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Higher classification performance is obtained by maximising the tem-poral separation, which also drives the correct class neuron to spike earlier in the simulation. As a consequence, the predicted class may be determined from the first spike in the output layer, leading to quicker classification.
The approach utilizes a multilayered feedfoward SNN in which each spiking neuron can generate an arbitrary number of spikes.
Jun 25, 2024 · This paper develops a new approach to estimate predicted class probabilities in deep Spiking Neural Networks (SNN) that encourages faster ...
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Apr 12, 2024 · We show that neurons with SFA endow networks of spiking neurons with the capability to learn very fast, even without synaptic plasticity.
Jan 25, 2024 · We demonstrate that the proposed methodology enhances the efficiency of learning, showcasing its potential impact on neuromorphic and real-world applications.
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Our goal is to develop a fast and small neural network to extract useful features, learn their statistical structure, and make accurate classification decisions ...
Spiking neural networks (SNNs) that enables energy effi- cient implementation on emerging neuromorphic hardware are gaining more attention.
Sep 20, 2024 · Results suggest that SSM-based SNNs can outperform the Transformer on all tasks of a well-established long-range sequence modelling benchmark.
Spiking neural networks (SNNs) are believed to be highly computationally and energy efficient for specific neurochip hardware real-time solutions.
Aug 7, 2023 · Optimization of SNNs is a challenging task. Two main methods, artificial neural network (ANN)-to-SNN conversion and spike-based backpropagation ...