Spiking Neural Networks

Introduced by Klambauer et al. in Self-Normalizing Neural Networks

Spiking Neural Networks (SNNs) are a class of artificial neural networks inspired by the structure and functioning of the brain's neural networks. Unlike traditional artificial neural networks that operate based on continuous firing rates, SNNs simulate the behavior of individual neurons through discrete spikes or action potentials. These spikes are triggered when the neuron's membrane potential reaches a certain threshold, and they propagate through the network, communicating information and triggering subsequent neuron activations. This spike-based communication allows SNNs to capture the temporal dynamics of information processing and exhibit asynchronous, event-driven behavior, making them well-suited for tasks such as temporal pattern recognition, event detection, and real-time processing. SNNs have gained attention due to their potential in efficiently processing and encoding information, offering advantages in energy efficiency, robustness, and compatibility with neuromorphic hardware architectures.

Source: Self-Normalizing Neural Networks

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