Journal Article

FZJ-2022-02720

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Sequence learning, prediction, and replay in networks of spiking neurons

Bouhadjar, Y. (Corresponding author)FZJ* ; Wouters, D. J. ; Diesmann, M.FZJ* ; Tetzlaff, T.FZJ*

2022
Public Library of Science

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Please use a persistent id in citations: http://hdl.handle.net/2128/31680  doi:10.1371/journal.pcbi.1010233

Abstract: Sequence learning, prediction and replay have been proposed to constitute the universal computations performed by the neocortex. The Hierarchical Temporal Memory (HTM) algorithm realizes these forms of computation. It learns sequences in an unsupervised and continuous manner using local learning rules, permits a context specific prediction of future sequence elements, and generates mismatch signals in case the predictions are not met. While the HTM algorithm accounts for a number of biological features such as topographic receptive fields, nonlinear dendritic processing, and sparse connectivity, it is based on abstract discrete-time neuron and synapse dynamics, as well as on plasticity mechanisms that can only partly be related to known biological mechanisms. Here, we devise a continuous-time implementation of the temporal-memory (TM) component of the HTM algorithm, which is based on a recurrent network of spiking neurons with biophysically interpretable variables and parameters. The model learns high-order sequences by means of a structural Hebbian synaptic plasticity mechanism supplemented with a rate-based homeostatic control. In combination with nonlinear dendritic input integration and local inhibitory feedback, this type of plasticity leads to the dynamic self-organization of narrow sequence-specific subnetworks. These subnetworks provide the substrate for a faithful propagation of sparse, synchronous activity, and, thereby, for a robust, context specific prediction of future sequence elements as well as for the autonomous replay of previously learned sequences. By strengthening the link to biology, our implementation facilitates the evaluation of the TM hypothesis based on experimentally accessible quantities. The continuous-time implementation of the TM algorithm permits, in particular, an investigation of the role of sequence timing for sequence learning, prediction and replay. We demonstrate this aspect by studying the effect of the sequence speed on the sequence learning performance and on the speed of autonomous sequence replay.

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Contributing Institute(s):

1. Computational and Systems Neuroscience (INM-6)
2. Theoretical Neuroscience (IAS-6)
3. Jara-Institut Brain structure-function relationships (INM-10)
4. Elektronische Materialien (PGI-7)
5. JARA Institut Green IT (PGI-10)

Research Program(s):

1. 574 - Theory, modelling and simulation (POF3-574) (POF3-574)
2. 5232 - Computational Principles (POF4-523) (POF4-523)
3. Advanced Computing Architectures (aca_20190115) (aca_20190115)
4. PhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405) (PHD-NO-GRANT-20170405)
5. HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) (945539)
6. Open-Access-Publikationskosten Forschungszentrum Jülich (OAPKFZJ) (491111487) (491111487)

Appears in the scientific report 2022

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Database coverage:
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