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Found 5 papers, 1 papers with code
Beyond still images: Temporal features and input variance resilience
Traditionally, vision models have predominantly relied on spatial features extracted from static images, deviating from the continuous stream of spatiotemporal features processed by the brain in natural vision.
Spyker: High-performance Library for Spiking Deep Neural Networks
Several SNNs are implemented in this work with different learning rules (spike-timing-dependent plasticity and reinforcement learning) using Spyker that achieve significantly better runtimes, to prove the practicality of the library in the simulation of large-scale networks.
Models Developed for Spiking Neural Networks
Spiking neural networks (SNNs) have been around for a long time, and they have been investigated to understand the dynamics of the brain.
A Faster Approach to Spiking Deep Convolutional Neural Networks
The proposed structure fractionalizes runtime and introduces an efficient approach to deep convolutional SNNs.
An automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions
Here, we develop an automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions to address these distortions and instabilities.
