Change search
ReferencesLink to record
Permanent link

Direct link
Spike-Based Bayesian-Hebbian Learning of Temporal Sequences
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Karolinska Inst, Sweden.
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Karolinska Inst, Sweden.
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Karolinska Inst, Sweden.
2016 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 12, no 5, e1004954Article in journal (Refereed) PublishedText
Abstract [en]

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model's feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison.

Place, publisher, year, edition, pages
2016. Vol. 12, no 5, e1004954
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-190519DOI: 10.1371/journal.pcbi.1004954ISI: 000379348100041ScopusID: 2-s2.0-84975865045OAI: oai:DiVA.org:kth-190519DiVA: diva2:953280
Funder
Swedish Research Council, VR-621-2012-3502VINNOVASwedish e‐Science Research CenterEU, FP7, Seventh Framework Programme, DFF - 1330-00226EU, FP7, Seventh Framework Programme, EU-FP7-FET-269921
Note

QC 20160817

Available from: 2016-08-17 Created: 2016-08-12 Last updated: 2016-08-17Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Tully, Philip J.Lindén, HenrikLansner, Anders
By organisation
Computational Science and Technology (CST)
In the same journal
PloS Computational Biology
Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 3 hits
ReferencesLink to record
Permanent link

Direct link