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Spike-based computing and learning in brain, machines, and visual systems in particular| old_uid | 12155 |
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| title | Spike-based computing and learning in brain, machines, and visual systems in particular |
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| start_date | 2013/03/01 |
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| schedule | 14h |
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| online | no |
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| details | Invited by A. Arleo. |
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| summary | 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 visual system, 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]. |
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| responsibles | Wehrle |
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