Unsupervised learning of temporal features for word categorization in a spiking neural network model of the auditory brain

PloS One
Irina HigginsJan W H Schnupp

Abstract

The nature of the code used in the auditory cortex to represent complex auditory stimuli, such as naturally spoken words, remains a matter of debate. Here we argue that such representations are encoded by stable spatio-temporal patterns of firing within cell assemblies known as polychronous groups, or PGs. We develop a physiologically grounded, unsupervised spiking neural network model of the auditory brain with local, biologically realistic, spike-time dependent plasticity (STDP) learning, and show that the plastic cortical layers of the network develop PGs which convey substantially more information about the speaker independent identity of two naturally spoken word stimuli than does rate encoding that ignores the precise spike timings. We furthermore demonstrate that such informative PGs can only develop if the input spatio-temporal spike patterns to the plastic cortical areas of the model are relatively stable.

Citations

Jun 29, 1992·Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences·E F Evans
Oct 1, 1996·Proceedings of the National Academy of Sciences of the United States of America·D DebanneS M Thompson
Jul 29, 2000·Hearing Research·A Recio, William S Rhode
Oct 26, 2000·Proceedings of the National Academy of Sciences of the United States of America·D OertelP X Joris
Nov 30, 2000·Proceedings of the National Academy of Sciences of the United States of America·J J Hopfield, C D Brody
Oct 16, 2004·IEEE Transactions on Neural Networks·E M Izhikevich
Nov 19, 2004·The Journal of Neuroscience : the Official Journal of the Society for Neuroscience·Nachum UlanovskyIsrael Nelken
Dec 22, 2005·Biological cybernetics·Simon M StringerJ H Proske
Dec 28, 2005·Neural Computation·Eugene M Izhikevich
Jan 18, 2007·The Journal of the Acoustical Society of America·Ray Meddis, Lowel P O'Mard
Feb 16, 2007·Hearing Research·Israel Nelken, Gal Chechik
Feb 5, 2008·IEEE Transactions on Neural Networks·E M Izhikevich
Jun 18, 2009·Hearing Research·William S RhodeAlberto Recio-Spinoso
Nov 10, 2009·The Journal of the Acoustical Society of America·Muhammad S A ZilanyLaurel H Carney
Apr 8, 2010·The Journal of Neuroscience : the Official Journal of the Society for Neuroscience·Jennifer K BizleyJan W H Schnupp
Aug 1, 2012·Frontiers in Computational Neuroscience·Benjamin D Evans, Simon M Stringer
Aug 1, 2012·Frontiers in Computational Neuroscience·James M TromansSimon M Stringer
Jul 12, 2013·The Journal of Neuroscience : the Official Journal of the Society for Neuroscience·Stefan Klampfl, Wolfgang Maass
Nov 27, 2015·The Journal of Neuroscience : the Official Journal of the Society for Neuroscience·Ruben A Tikidji-HamburyanCarmen C Canavier
Sep 15, 2016·Neural Computation·Yuwei CuiJeff Hawkins
Dec 9, 2016·The Journal of Neuroscience : the Official Journal of the Society for Neuroscience·Izzet B YildizSophie Deneve

Related Concepts

In Silico
Biological Neural Networks
Entire Auditory Pathway
Patterns
Phosphatidylglycerols
Methyl Green
Projections and Predictions
Memory Training
Neurons
Brain

Related Feeds

Barrel cortex

Here is the latest research on barrel cortex, a region of somatosensory and motor corticies in the brain, which are used by animals that rely on whiskers for world exploration.

Cardiac Conduction System

The cardiac conduction system is a specialized tract of myocardial cells responsible for maintaining normal cardiac rhythm. Discover the latest research on the cardiac conduction system here.

Related Papers

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Lucas C Parra, Marom Bikson
© 2020 Meta ULC. All rights reserved