Tag: CAN

Whether grid cell networks show continuous attractor dynamics, and how they interface with inputs from the environment?

Richard J. Gardner, Erik Hermansen, Marius Pachitariu, Yoram Burak, Nils A. Baas, Benjamin A. Dunn, May-Britt Moser & Edvard I. Moser. Toroidal topology of population activity in grid cells. Nature (2022). https://doi.org/10.1038/s41586-021-04268-7

Abstract
“The medial entorhinal cortex is part …

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How to implement long-range navigation by path integration and decoding of grid cells in a neural network?

Edvardsen, Vegard. “Long-range navigation by path integration and decoding of grid cells in a neural network.” In 2017 International Joint Conference on Neural Networks (IJCNN), pp. 4348-4355. IEEE, 2017.

The following content is extracted from Edvardsen 2017.

Neural …

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How landmark and self-motion cues combine during navigation to generate spatial representations?

The excerpt note is about how combine landmark and self-motion cues for navigation from Campbell et al., 2018.

Campbell, Malcolm G., Samuel A. Ocko, Caitlin S. Mallory, Isabel I. C. Low, Surya Ganguli & Lisa M. Giocomo. Principles governing the

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How to represent large areas by reusing cells in 2D continuous attractor network model?

The excerpt note is about how to represent large areas by reusing cells in 2D continuous attractor network model in RatSLAM from Michael et al., 2008, 2010, and Samsonovich et al., 1997, McNaughton et al., 2006.

Michael Milford, and Gordon …

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How to represent location based on the place cells?

The excerpt note is about a model of spatial location based on the place cells from the Michael 2008.

Michael Milford. Robot Navigation from Nature: Simultaneous Localisation, Mapping, and Path Planning Based on Hippocampal Models. Springer-Verlag Berlin Heidelberg Press,

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How to represent robot’s pose with a rate-coded neural network CAN in RatSLAM?

The excerpt note is about robot’s pose representation with a rate-coded neural network, continuous attractor network (CAN) in RatSLAM from Michael and Gordon 2010.

The CAN is a neural network that consists of an array of units with fixed weighted …

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【Excerpt Note】Continuous Attractor Neural Network (CANN) and 1D CANN for Head Direction

This is a brief excerpt note for studying the continuous attractor neural network (CANN) and 1D CANN for Head Direction.

The content is from the Wu, S., et al. review paper. (Wu, S., Wong, K.M., Fung, C.A.,

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