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Related Concept Videos

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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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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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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A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
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A synapse is a specialized structure where two neurons connect, allowing them to pass an electrical or chemical signal to another neuron. It is the point of communication between neurons. The term "synapse" is derived from the Greek word "synapsis," which means "conjunction." The entire process of neural communication revolves around the synapse. When activated, a neuron releases chemicals known as neurotransmitters into the synapse. These neurotransmitters cross the synapse and bind to...
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Postsynaptic potential (PSP) refers to a change in the electrical potential of a neuron when neurotransmitters released by presynaptic neurons bind to postsynaptic receptors. This potential can either be excitatory, leading to depolarization and ultimately action potential generation, or inhibitory, leading to hyperpolarization and suppression of the postsynaptic neuron.
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Related Experiment Video

Updated: Nov 16, 2025

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
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Non-linear Memristive Synaptic Dynamics for Efficient Unsupervised Learning in Spiking Neural Networks.

Stefano Brivio1, Denys R B Ly2, Elisa Vianello2

  • 1CNR - IMM, Unit of Agrate Brianza, Agrate Brianza, Italy.

Frontiers in Neuroscience
|February 26, 2021
PubMed
Summary

Spiking neural networks (SNNs) require different memristive synapse dynamics than conventional neural networks (NNs). Non-linear memristive synapses with hard boundaries improve SNN classification accuracy and training efficiency for edge AI.

Keywords:
MNISTSTDPanalog memorymemristive devicesmemristive synapsememristorneuromorphicspiking neural network

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Area of Science:

  • Neuromorphic Engineering
  • Materials Science
  • Computer Science

Background:

  • Spiking neural networks (SNNs) encode information in spikes, mimicking biological neurons, enabling efficient edge computing.
  • Conventional neural networks (NNs) use real numbers and are suited for cloud computing.
  • Both SNNs and NNs face hardware limitations, with memristive devices showing promise for overcoming these.

Purpose of the Study:

  • To investigate the distinct requirements of memristive synaptic dynamics for efficient SNN operation compared to conventional NNs.
  • To analyze the impact of various memristive synaptic dynamics on SNN training and performance.
  • To identify optimal memristive device characteristics for on-line SNN training.

Main Methods:

  • System-level simulations of SNNs trained on handwritten digit classification using spike timing-dependent plasticity (STDP).
  • Evaluation of linear and non-linear memristive synaptic dynamics with hard and soft conductance boundaries.
  • Quantitative analysis of synapse resolution and non-linearity effects on network performance.

Main Results:

  • Non-linear memristive synapses with hard boundaries significantly enhance SNN classification performance.
  • These non-linear, hard-bounded synapses offer the best trade-off between accuracy and training time.
  • Memristive device dynamics for SNNs differ substantially from those optimized for conventional NNs.

Conclusions:

  • Non-linear memristive synapses are crucial for efficient SNNs, particularly for on-line training.
  • Memristive devices with non-linear dynamics represent a practical solution for developing advanced neuromorphic hardware.
  • This research redefines the design criteria for memristive synapses in SNN applications.