no code implementations • 22 Sep 2023 • Mireille El-Assal, Pierre Tirilly, Ioan Marius Bilasco
In this work, we use CSNNs trained in an unsupervised manner with the Spike Timing-Dependent Plasticity (STDP) rule, and we introduce, for the first time, Spiking Separated Spatial and Temporal Convolutions (S3TCs) for the sake of reducing the number of parameters required for video analysis.
no code implementations • 4 Aug 2023 • Gaspard Goupy, Pierre Tirilly, Ioan Marius Bilasco
Then, we introduce a training architecture called Paired Competing Neurons (PCN) to further enhance the learning capabilities of our classification layer trained with S2-STDP.
no code implementations • 23 Jun 2023 • Mireille El-Assal, Pierre Tirilly, Ioan Marius Bilasco
Implementing this model with unsupervised STDP-based CSNNs allows us to further study the performance of these networks with video analysis.
no code implementations • 26 May 2022 • Mireille El-Assal, Pierre Tirilly, Ioan Marius Bilasco
We compare the performance of this model to those of a 2D STDP-based SNN when challenged with the KTH and Weizmann datasets.
no code implementations • 24 Feb 2020 • Pierre Falez, Pierre Tirilly, Ioan Marius Bilasco
Experiments on CIFAR-10 show that whitening allows STDP to learn visual features that are closer to the ones learned with standard neural networks, with a significantly increased classification performance as compared to DoG filtering.
no code implementations • 26 May 2019 • Romain Belmonte, Benjamin Allaert, Pierre Tirilly, Ioan Marius Bilasco, Chaabane Djeraba, Nicu Sebe
Although facial landmark localization (FLL) approaches are becoming increasingly accurate for characterizing facial regions, one question remains unanswered: what is the impact of these approaches on subsequent related tasks?
no code implementations • 3 Apr 2019 • Pierre Falez, Pierre Tirilly, Ioan Marius Bilasco, Philippe Devienne, Pierre Boulet
Spiking neural networks (SNNs) are good candidates to produce ultra-energy-efficient hardware.
no code implementations • 14 Jan 2019 • Pierre Falez, Pierre Tirilly, Ioan Marius Bilasco, Philippe Devienne, Pierre Boulet
Spiking neural networks (SNNs) equipped with latency coding and spike-timing dependent plasticity rules offer an alternative to solve the data and energy bottlenecks of standard computer vision approaches: they can learn visual features without supervision and can be implemented by ultra-low power hardware architectures.