Journal Article

FZJ-2023-01976

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Coherent noise enables probabilistic sequence replay in spiking neuronal networks

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

2023
Public Library of Science San Francisco, Calif.

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Please use a persistent id in citations: http://hdl.handle.net/2128/34370  doi:10.1371/journal.pcbi.1010989

Abstract: Animals rely on different decision strategies when faced with ambiguous or uncertain cues. Depending on the context, decisions may be biased towards events that were most frequently experienced in the past, or be more explorative. A particular type of decision making central to cognition is sequential memory recall in response to ambiguous cues. A previously developed spiking neuronal network implementation of sequence prediction and recall learns complex, high-order sequences in an unsupervised manner by local, biologically inspired plasticity rules. In response to an ambiguous cue, the model deterministically recalls the sequence shown most frequently during training. Here, we present an extension of the model enabling a range of different decision strategies. In this model, explorative behavior is generated by supplying neurons with noise. As the model relies on population encoding, uncorrelated noise averages out, and the recall dynamics remain effectively deterministic. In the presence of locally correlated noise, the averaging effect is avoided without impairing the model performance, and without the need for large noise amplitudes. We investigate two forms of correlated noise occurring in nature: shared synaptic background inputs, and random locking of the stimulus to spatiotemporal oscillations in the network activity. Depending on the noise characteristics, the network adopts various recall strategies. This study thereby provides potential mechanisms explaining how the statistics of learned sequences affect decision making, and how decision strategies can be adjusted after learning.

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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. HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) (785907)
5. HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) (945539)
6. DFG project 491111487 - Open-Access-Publikationskosten / 2022 - 2024 / Forschungszentrum Jülich (OAPKFZJ) (491111487) (491111487)

Appears in the scientific report 2023

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