Improving STDP-based Visual Feature Learning with Whitening

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.

Computational Efficiency General Classification +1

Multi-layered Spiking Neural Network with Target Timestamp Threshold Adaptation and STDP

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.

General Classification

Unsupervised Visual Feature Learning with Spike-timing-dependent Plasticity: How Far are we from Traditional Feature Learning Approaches?

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.

General Classification Image Classification