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Long-term Potentiation

Long-term Potentiation

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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Related Experiment Video

Updated: Jun 15, 2025

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

Trainable Reference Spikes Improve Temporal Information Processing of SNNs With Supervised Learning.

Zeyuan Wang¹, Luis Cruz²

¹Department of Physics, Drexel University, Philadelphia, PA 19104, U.S.A. zw435@drexel.edu.

Neural Computation

|August 23, 2024

Summary

This summary is machine-generated.

Reference spikes enhance Spiking Neural Networks (SNNs) performance by improving temporal information processing. This novel parameter boosts memory capacity and classification accuracy in SNNs for complex AI tasks.

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Last Updated: Jun 15, 2025

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

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Published on: May 29, 2017

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Area of Science:

Computational Neuroscience
Artificial Intelligence

Background:

Spiking Neural Networks (SNNs) mimic biological neurons, communicating via spikes.
Current SNNs often lag behind Artificial Neural Networks (ANNs) in performance due to limited trainable parameters.
Brain complexity offers potential for novel SNN parameters to enhance information processing.

Purpose of the Study:

Introduce 'reference spikes' as a new trainable plastic parameter for SNNs.
Investigate the impact of reference spikes on SNN temporal information processing.
Enhance SNN performance in supervised learning tasks.

Main Methods:

Proposed reference spikes as a novel plastic parameter in SNNs.
Implemented a supervised learning scheme with error backpropagation for reference spike training.
Evaluated SNNs with reference spikes on temporal datasets (MNIST, Fashion-MNIST, SHD).

Main Results:

Reference spikes significantly improved SNN memory capacity for spike pattern mapping.
Classification accuracy increased on MNIST, Fashion-MNIST, and SHD datasets.
Demonstrated enhanced temporal information processing capabilities in SNNs.

Conclusions:

Reference spikes represent a promising new parameter for advancing SNN performance.
This method effectively boosts SNNs' ability to handle temporally encoded information.
The findings suggest a pathway towards more brain-like AI functions using SNNs.