Tag: Continuous Attractor Neural Network

How grid cells could possibly organize?

Anselmi, Fabio, Benedetta Franceschiello, Micah M. Murray, and Lorenzo Rosasco. “A computational model for grid maps in neural populations.” arXiv preprint arXiv:1902.06553 (2019).

The following content is extracted from Anselmi 2019.

Anselmi, Fabio, Benedetta Franceschiello, Micah M. Murray, …

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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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Path Integration in a Continuous Attractor Network Model

The excerpt note is about path integration with continuous attractor network according to McNaughton B. L., et al., 2006.

McNaughton, Bruce L., Francesco P. Battaglia, Ole Jensen, Edvard I. Moser, and May-Britt Moser. “Path integration and the neural basis

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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 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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