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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.
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https://hal.archives-ouvertes.fr/hal-00706681
Contributor : Catherine Marlot <>
Submitted on : Monday, June 11, 2012 - 12:10:24 PM
Last modification on : Thursday, November 5, 2020 - 9:14:02 AM

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Olivier Bichler, Damien Querlioz, Simon J Thorpe, Jean-Philippe Bourgoin, Christian Gamrat. Extraction of temporally correlated features from dynamic vision sensors with spike-timing-dependent plasticity.. Neural Networks, Elsevier, 2012, 32, pp.339-48. ⟨10.1016/j.neunet.2012.02.022⟩. ⟨hal-00706681⟩

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