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Controlling Spatio-Temporal Sequences of Neural Activity by Local Synaptic Changes
KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Digital futures.ORCID iD: 0009-0001-5116-7535
Department of Neuro- and Sensory Physiology, University Medical Center Göttingen, Göttingen 37073, Germany.
KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Digital futures.ORCID iD: 0000-0002-8044-9195
2026 (English)In: Journal of Neuroscience, ISSN 0270-6474, E-ISSN 1529-2401, Vol. 46, no 22, article id e1506252026Article in journal (Refereed) Published
Abstract [en]

The neural basis of behavior is believed to consist of sequential patterns of neural activity in the relevant brain regions. Behavioral flexibility also requires neural circuit mechanisms that support dynamic control of sequential activity. However, mechanisms to control and reconfigure sequential activity have received little attention. Here, we show that recurrently connected networks with heterogeneous connectivity and a smooth spatial in-degree landscape (which may arise due to asymmetric neuron morphologies) provide a robust mechanism to evoke and control sequential activity. By modulating the synaptic strength of only a few neurons in local neighborhoods, we uncovered high-impact locations that can start, stop, extend, gate, and redirect sequences. Interestingly, high-impact locations coincide with mid in-degree regions. We demonstrate that these motifs can flexibly reconfigure sequential activity, and hence, provide a framework for fast and flexible computations on behavioral timescales, while the individual parts of the pathways remain rigid and reliable.

Place, publisher, year, edition, pages
Society for Neuroscience , 2026. Vol. 46, no 22, article id e1506252026
Keywords [en]
computational neuroscience, dynamical networks, neuromodulation, neuroscience
National Category
Neurosciences Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:kth:diva-383816DOI: 10.1523/JNEUROSCI.1506-25.2026ISI: 001791067100001PubMedID: 42086319Scopus ID: 2-s2.0-105040946905OAI: oai:DiVA.org:kth-383816DiVA, id: diva2:2083448
Note

QC 20260702

Available from: 2026-07-02 Created: 2026-07-02 Last updated: 2026-07-02Bibliographically approved

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Wernecke, Hauke O.Kumar, Arvind

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