally, I will discuss another crucial role of the hippocampal memory system, memory replay, in a spiking neural network model. Unlike the conventional associative memory models that maintain attractor memory states, this model attempts to maximize the capacity of replayed activity patterns. Our model suggests the crucial role of inhibitory plasticity in optimizing spontaneous memory replay.

Alexei Koulakov

Cold Spring Harbor Laboratory

May 21, 2025

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TBA

Nischal Mainali

May 28, 2025

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TBA

Bing Wen Brunton

University of Washington 

Seattle

June 4, 2025

TBA

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Ulises Pereira Oblinovic

Allen Institute

June 11, 2025

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TBA

Riccardo Zecchina

Bocconi University, Milano

June 18, 2025

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TBA

Sebastian Seung

Princeton Neuroscience Institute

June 25, 2025

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VVTNS Fifth Season Closing Lecture

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