000908612 001__ 908612
000908612 005__ 20240313103134.0
000908612 0247_ $$2doi$$a10.48550/arXiv.2206.10538
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000908612 037__ $$aFZJ-2022-02721
000908612 041__ $$aEnglish
000908612 1001_ $$0P:(DE-Juel1)176778$$aBouhadjar, Younes$$b0$$eCorresponding author
000908612 245__ $$aCoherent noise enables probabilistic sequence replay in spiking neuronal networks
000908612 260__ $$barXiv$$c2022
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000908612 520__ $$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. 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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000908612 536__ $$0G:(DE-Juel1)aca_20190115$$aAdvanced Computing Architectures (aca_20190115)$$caca_20190115$$fAdvanced Computing Architectures$$x2
000908612 536__ $$0G:(DE-Juel1)PHD-NO-GRANT-20170405$$aPhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)$$cPHD-NO-GRANT-20170405$$x3
000908612 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x4
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000908612 650_7 $$2Other$$aNeurons and Cognition (q-bio.NC)
000908612 650_7 $$2Other$$aFOS: Biological sciences
000908612 7001_ $$0P:(DE-HGF)0$$aWouters, Dirk J.$$b1
000908612 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b2
000908612 7001_ $$0P:(DE-Juel1)145211$$aTetzlaff, Tom$$b3
000908612 773__ $$a10.48550/arXiv.2206.10538
000908612 8564_ $$uhttps://doi.org/10.48550/arXiv.2206.10538
000908612 8564_ $$uhttps://juser.fz-juelich.de/record/908612/files/2206.10538.pdf$$yOpenAccess
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000908612 9201_ $$0I:(DE-Juel1)PGI-7-20110106$$kPGI-7$$lElektronische Materialien$$x3
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