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Related Concept Videos

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.
Hebbian LTP
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Long-term Potentiation01:35

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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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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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Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
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Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
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Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...
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Updated: Dec 28, 2025

Recording Synaptic Plasticity in Acute Hippocampal Slices Maintained in a Small-volume Recycling-, Perfusion-, and Submersion-type Chamber System
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Recording Synaptic Plasticity in Acute Hippocampal Slices Maintained in a Small-volume Recycling-, Perfusion-, and Submersion-type Chamber System

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Energy efficient synaptic plasticity.

Ho Ling Li1, Mark Cw van Rossum1,2

  • 1School of Psychology, University of Nottingham, Nottingham, United Kingdom.

Elife
|February 14, 2020
PubMed
Summary
This summary is machine-generated.

Neural network training is energy-intensive due to synaptic plasticity costs. A new "synaptic caching" method balances plasticity types, significantly boosting energy efficiency for learning and neuromorphic computing.

Keywords:
computational modelsmetabolismneurosciencenonesynaptic consolidationsynaptic plasticity

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Brain evolution favors metabolic efficiency.
  • Synaptic plasticity, crucial for learning, is energetically demanding.
  • Naive implementation of plasticity rules in neural networks leads to high energy consumption.

Purpose of the Study:

  • To investigate the impact of metabolic costs of synaptic plasticity on learning in neural networks.
  • To develop an energy-efficient algorithm for neural network training.
  • To provide a novel interpretation of experimentally observed synaptic plasticity mechanisms.

Main Methods:

  • Simulated training of artificial neural networks with varying synaptic plasticity rules.
  • Implementation and testing of the proposed "synaptic caching" algorithm.
  • Analysis of energy consumption and learning performance.

Main Results:

  • Naive synaptic plasticity rules result in prohibitive energy costs for storing many patterns.
  • Synaptic caching significantly enhances energy efficiency during neural network training.
  • The algorithm is compatible with existing plasticity rules, including back-propagation.

Conclusions:

  • Balancing labile and stable synaptic plasticity forms is key to energy-efficient learning.
  • Synaptic caching offers a biologically plausible and computationally efficient approach to neural learning.
  • Findings have implications for understanding brain function and designing energy-efficient neuromorphic systems.