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06/30/2023
Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings
Spiking Neural Networks (SNNs) are a promising research direction for bu...
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06/01/2023
Adapting a ConvNeXt model to audio classification on AudioSet
In computer vision, convolutional neural networks (CNN) such as ConvNeXt...
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06/01/2023
Dilated Convolution with Learnable Spacings: beyond bilinear interpolation
Dilated Convolution with Learnable Spacings (DCLS) is a recently propose...
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04/25/2023
Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies
Vanilla spiking neurons in Spiking Neural Networks (SNNs) use charge-fir...
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02/13/2023
Optical Flow estimation with Event-based Cameras and Spiking Neural Networks
Event-based cameras are raising interest within the computer vision comm...
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10/23/2022
Drastically Reducing the Number of Trainable Parameters in Deep CNNs by Inter-layer Kernel-sharing
Deep convolutional neural networks (DCNNs) have become the state-of-the-...
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12/07/2021
Dilated convolution with learnable spacings
Dilated convolution is basically a convolution with a wider kernel creat...
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09/28/2021
StereoSpike: Depth Learning with a Spiking Neural Network
Depth estimation is an important computer vision task, useful in particu...
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09/27/2021
Spiking neural networks trained via proxy
We propose a new learning algorithm to train spiking neural networks (SN...
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08/31/2021
Spike time displacement based error backpropagation in convolutional spiking neural networks
We recently proposed the STiDi-BP algorithm, which avoids backward recur...
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03/01/2021
Fast threshold optimization for multi-label audio tagging using Surrogate gradient learning
Multi-label audio tagging consists of assigning sets of tags to audio re...
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02/08/2021
Spike-based Residual Blocks
Deep Spiking Neural Networks (SNNs) are harder to train than ANNs becaus...
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01/24/2021
Encrypted Internet traffic classification using a supervised Spiking Neural Network
Internet traffic recognition is an essential tool for access providers s...
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11/13/2020
Low-activity supervised convolutional spiking neural networks applied to speech commands recognition
Deep Neural Networks (DNNs) are the current state-of-the-art models in m...
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07/08/2020
BS4NN: Binarized Spiking Neural Networks with Temporal Coding and Learning
We recently proposed the S4NN algorithm, essentially an adaptation of ba...
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11/22/2019
Technical report: supervised training of convolutional spiking neural networks with PyTorch
Recently, it has been shown that spiking neural networks (SNNs) can be t...
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10/21/2019
S4NN: temporal backpropagation for spiking neural networks with one spike per neuron
We propose a new supervised learning rule for multilayer spiking neural ...
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03/06/2019
SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks with at most one Spike per Neuron
Application of deep convolutional spiking neural networks (SNNs) to arti...
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04/22/2018
Deep Learning in Spiking Neural Networks
Deep learning approaches have shown remarkable performance in many areas...
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03/31/2018
Combining STDP and Reward-Modulated STDP in Deep Convolutional Spiking Neural Networks for Digit Recognition
The primate visual system has inspired the development of deep artificia...
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03/01/2018
Optimal localist and distributed coding of spatiotemporal spike patterns through STDP and coincidence detection
Repeating spatiotemporal spike patterns exist and carry information. Her...
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06/20/2017
Representation Learning using Event-based STDP
Although representation learning methods developed within the framework ...
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05/25/2017
First-spike based visual categorization using reward-modulated STDP
Reinforcement learning (RL) has recently regained popularity, with major...
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03/29/2017
Object categorization in finer levels requires higher spatial frequencies, and therefore takes longer
The human visual system contains a hierarchical sequence of modules that...
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11/04/2016
STDP-based spiking deep convolutional neural networks for object recognition
Previous studies have shown that spike-timing-dependent plasticity (STDP...
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10/24/2016
STDP allows close-to-optimal spatiotemporal spike pattern detection by single coincidence detector neurons
By recording multiple cells simultaneously, electrophysiologists have fo...
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06/03/2016
Acquisition of Visual Features Through Probabilistic Spike-Timing-Dependent Plasticity
The final version of this paper has been published in IEEEXplore availab...
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04/21/2016
Humans and deep networks largely agree on which kinds of variation make object recognition harder
View-invariant object recognition is a challenging problem, which has at...
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04/15/2015
