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Introducing double bouquet cells into a modular cortical associative memory model
KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). Aristotle University of Thessaloniki, Faculty of Engineering, School of Electrical and Computer Engineering, 54124, Thessaloniki, Greece.
KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). Institute for Adaptive and Neural Computation, Edinburgh University, EH8 9AB Edinburgh, Scotland. (Computational Brain Science)ORCID iD: 0000-0002-7314-8562
KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). Department of Numerical Analysis and Computer Science, Stockholm University, 10691 Stockholm, Sweden.ORCID iD: 0000-0002-2358-7815
(English)Manuscript (preprint) (Other academic)
Abstract [en]

We present an electrophysiological model of double bouquet cells and integrate them into an established cortical columnar microcircuit model that has previously been used as a spiking attractor model for memory. Learning in that model relies on a Bayesian-Hebbian learning rule to condition recurrent connectivity between pyramidal cells. We here demonstrate that the inclusion of a biophysically plausible double bouquet cell model can solve earlier concerns about learning rules that simultaneously learn excitation and inhibition and might thus violate Dale's Principle. We show that learning ability and resulting effective connectivity between functional columns of previous network models is preserved when pyramidal synapses onto double-bouquet cells are plastic under the same Hebbian-Bayesian learning rule. The proposed architecture draws on experimental evidence on double bouquet cells and effectively solves the problem of duplexed learning of inhibition and excitation by replacing recurrent inhibition between pyramidal cells in functional columns of different stimulus selectivity with a plastic disynaptic pathway. We thus show that the resulting change to the microcircuit architecture improves the model's biological plausibility without otherwise impacting the models spiking activity, basic operation, and learning abilities.

Keywords [en]
Double Bouquet cells electrophysiology cortical microcircuit memory cortex computational neuroscience
National Category
Bioinformatics (Computational Biology) Neurosciences
Research subject
Applied and Computational Mathematics; Computer Science; Biological Physics
Identifiers
URN: urn:nbn:se:kth:diva-239040DOI: 10.1101/462010OAI: oai:DiVA.org:kth-239040DiVA, id: diva2:1263423
Note

QC 20181115

Available from: 2018-11-15 Created: 2018-11-15 Last updated: 2018-11-15Bibliographically approved

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Fiebig, FlorianLansner, Anders

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