Path integration and cognitive mapping in a continuous attractor neural network model
Open accessAbstract
A minimal synaptic architecture is proposed for how the brain might perform path integration by computing the next internal representation of self-location from the current representation and from the perceived velocity of motion. In the model, a place-cell assembly called a “chart” contains a two-dimensional attractor set called an “attractor map” that can be used to represent coordinates in any arbitrary environment, once associative binding has occurred between chart locations and sensory inputs. In hippocampus, there are different spatial relations among place fields in different environments and behavioral contexts. Thus, the same units may participate in many charts, and it is shown that the number of uncorrelated charts that can be encoded in the same recurrent network is potentially quite large. According to this theory, the firing of a given place cell is primarily a cooperative effect of the activity of its neighbors on the currently active chart. Therefore, it is not particularly useful to think of place cells as encoding any particular external object or event. Because of its recurrent connections, hippocampal field CA3 is proposed as a possible location for this “multichart” architecture; however, other implementations in anatomy would not invalidate the main concepts. The model is implemented numerically both as a network of integrate-and-fire units and as a “macroscopic” (with respect to the space of states) description of the system, based on a continuous approximation defined by a system of stochastic differential equations. It provides an explanation for a number of hitherto perplexing observations on hippocampal place fields, including doubling, vanishing, reshaping in distorted environments, acquiring directionality in a two-goal shuttling task, rapid formation in a novel environment, and slow rotation after disorientation. The model makes several new predictions about the expected properties of hippocampal place cells and other cells of the proposed network.
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Remapping mechanisms have been modeled using multistable attractor dynamics for several decades.
“The mechanisms of remapping in biological circuits are still poorly understood, but have been modeled using multistable attractor dynamics for several decades (Samsonovich and McNaughton, 1997)”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as Samsonovich and McNaughton, 1997
Classical multistable attractor models typically store completely decorrelated spatial maps.
“Classical models typically store completely decorrelated spatial maps (Samsonovich and McNaughton, 1997)”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as Samsonovich and McNaughton, 1997
Classical models typically store completely decorrelated spatial maps, while our RNNs produce distinct maps that are, by construction, perfectly correlated in the position readout dimensions.
“Classical models typically store completely decorrelated spatial maps (Samsonovich and McNaughton, 1997), while our RNNs produce distinct maps that are, by construction, perfectly correlated in the position readout dimensions.”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as Samsonovich and McNaughton, 1997
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“46 Classically, these models were engineered and hand-tuned to produce the desired attractor dynamics.”
BackgroundRemapping in a recurrent neural network model of navigation and context inference
Low, I. I. C., Giocomo, L. M., & Williams, A. H. (2023) · doi:10.1101/2023.01.25.525596 · Cited by 3 · see citing works
Remapping in a recurrent neural network model of navigation and context inference
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- Authors
- Samsonovich, A., & McNaughton, B. L.
- Affiliations
- University of Arizona (US)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 1997-08-01
- Volume
- 17
- Issue
- 15
- Pages
- 5900-5920
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6573219
- OpenAlex
- W2149422514
- Cited by
- 989
- References
- 5
- Citations by year
- 2026: 33 · 2025: 46 · 2024: 47 · 2023: 49 · 2022: 34 · 2021: 34 · 2020: 30 · 2019: 48 · 2018: 27 · 2017: 35 · 2016: 32 · 2015: 43 · 2014: 36 · 2013: 43 · 2012: 30
- FWCI
- 13.63
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Office of Naval Research
- National Institute of Neurological Disorders and Stroke (R37NS020331, R01NS020331, NS-20331)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Physical Sciences › Engineering › Automotive Engineering › Spatial Cognition and Navigation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- path integration, cognitive mapping, continuous attractor networks, place cells, hippocampus, CA3, integrate-and-fire neurons, stochastic differential equations, recurrent neural networks
- MeSH terms
- Animals, Brain Mapping, Hippocampus, Neural Networks, Computer, Rats
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