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Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
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Controlling Spatio-Temporal Sequences of Neural Activity by Local Synaptic Changes.

Hauke O Wernecke1,2, Andrew B Lehr3, Arvind Kumar1,2

  • 1Department of Computational Science and Technology, School of Electrical Engineering and Computer Science and Digital Futures, KTH Royal Institute of Technology, Stockholm 11428, Sweden hower@kth.se arvkumar@kth.se.

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience
|May 5, 2026
PubMed
Summary
This summary is machine-generated.

Heterogeneous neural networks with asymmetric neuron morphologies can control sequential brain activity. Modulating synaptic strength in specific locations allows flexible reconfiguration of neural sequences for rapid behavioral computations.

Keywords:
computational neurosciencedynamical networksneuromodulationneuroscience

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Network Dynamics

Background:

  • Sequential activity in neural networks underlies behavior.
  • Behavioral flexibility necessitates dynamic control over these sequences.
  • Mechanisms for controlling and reconfiguring neural sequences are not well understood.

Purpose of the Study:

  • To investigate how heterogeneous neural networks can evoke and control sequential activity.
  • To identify mechanisms for flexible reconfiguration of neural sequences.
  • To explore the computational capabilities of spatially heterogeneous networks.

Main Methods:

  • Simulated recurrently connected networks with heterogeneous connectivity and spatial in-degree landscapes.
  • Modulated synaptic strength in local neuronal neighborhoods.
  • Analyzed the impact of modulation on sequence initiation, termination, extension, gating, and redirection.

Main Results:

  • Heterogeneous networks with smooth spatial in-degree landscapes robustly evoke and control sequential activity.
  • High-impact locations for sequence control were identified, often coinciding with mid in-degree regions.
  • Local modulation of synaptic strength allowed flexible reconfiguration of sequential activity patterns.

Conclusions:

  • Spatially heterogeneous networks offer a framework for fast, flexible computations on behavioral timescales.
  • These networks enable context-dependent fine-tuning of computations via local modulation.
  • The findings suggest a mechanism for dynamic behavioral control within otherwise stable neural pathways.