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Topic: spatial exploration

Seminar
2 seminars

In Computational Neuroscience and Dynamical Systems

Seminar · Computational Neuroscience

Emergence of long time scales in data-driven network models of zebrafish activity

Remi Monasson · CNRS

Wed, Feb 10, 2021 · 05:00 UTC

How can neural networks exhibit persistent activity on time scales much larger than allowed by cellular properties? We address this question in the context of larval zebrafish, a model vertebrate that is accessible to brain-scale neuronal recording and high-throughput behavioral studies. We study in particular the dynamics of a bilaterally distributed circuit, the so-called ARTR, including hundreds neurons. ARTR exhibits slow antiphasic alternations between its left and right subpopulations, which can be modulated by the water temperature, and drive the coordinated orientation of swim bouts, t

Seminar · Computational Neuroscience

Linking neural representations of space by multiple attractor networks in the entorhinal cortex and the hippocampus

Yoram Burak · Hebrew University

Wed, Dec 9, 2020 · 05:00 UTC

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