Accurate path integration in continuous attractor network models of grid cells
- Creators
- Burak, Yoram
- Fiete, Ila R.
Abstract
Grid cells in the rat entorhinal cortex display strikingly regular firing responses to the animal's position in 2-D space and have been hypothesized to form the neural substrate for dead-reckoning. However, errors accumulate rapidly when velocity inputs are integrated in existing models of grid cell activity. To produce grid-cell-like responses, these models would require frequent resets triggered by external sensory cues. Such inadequacies, shared by various models, cast doubt on the dead-reckoning potential of the grid cell system. Here we focus on the question of accurate path integration, specifically in continuous attractor models of grid cell activity. We show, in contrast to previous models, that continuous attractor models can generate regular triangular grid responses, based on inputs that encode only the rat's velocity and heading direction. We consider the role of the network boundary in the integration performance of the network and show that both periodic and aperiodic networks are capable of accurate path integration, despite important differences in their attractor manifolds. We quantify the rate at which errors in the velocity integration accumulate as a function of network size and intrinsic noise within the network. With a plausible range of parameters and the inclusion of spike variability, our model networks can accurately integrate velocity inputs over a maximum of ~10–100 meters and ~1–10 minutes. These findings form a proof-of-concept that continuous attractor dynamics may underlie velocity integration in the dorsolateral medial entorhinal cortex. The simulations also generate pertinent upper bounds on the accuracy of integration that may be achieved by continuous attractor dynamics in the grid cell network. We suggest experiments to test the continuous attractor model and differentiate it from models in which single cells establish their responses independently of each other.
Additional Information
© 2009 PubMed Central. Received August 4, 2008; Accepted January 6, 2009. This work was partially supported by the National Science Foundation Division of Physics (99-07949) to the Kavli Institute for Theoretical Physics. YB is a Swartz Fellow in Theoretical Neuroscience, and IRF is a Broad Senior Research Fellow in Brain Circuitry. The authors have declared that no competing interests exist.Attached Files
Published - Burak2009p53410.1371journal.pcbi.1000291.pdf
Supplemental Material - journal.pcbi.1000291.s001.pdf
Supplemental Material - journal.pcbi.1000291.s002.pdf
Supplemental Material - journal.pcbi.1000291.s003.pdf
Supplemental Material - journal.pcbi.1000291.s004.pdf
Supplemental Material - journal.pcbi.1000291.s005.pdf
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Additional details
- PMCID
- PMC2632741
- Eprint ID
- 14131
- Resolver ID
- CaltechAUTHORS:20090501-113317998
- 99-07949
- NSF
- Kavli Institute for Theoretical Physics
- Swartz Foundation
- Eli and Edythe Broad Foundation
- Created
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2009-08-10Created from EPrint's datestamp field
- Updated
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2021-11-08Created from EPrint's last_modified field