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spiking neural network
A computational model that incorporates the timing of individual action potentials, unlike rate-based neural networks.
Full Definition
Spiking neural networks are computational models that explicitly represent the precise timing of action potentials (spikes) rather than just firing rates. These models are considered the third generation of artificial neural networks and are more biologically realistic than their predecessors. They can process temporal information and exhibit complex dynamics like synchronization and oscillations. In neurobiology research, they are used to understand how neural circuits process information and to develop brain-inspired computing systems.
Usage
Usage note: Often abbreviated as SNN in technical contexts.
In Context
- "The researchers used a spiking neural network to model the temporal dynamics of cortical processing." — Research paper
- "Unlike traditional neural networks, spiking neural networks can capture the precise timing of neural events." — Technical documentation
Also known as
SNN
third-generation neural network
Contrasted with
rate-based neural network
artificial neural network
Don't confuse with
artificial neural network
recurrent neural network