title: Extraction of temporally correlated features from dynamic vision sensors with spike-timing-dependent plasticity.
abstract: A biologically inspired approach to learning temporally correlated patterns from a spiking silicon retina is presented. Spikes are generated from the retina in response to relative changes in illumination at the pixel level and transmitted to a feed-forward spiking neural network. Neurons become sensitive to patterns of pixels with correlated activation times, in a fully unsupervised scheme. This is achieved using a special form of Spike-Timing-Dependent Plasticity which depresses synapses that did not recently contribute to the post-synaptic spike activation, regardless of their activation time. Competitive learning is implemented with lateral inhibition. When tested with real-life data, the system is able to extract complex and overlapping temporally correlated features such as car trajectories on a freeway, after only 10 min of traffic learning. Complete trajectories can be learned with a 98% detection rate using a second layer, still with unsupervised learning, and the system may be used as a car counter. The proposed neural network is extremely robust to noise and it can tolerate a high degree of synaptic and neuronal variability with little impact on performance. Such results show that a simple biologically inspired unsupervised learning scheme is capable of generating selectivity to complex meaningful events on the basis of relatively little sensory experience.
authors.affiliation: CEA, LIST, Embedded Computing Laboratory, 91191 Gif-sur-Yvette Cedex, France. olivier.bichler@cea.fr
authors.forename: Olivier
authors.initials: O
authors.lastname: Bichler
authors.forename: Damien
authors.initials: D
authors.lastname: Querlioz
authors.forename: Simon J
authors.initials: SJ
authors.lastname: Thorpe
authors.forename: Jean-Philippe
authors.initials: JP
authors.lastname: Bourgoin
authors.forename: Christian
authors.initials: C
authors.lastname: Gamrat
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D017774:Long-Term Potentiation; D008957:Models, Genetic; D018986:Motor Vehicles; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity; D009474:Neurons / Q000502:physiology; D015203:Reproducibility of Results; D012160:Retina / Q000502:physiology*; D059629:Signal-To-Noise Ratio; D012825:Silicon; D013569:Synapses / Q000502:physiology; D009435:Synaptic Transmission / Q000502:physiology
chemical_list: D012825:Silicon