Hebbian Learning in a Random Network Captures Selectivity Properties of the Prefrontal Cortex
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11021.full.pdf
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2.98 MB
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Author(s)
Lindsay, Grace W.
Rigotti, Mattia
Fusi, Stefano
Warden, Melissa
Miller, Earl K
Date Issued
November 2017
Journal
Journal of Neuroscience
Publisher
Society for Neuroscience
Citation
Lindsay, Grace W. et al. “Hebbian Learning in a Random Network Captures Selectivity Properties of the Prefrontal Cortex.” The Journal of Neuroscience 37, 45 (October 2017): 11021–11036 © 2017 The Authors
Version
Final published version
Abstract
Complex cognitive behaviors, such as context-switching and rule-following, are thought to be supported by the prefrontal cortex (PFC). Neural activity in the PFC must thus be specialized to specific tasks while retaining flexibility. Nonlinear “mixed” selectivity is an important neurophysiological trait for enabling complex and context-dependent behaviors. Here we investigate (1) the extent to which the PFC exhibits computationally relevant properties, such as mixed selectivity, and (2) how such properties could arise via circuit mechanisms. We show that PFC cells recorded from male and female rhesus macaques during a complex task show a moderate level of specialization and structure that is not replicated by a model wherein cells receive random feedforward inputs. While random connectivity can be effective at generating mixed selectivity, the data show significantly more mixed selectivity than predicted by a model with otherwise matched parameters. A simple Hebbian learning rule applied to the random connectivity, however, increases mixed selectivity and enables the model to match the data more accurately. To explain how learning achieves this, we provide analysis along with a clear geometric interpretation of the impact of learning on selectivity. After learning, the model also matches the data on measures of noise, response density, clustering, and the distribution of selectivities. Of two styles of Hebbian learning tested, the simpler and more biologically plausible option better matches the data. These modeling results provide clues about how neural properties important for cognition can arise in a circuit and make clear experimental predictions regarding how various measures of selectivity would evolve during animal training.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Picower Institute for Learning and Memory
Terms of Use
Creative Commons Attribution 4.0 International License
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DOI of Published Version
https://doi.org/10.1523/JNEUROSCI.1222-17.2017
