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| 001 | 1041699 | ||
| 005 | 20250505202225.0 | ||
| 037 | _ | _ | |a FZJ-2025-02386 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Lober, Melissa |0 P:(DE-Juel1)190224 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a IAS Retreat |c Juelich |d 2025-05-27 - 2025-05-27 |w Germany |
| 245 | _ | _ | |a Learning sequence timing and controlling recall speed in networks of spiking neurons |
| 260 | _ | _ | |c 2025 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a CONFERENCE_POSTER |2 ORCID |
| 336 | 7 | _ | |a Output Types/Conference Poster |2 DataCite |
| 336 | 7 | _ | |a Poster |b poster |m poster |0 PUB:(DE-HGF)24 |s 1746441554_14469 |2 PUB:(DE-HGF) |x After Call |
| 502 | _ | _ | |c RWTH Aachen |
| 520 | _ | _ | |a Processing 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 |
| 536 | _ | _ | |a 5232 - Computational Principles (POF4-523) |0 G:(DE-HGF)POF4-5232 |c POF4-523 |f POF IV |x 0 |
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| 536 | _ | _ | |a JL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027) |0 G:(DE-Juel1)JL SMHB-2021-2027 |c JL SMHB-2021-2027 |x 2 |
| 700 | 1 | _ | |a Bouhadjar, Younes |0 P:(DE-Juel1)176778 |b 1 |u fzj |
| 700 | 1 | _ | |a Diesmann, Markus |0 P:(DE-Juel1)144174 |b 2 |u fzj |
| 700 | 1 | _ | |a Tetzlaff, Tom |0 P:(DE-Juel1)145211 |b 3 |u fzj |
| 909 | C | O | |o oai:juser.fz-juelich.de:1041699 |p VDB |
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| 914 | 1 | _ | |y 2025 |
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| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-6 |l Computational and Systems Neuroscience |x 0 |
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| 920 | 1 | _ | |0 I:(DE-Juel1)INM-10-20170113 |k INM-10 |l Jara-Institut Brain structure-function relationships |x 2 |
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| 980 | _ | _ | |a I:(DE-Juel1)INM-10-20170113 |
| 980 | _ | _ | |a UNRESTRICTED |
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