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Found 5 papers, 4 papers with code
Frameworks for SNNs: a Review of Data Science-oriented Software and an Expansion of SpykeTorch
Developing effective learning systems for Machine Learning (ML) applications in the Neuromorphic (NM) field requires extensive experimentation and simulation.
Spiking Neural Networks for event-based action recognition: A new task to understand their advantage
Spiking Neural Networks (SNN) are characterised by their unique temporal dynamics, but the properties and advantages of such computations are still not well understood.
Simple and complex spiking neurons: perspectives and analysis in a simple STDP scenario
From this selection, we make a comparative study of three simple I&F neuron models, namely the LIF, the Quadratic I&F (QIF) and the Exponential I&F (EIF), to understand whether the use of more complex models increases the performance of the system and whether the choice of a neuron model can be directed by the task to be completed.
Keys to Accurate Feature Extraction Using Residual Spiking Neural Networks
Spiking neural networks (SNNs) have become an interesting alternative to conventional artificial neural networks (ANN) thanks to their temporal processing capabilities and energy efficient implementations in neuromorphic hardware.
Unsupervised Spiking Instance Segmentation on Event Data using STDP
We highlight the new capability by successfully transforming a single class unsupervised network for face detection into a multi-person face recognition and instance segmentation network.
