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Neuroplasticity01:01

Neuroplasticity

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Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
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Long-term Potentiation01:25

Long-term Potentiation

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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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Plasticity00:58

Plasticity

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Plasticity is the property where an object loses its elasticity and undergoes irreversible deformation, even after the deformation forces are eliminated. If a material deforms irreversibly without increasing stress or load, then this is called ideal plasticity. For example, when a force is applied to an aluminum rod, it changes its shape, but it does not return to its original shape once the force is removed. Plastic deformation or ductility is thus a permanent deformation or change in the...
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Purposive Learning01:22

Purposive Learning

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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Long-term Depression01:03

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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: Sep 9, 2025

Ex Vivo Optogenetic Interrogation of Long-Range Synaptic Transmission and Plasticity from Medial Prefrontal Cortex to Lateral Entorhinal Cortex
11:31

Ex Vivo Optogenetic Interrogation of Long-Range Synaptic Transmission and Plasticity from Medial Prefrontal Cortex to Lateral Entorhinal Cortex

Published on: February 25, 2022

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Reward-optimizing learning using stochastic release plasticity.

Yuhao Sun1,2, Wantong Liao1,2, Jinhao Li1,3

  • 1Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China.

Frontiers in Neural Circuits
|September 2, 2025
PubMed
Summary
This summary is machine-generated.

We introduce Reward-Optimized Stochastic Release Plasticity (RSRP), a novel learning rule for neural networks. RSRP achieves robust and effective reward-driven learning, comparable to established methods in AI and neuroscience.

Keywords:
Spiking Neural Networkbrain inspired computingreinforcement learningsupervised learningsynaptic plasticity

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Machine Learning

Background:

  • Synaptic plasticity enables adaptive learning in neural systems and is a biologically plausible model for reward-driven learning.
  • A key challenge is developing plasticity rules that match the robustness and effectiveness of error backpropagation.

Purpose of the Study:

  • Introduce Reward-Optimized Stochastic Release Plasticity (RSRP), a new learning framework.
  • Derive a plasticity rule that maximizes reward signals using natural gradient estimation.
  • Evaluate RSRP's performance and stability in reinforcement learning and digit classification tasks.

Main Methods:

  • Model synaptic release as a parameterized distribution within the RSRP framework.
  • Employ natural gradient estimation to derive the RSRP learning rule.
  • Validate RSRP in biologically plausible neural networks and compare it with Proximal Policy Optimization (PPO) and error backpropagation.

Main Results:

  • RSRP demonstrates competitive performance and stability in reinforcement learning, on par with PPO.
  • RSRP achieves accuracy comparable to error backpropagation in digit classification tasks.
  • Reward regularization is identified as a crucial mechanism for stabilizing RSRP.

Conclusions:

  • RSRP provides a robust and effective synaptic plasticity learning rule.
  • The findings have implications for both artificial intelligence and experimental neuroscience, particularly in discontinuous reinforcement learning scenarios.