Unsupervised Learning and Adaptation in a Model of Adult Neurogenesis (original) (raw)

Abstract

Adult neurogenesis has long been documented in the vertebrate brain and recently even in humans. Although it has been conjectured for many years that its functional role is related to the renewing of memories, no clear mechanism as to how this can be achieved has been proposed. Using the mammalian olfactory bulb as a paradigm, we present a scheme in which incorporation of new neurons proceeds at a constant rate, while their survival is activity-dependent and thus contingent on new neurons establishing suitable connections. We show that a simple mathematical model following these rules organizes its activity so as to maximize the difference between its responses and can adapt to changing environmental conditions in unsupervised fashion, in agreement with current neurophysiological data.

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Authors and Affiliations

1. Laboratory of Mathematical Physics, The Rockefeller University, 1230 York Avenue, New York, NY, 10021, U.S.A.
   Guillermo A. Cecchi & Marcelo O. Magnasco
2. Functional Neuroimaging Laboratory, Weill Medical College of Cornell University, New York, NY, 10021
   Guillermo A. Cecchi
3. Biometaphorical Computing Group, TJ Watson Center, IBM Research, Yorktown Heights, NY, 10598, USA
   Guillermo A. Cecchi
4. Laboratory of Neurogenesis, The Rockefeller University, 1230 York Avenue, New York, NY, 10021, U.S.A
   Leopoldo T. Petreanu & Arturo Alvarez-Buylla

Authors

1. Guillermo A. Cecchi
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2. Leopoldo T. Petreanu
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3. Arturo Alvarez-Buylla
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4. Marcelo O. Magnasco
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Cecchi, G.A., Petreanu, L.T., Alvarez-Buylla, A. et al. Unsupervised Learning and Adaptation in a Model of Adult Neurogenesis.J Comput Neurosci 11, 175–182 (2001). https://doi.org/10.1023/A:1012849801892

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• Issue Date: September 2001
• DOI: https://doi.org/10.1023/A:1012849801892