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001022054 041__ $$aEnglish
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001022054 1001_ $$0P:(DE-Juel1)176778$$aBouhadjar, Younes$$b0$$eCorresponding author
001022054 1112_ $$aOCNS$$cLeipzig$$d2023-07-15 - 2023-07-19$$wGermany
001022054 245__ $$aProbabilistic sequential memory recall in spiking neuronal networks
001022054 260__ $$c2023
001022054 3367_ $$033$$2EndNote$$aConference Paper
001022054 3367_ $$2DataCite$$aOther
001022054 3367_ $$2BibTeX$$aINPROCEEDINGS
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001022054 520__ $$aAnimals 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. The model consists of a sparsely connected network of excitatory neurons and a single inhibitory neuron. Excitatory neurons are divided into subpopulations with identical stimulus preferences.  External inputs representing specific sequence elements provide neurons in the same subpopulation with identical input. The inhibitory neuron mediates competition between neurons within subpopulations and promotes sparse activity and context sensitivity. Synapses between excitatory neurons are plastic and subject to spike-timing-dependent plasticity and homeostatic control. After learning, the network develops specific subnetworks representing the learned sequences, such that the presentation of a sequence element leads to a context dependent prediction of the subsequent element. 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 replay 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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001022054 536__ $$0G:(DE-Juel1)aca_20190115$$aAdvanced Computing Architectures (aca_20190115)$$caca_20190115$$fAdvanced Computing Architectures$$x2
001022054 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x3
001022054 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x4
001022054 536__ $$0G:(GEPRIS)491111487$$aDFG project G:(GEPRIS)491111487 - Open-Access-Publikationskosten / 2022 - 2024 / Forschungszentrum Jülich (OAPKFZJ) (491111487)$$c491111487$$x5
001022054 7001_ $$0P:(DE-HGF)0$$aWouters, Dirk J.$$b1
001022054 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b2
001022054 7001_ $$0P:(DE-Juel1)145211$$aTetzlaff, Tom$$b3
001022054 773__ $$0PERI:(DE-600)2193340-6$$x1553-734X
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001022054 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
001022054 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lComputational and Systems Neuroscience$$x1
001022054 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
001022054 9201_ $$0I:(DE-Juel1)PGI-7-20110106$$kPGI-7$$lElektronische Materialien$$x3
001022054 9201_ $$0I:(DE-Juel1)PGI-10-20170113$$kPGI-10$$lJARA Institut Green IT$$x4
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