Séminaire Donnees et APprentissage ArtificielRSS

Spike-based computing and learning in brains, machines, and visual systems in particular


28/03/2013
Intervenant(s) : Timothée Masquelier (Adaptive NeuroComputation Group, UPMC)
Using simulations, 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[1,2]. 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[3]. Brain oscillations may help in getting the required temporal precision[4], 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 visualsystem, where they can explain how selectivity to visual primitives emerges[5,6], leading to very reactive systems. I am now investigating if they are also at work in the somatosensory system. Finally, these mechanisms are also appealing for neuromorphic engineering: they can be efficiently implemented in hardware, leading to fast systems with self-learning abilities[7].
References 1 Masquelier, T. et al. (2008) Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains. PLoS ONE 3, e1377 2 Masquelier, T. et al. (2009) Competitive STDP-Based Spike Pattern Learning. Neural Comput 21, 1259–1276 3 Gilson, M. et al. (2011) STDP allows fast rate-modulated coding with Poisson-like spike trains. PLoS Comput Biol 7, e1002231 4 Masquelier, T. et al. (2009) Oscillations, phase-of-firing coding, and spike timing-dependent plasticity: an efficient learning scheme. The Journal of neuroscience 29, 13484–93 5 Masquelier, T. and Thorpe, S.J. (2007) Unsupervised learning of visual features through spike timing dependent plasticity. PLoS Comput Biol 3, e31 6 Masquelier, T. (2012) Relative spike time coding and STDP-based orientation selectivity in the early visual system in natural continuous and saccadic vision: a computational model. Journal of computational neuroscience 32, 425–41 7 Zamarreño-Ramos, C. et al. (2011) On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex. Frontiers in neuroscience 5, 22
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