Bulletin of the American Physical Society
APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020; Denver, Colorado
Session L23: Physics of the Brain: Structure and Dynamics I
8:00 AM–10:48 AM,
Wednesday, March 4, 2020
Room: 304
Sponsoring
Units:
DBIO GSNP
Chair: Tatyana Sharpee, Salk Institute for Biological Studies
Abstract: L23.00006 : Margin learning in spiking neural networks*
Presenter:
Robert Gütig
(Mathematical modeling of neuronal learning, Charite Medical School Berlin)
Author:
Robert Gütig
(Mathematical modeling of neuronal learning, Charite Medical School Berlin)
Constituting a major breakthrough in machine learning, learning in high-dimensional spaces has been greatly improved by margin techniques that maximize the minimal distance between available training examples and the learned decision boundary. Maximal margin techniques have been applied successfully in artificial neural networks with graded responses where standard metrics apply. By contrast, the use of margin learning has been much less straight forward in networks of spiking neurons that consist of neuron models that (like nerve cells in the brain) respond to inputs by eliciting trains of discrete all-or-nothing events (action potentials).
Recently, we have introduced the spike-threshold-surface to define a continuous distance between the responses of spiking neurons. Here we extend this notion to capture the margins between responses of spiking neurons. We show that a family of gradient-based learning rules that operate on these margins strongly improves the learning capabilities of spiking neurons. We discuss their biologically plausible implementation through empirically observed synaptic learning rules. This work transfers powerful margin-based learning concepts from machine to neurobiological learning.
In collaboration with Timo Wunderlich
*Charite Medical School Berlin, Berlin Institute of Health, ERC Synergy Grant BrainPlay
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