A Continuous Attractor Model with Realistic Neural and Synaptic Properties Quantitatively Reproduces Grid Cell Physiology DOI Creative Commons
Nate Sutton, Blanca Erika Gutiérrez-Guzmán, Holger Dannenberg

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: May 1, 2024

Abstract Computational simulations with data-driven physiological detail can foster a deeper understanding of the neural mechanisms involved in cognition. Here, we utilize wealth cellular properties from Hippocampome.org to study spatial coding spiking continuous attractor network model medial entorhinal cortex circuit activity. The primary goal was investigate if adding such realistic constraints could produce firing patterns similar those measured real neurons. Biological characteristics included work are excitability, connectivity, and synaptic signaling neuron types defined primarily by their axonal dendritic morphologies. We dynamics specific activities between groups Modeling rodent hippocampal formation keeps computationally reasonable scale while also anchoring parameters results experimental measurements. Our generates grid cell activity that well matches spacing, size, rates fields recorded live behaving animals both published datasets new experiments performed for this study. recreate different scales properties, e.g., small large, as found along dorsoventral axis cortex. exploration neuronal reveals broad range simulation. These demonstrate cells is compatible implementation sourcing biophysical anatomical . software released open source enable community reuse encourage novel applications.

Language: Английский

A Continuous Attractor Model with Realistic Neural and Synaptic Properties Quantitatively Reproduces Grid Cell Physiology DOI Creative Commons
Nate Sutton, Blanca Erika Gutiérrez-Guzmán, Holger Dannenberg

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: May 1, 2024

Abstract Computational simulations with data-driven physiological detail can foster a deeper understanding of the neural mechanisms involved in cognition. Here, we utilize wealth cellular properties from Hippocampome.org to study spatial coding spiking continuous attractor network model medial entorhinal cortex circuit activity. The primary goal was investigate if adding such realistic constraints could produce firing patterns similar those measured real neurons. Biological characteristics included work are excitability, connectivity, and synaptic signaling neuron types defined primarily by their axonal dendritic morphologies. We dynamics specific activities between groups Modeling rodent hippocampal formation keeps computationally reasonable scale while also anchoring parameters results experimental measurements. Our generates grid cell activity that well matches spacing, size, rates fields recorded live behaving animals both published datasets new experiments performed for this study. recreate different scales properties, e.g., small large, as found along dorsoventral axis cortex. exploration neuronal reveals broad range simulation. These demonstrate cells is compatible implementation sourcing biophysical anatomical . software released open source enable community reuse encourage novel applications.

Language: Английский

Citations

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