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Mark van Rossum 

University of Nottingham

May 7, 2025

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Energy efficient learning in neural networks

The brain is one of the most energy intense organs. Some of this energyis used for neural information processing, however, fruitfly experiments have shown that also learning is metabolically costly. We will present estimates of this cost and introduce a general model of this cost, and compare it to costs in computers. Next, we turn to a supervised artificial network setting and explore a number of strategies that cansave energy need for plasticity. Either by modifying the objective function, by restricting plasticity, or by using less costly transient forms of plasticity. Finally, we will discuss adaptive strategies and possible relevance for biological learning.

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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