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A model of associative learning in the mushroom body

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Abstract

The mushroom body is a prominent invertebrate neuropil strongly associated with learning and memory. We built a high-level computational model of this structure using simplified but realistic models of neurons and synapses, and developed a learning rule based on activity dependent pre-synaptic facilitation. We show that our model, which is consistent with mushroom body Drosophila data and incorporates Aplysia learning, is able to both acquire and later recall CS–US associations. We demonstrate that a highly divergent input connectivity to the mushroom body and strong periodic inhibition both serve to improve overall learning performance. We also examine the problem of how synaptic conductance, driven by successive training events, obtains a value appropriate for the stimulus being learnt. We employ two feedback mechanisms: one stabilises strength at an initial level appropriate for an association; another prevents strength increase for established associations.

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Authors and Affiliations

  1. IPAB, School of Informatics, University of Edinburgh, James Clerk Maxwell Building, The King’s Buildings, Mayfield Road, Edinburgh, EH9 3JZ, UK

    Darren Smith, Jan Wessnitzer & Barbara Webb

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  1. Darren Smith
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  2. Jan Wessnitzer
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  3. Barbara Webb
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Correspondence to Darren Smith.

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Smith, D., Wessnitzer, J. & Webb, B. A model of associative learning in the mushroom body. Biol Cybern 99, 89–103 (2008). https://doi.org/10.1007/s00422-008-0241-1

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  • DOI: https://doi.org/10.1007/s00422-008-0241-1

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