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001047536 0247_ $$2datacite_doi$$a10.34734/FZJ-2025-04366
001047536 037__ $$aFZJ-2025-04366
001047536 041__ $$aEnglish
001047536 1001_ $$0P:(DE-Juel1)190224$$aLober, Melissa$$b0$$eCorresponding author$$ufzj
001047536 1112_ $$aBernstein Conference$$cFrankfurt$$d2025-09-29 - 2025-10-02$$wGermany
001047536 245__ $$aLearning sequence timing and controlling recall speed in networks of spiking neurons
001047536 260__ $$c2025
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001047536 502__ $$cRWTH Aachen
001047536 520__ $$aProcessing sequential inputs is a fundamental aspect of brain function, underlying tasks such as sensory perception, reading, and mathematical reasoning. At the core of the cortical algorithm, sequence processing involves learning the order and timing of elements, predicting future events, detecting unexpected deviations, and recalling learned sequences. The spiking Temporal Memory (sTM) model (Bouhadjar, 2022), a biologically inspired spiking neuronal network, provides a framework for key aspects of sequence processing. In its original version, however, it can not learn the timing of sequence elements. Further, it remains an open question how the speed of sequential recall can be flexibly modulated. We propose a mechanism in which the duration of sequence elements is represented by repeated activations of element specific neuronal populations. The sTM model can thereby represent even long time intervals, providing a biologically plausible basis for learning and recalling not only the order of sequence elements, but also complex rhythms. Additionally, we demonstrate that oscillatory background inputs can serve as a clock signal and thereby provide a robust mechanism for controlling the speed of sequence recall. Modulation of oscillation frequency and amplitude enable a stable recall across a wide range of speeds,offering a biologically relevant strategy for flexible temporal adaptation. Our findings suggest that time is encoded by unique and sparse spatio-temporal patterns of neural activity, and that the speed of sequence recall during wakefulness and sleep is correlated to the characteristics of global oscillatory activity, as observed in EEG or LFP recordings. In summary, our results contribute to the understanding of sequence processing and time representation in the brain.
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001047536 7001_ $$0P:(DE-Juel1)176778$$aBouhadjar, Younes$$b1$$ufzj
001047536 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b2$$ufzj
001047536 7001_ $$0P:(DE-Juel1)145211$$aTetzlaff, Tom$$b3$$ufzj
001047536 8564_ $$uhttps://juser.fz-juelich.de/record/1047536/files/tm_timing_poster_Bernstein_2025.pdf$$yOpenAccess
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001047536 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lComputational and Systems Neuroscience$$x0
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