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Related Concept Videos

Long-term Depression01:03

Long-term Depression

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

Updated: Jun 9, 2026

Generalized Psychophysiological Interaction (PPI) Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
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Eligibility-trace-gated depression in predecessor feature learning enables post-reward exploration.

Incheol Seo1,2,3, Sun-Hyun Park4, Hyunsu Lee5,6,7

  • 1Department of Immunology, School of Medicine, Kyungpook National University, Gukchaebosang-Ro, Daegu, 41944 Republic of Korea.

Cognitive Neurodynamics
|June 8, 2026
PubMed
Summary

Acetylcholine (ACh) modulation enhances spatial exploration in reinforcement learning by employing synaptic depression. This ACh-modulated predecessor feature (PF) algorithm shows improved performance in complex mazes, but with scaling limitations.

Keywords:
Eligibility tracesNeuromodulated plasticityPost-reward explorationPredecessor featuresPredictive representationsTrace-dependent depression

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Reinforcement Learning

Background:

  • Dopamine's role in reward prediction error is established in reinforcement learning.
  • Computational functions of other neuromodulators, like acetylcholine (ACh), are less understood.
  • Flexible learning and exploration are crucial for adapting to changing environments.

Purpose of the Study:

  • Investigate the effect of ACh modulation on predecessor feature (PF)-based learning.
  • Develop an ACh-modulated PF algorithm (ACh-PF) using synaptic depression to promote exploration.
  • Compare ACh-PF performance against a baseline PF algorithm in varying maze complexities.

Main Methods:

  • Developed an ACh-modulated PF algorithm (ACh-PF) implementing synaptic depression.
  • Utilized eligibility-trace outer products for synaptic depression in the ACh-PF model.
  • Tested algorithms in n-arm radial mazes with conventional and post-reward exploration criteria.

Main Results:

  • ACh-PF demonstrated improved performance under a post-reward exploration criterion compared to baseline PF.
  • ACh-PF performance showed a non-monotonic dependence on the synaptic depression parameter, with an optimal intermediate regime.
  • Performance degraded with increased spatial and action-space complexity, revealing scaling limits.

Conclusions:

  • Cholinergic-like synaptic depression is linked to flexible exploration in spatial navigation.
  • The ACh-PF algorithm offers a computational framework for understanding ACh's role in exploration.
  • Environmental complexity imposes scaling limits on the efficiency of exploration strategies.