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001041699 037__ $$aFZJ-2025-02386
001041699 041__ $$aEnglish
001041699 1001_ $$0P:(DE-Juel1)190224$$aLober, Melissa$$b0$$eCorresponding author$$ufzj
001041699 1112_ $$aIAS Retreat$$cJuelich$$d2025-05-27 - 2025-05-27$$wGermany
001041699 245__ $$aLearning sequence timing and controlling recall speed in networks of spiking neurons
001041699 260__ $$c2025
001041699 3367_ $$033$$2EndNote$$aConference Paper
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001041699 502__ $$cRWTH Aachen
001041699 520__ $$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 [1], 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 is correlated to the characteristics of global oscillatory activity, observed in EEG or LFP recordings. In summary, our results contribute to the understanding of sequence processing and time representation in the brain.[1] Bouhadjar, Y., Wouters, D. J., Diesmann, M., Tetzlaff, T. (2022), Sequence learning, prediction, and replay in networks of spiking neurons, PLOS Computational Biology 18(6):e1010233
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001041699 536__ $$0G:(DE-Juel1)JL SMHB-2021-2027$$aJL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027)$$cJL SMHB-2021-2027$$x2
001041699 7001_ $$0P:(DE-Juel1)176778$$aBouhadjar, Younes$$b1$$ufzj
001041699 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b2$$ufzj
001041699 7001_ $$0P:(DE-Juel1)145211$$aTetzlaff, Tom$$b3$$ufzj
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001041699 9141_ $$y2025
001041699 920__ $$lyes
001041699 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lComputational and Systems Neuroscience$$x0
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