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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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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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Related Experiment Video

Updated: May 24, 2025

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

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Published on: March 25, 2014

STSF: Spiking Time Sparse Feedback Learning for Spiking Neural Networks.

Ping He, Rong Xiao, Chenwei Tang

IEEE Transactions on Neural Networks and Learning Systems

|March 3, 2025

Summary

This summary is machine-generated.

We introduce a Spiking Time Sparse Feedback (STSF) learning method for efficient training of Spiking Neural Networks (SNNs). This biologically plausible approach enhances accuracy and reduces computational costs for SNNs.

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Last Updated: May 24, 2025

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Published on: May 29, 2017

Area of Science:

Computational Neuroscience
Artificial Intelligence
Machine Learning

Background:

Spiking Neural Networks (SNNs) offer computational efficiency due to binary spike train information transmission.
Traditional backpropagation (BP) training is computationally expensive for SNNs due to their spatio-temporal dynamics.
Unsupervised learning methods for SNNs often result in suboptimal performance.

Purpose of the Study:

To propose a novel, efficient, and biologically plausible learning method for Spiking Neural Networks.
To address the challenges of high computational cost and suboptimal accuracy in current SNN training methods.
To improve the parallelism and reduce storage overhead in SNNs.

Main Methods:

Developed a Spiking Time Sparse Feedback (STSF) learning method combining global supervised learning with sparse direct feedback alignment (DFA) and local homeostasis learning with spike-timing-dependent plasticity (STDP).
Utilized a neuromodulator for global learning and incorporated sparse fixed random feedback connections for error modulation, replacing multiplication with selection operations.
Focused on instantaneous synaptic activity for independent and simultaneous optimization of network layers.

Main Results:

The STSF method significantly reduces computational cost compared to existing SNN training algorithms.
Achieved significantly higher accuracy across various classification tasks, comparable to state-of-the-art methods.
Demonstrated improved biological plausibility, enhanced parallelism, and reduced storage overhead.

Conclusions:

The proposed STSF learning method offers a highly efficient and biologically plausible approach for training Spiking Neural Networks.
STSF effectively balances computational efficiency and high accuracy, overcoming limitations of traditional training methods.
The method's architecture promotes parallelism and reduces memory requirements, making it suitable for neuromorphic hardware.