2018-04-11 at 14:00
Passage De L'Innovation 74 Rue du Faubourg Saint-Antoine, 75012 Paris, France
Spike-based computing and learning in brains, machines, and visual systems in particular
We have first shown that, thanks to the physiological learning mechanism referred to as spike timing-dependent plasticity (STDP), neurons can detect and learn repeating spike patterns, in an unsupervised manner, even when those patterns are embedded in noise, and the detection can be optimal. Importantly, the spike patterns do not need to repeat exactly: it also works when only a firing probability pattern repeats, providing this profile has narrow (10-20ms) temporal peaks. Brain oscillations may help in getting the required temporal precision, in particular when dealing with slowly changing stimuli. All together, these studies show that some envisaged problems associated to spike timing codes, in particular noise-resistance, the need for a reference time, or the decoding issue, might not be as severe as once thought. These generic STDP-based mechanisms are probably at work in particular the visual system, where they can explain how selectivity to visual primitives emerges, leading to efficient object recognition. High spike time precision is required, and microsaccades could help. All these mechanisms are appealing for neuromorphic engineering applications. They can lead to fast, energy efficient systems which can learn online, in an unsupervised manner.
Dr. Masquelier is a computational neuroscientist. His research domain is highly interdisciplinary - at the interface between biology, computer science, and physics. He uses numerical simulations and analytical calculations to gain understanding on how the brain works, and more specifically on how neurons process, encode and transmit information through action potentials (a.k.a spikes), in particular, in the visual modality.He is especially interested in bio-inspired computer vision and neuromorphic engineering. Dr Masquelier was trained at Ecole Centrale Paris (Ingénieur 1999), MIT (M. Sc. 2001), and Univ. Toulouse 3 (PhD 2008) and recruited by the CNRS in 2012.