Title |
Slowness and Sparseness Lead to Place, Head-Direction, and Spatial-View Cells
|
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Published in |
PLoS Computational Biology, August 2007
|
DOI | 10.1371/journal.pcbi.0030166 |
Pubmed ID | |
Authors |
Mathias Franzius, Henning Sprekeler, Laurenz Wiskott |
Abstract |
We present a model for the self-organized formation of place cells, head-direction cells, and spatial-view cells in the hippocampal formation based on unsupervised learning on quasi-natural visual stimuli. The model comprises a hierarchy of Slow Feature Analysis (SFA) nodes, which were recently shown to reproduce many properties of complex cells in the early visual system []. The system extracts a distributed grid-like representation of position and orientation, which is transcoded into a localized place-field, head-direction, or view representation, by sparse coding. The type of cells that develops depends solely on the relevant input statistics, i.e., the movement pattern of the simulated animal. The numerical simulations are complemented by a mathematical analysis that allows us to accurately predict the output of the top SFA layer. |
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Geographical breakdown
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Japan | 1 | 17% |
United States | 1 | 17% |
Germany | 1 | 17% |
Unknown | 2 | 33% |
Demographic breakdown
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Scientists | 3 | 50% |
Members of the public | 3 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 12 | 3% |
Germany | 6 | 1% |
Switzerland | 5 | 1% |
United Kingdom | 5 | 1% |
France | 3 | <1% |
Spain | 3 | <1% |
Malaysia | 2 | <1% |
Norway | 2 | <1% |
Japan | 2 | <1% |
Other | 8 | 2% |
Unknown | 421 | 90% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 128 | 27% |
Researcher | 77 | 16% |
Student > Master | 76 | 16% |
Student > Bachelor | 28 | 6% |
Student > Doctoral Student | 22 | 5% |
Other | 81 | 17% |
Unknown | 57 | 12% |
Readers by discipline | Count | As % |
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Computer Science | 72 | 15% |
Agricultural and Biological Sciences | 70 | 15% |
Engineering | 42 | 9% |
Neuroscience | 39 | 8% |
Social Sciences | 38 | 8% |
Other | 141 | 30% |
Unknown | 67 | 14% |