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rufin

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Registered: 2019-03-08

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March 28th, 2019, 2pm - SUPAERO - Tim Masquelier (CerCo, CNRS)

Title: Spike-based computing and learning in brains, machines, and visual systems in particular
By:    Tim Masquelier, CerCo (CNRS).

Abstract: In recent years, deep learning has been a revolution in the field of machine learning, for computer vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is usually trained in a supervised manner, using a stochastic gradient descent method known as backpropagation. Huge amounts of labeled examples are required, but the resulting classification accuracy is truly impressive, often outperforming humans. Neurons in an ANN are characterized by a single, static, continuous-valued activation. Yet biological neurons use discrete electrical impulses called spikes to compute and transmit information, and the spike times, in addition to the spike rates, matter. Spiking neural networks (SNNs) are thus more biologically realistic than ANNs, and arguably the only viable option if one wants to understand how the brain computes. SNNs are also more hardware friendly and energy-efficient than ANNs, and are thus appealing for technology, especially for edge computing. However, training deep SNNs remains a challenge. Spiking neurons' transfer function is usually non-differentiable, which prevents using classic backpropagation. I will review recent supervised and unsupervised methods to train deep SNNs, and compare them in terms of accuracy, but also computational cost and hardware friendliness.

Location: ISAE-SUPAERO, room 05.035
Access to the ISAE-SUPAERO campus requires that you send an email to emmanuel.rachelson@isae-supaero.fr at least the day before the seminar.