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The Brain Prize winners' webinar

Larry Abbott, Haim Sompolinsky, Terry Sejnowski · Columbia University; Harvard University / Hebrew University; Salk Institute

…Sompolinsky explores attractor networks, revealing how discrete and continuous attractors can stabilize activity patterns, enable working memory, and incorporate chaot

Sat, Nov 30, 2024 · 14:00 UTC

Seminar · Computational Neuroscience

…Although supplanted by neural networks as the dominant modelling modality, cognitive architectures possess many attributes sorely coveted by the computational modeller: large, architectural-level scales, unified explanations of disparate cognitive functions in single models, and rich, hierarchical-modular decompositional structure conducive to building and modifying large computational systems. In contrast, we find in the neural network literature a prolife

ePoster · Computational Neuroscience

In the past decade evidence has accumulated in favor of the hypothesis that multiple sub-networks in the medial entorhinal cortex (MEC) are characterized by low-dimensional, continuous attractor dynamics. Much has been learned about the joint activity of grid cells within a module (a module consists of grid cells that share a common grid spacing), but little is known about the interactions between them. Under typical conditions of spatial exploration in which sensory cues are abundant, all grid-cells in the MEC represent the animal’s position in space and their joint activity lies on a two-dim

Wed, Dec 9, 2020 · 05:00 UTC

Seminar · Computational Neuroscience