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Recognition of handwritten digits using sparse codes generated by local feature extraction methods
KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA. KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.ORCID iD: 0000-0002-2358-7815
2006 (English)In: ESANN'2006: 14th European Symposium on Artificial Neural Networks, 2006, 161-166 p.Conference paper, Published paper (Refereed)
Abstract [en]

We investigate when sparse coding of sensory inputs canimprove performance in a classification task. For this purpose, we use astandard data set, the MNIST database of handwritten digits. We systematicallystudy combinations of sparse coding methods and neural classifiersin a two-layer network. We find that processing the image data intoa sparse code can indeed improve the classification performance, comparedto directly classifying the images. Further, increasing the level of sparsenessleads to even better performance, up to a point where the reductionof redundancy in the codes is offset by loss of information.

Place, publisher, year, edition, pages
2006. 161-166 p.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-6305ISBN: 2-930307-06-4 (print)OAI: oai:DiVA.org:kth-6305DiVA: diva2:10984
Conference
ESANN'2006 - European Symposium on Artificial Neural Networks. Bruges, Belgium. 26-28 April 2006
Note
QC 20100916Available from: 2006-11-01 Created: 2006-11-01 Last updated: 2011-12-20Bibliographically approved
In thesis
1. Aspects of memory and representation in cortical computation
Open this publication in new window or tab >>Aspects of memory and representation in cortical computation
2006 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [sv]

Denna avhandling i datalogi föreslår modeller för hur vissa beräkningsmässiga uppgifter kan utföras av hjärnbarken. Utgångspunkten är dels kända fakta om hur en area i hjärnbarken är uppbyggd och fungerar, dels etablerade modellklasser inom beräkningsneurobiologi, såsom attraktorminnen och system för gles kodning. Ett neuralt nätverk som producerar en effektiv gles kod i binär mening för sensoriska, särskilt visuella, intryck presenteras. Jag visar att detta nätverk, när det har tränats med naturliga bilder, reproducerar vissa egenskaper (receptiva fält) hos nervceller i lager IV i den primära synbarken och att de koder som det producerar är lämpliga för lagring i associativa minnesmodeller. Vidare visar jag hur ett enkelt autoassociativt minne kan modifieras till att fungera som ett generellt sekvenslärande system genom att utrustas med synapsdynamik. Jag undersöker hur ett abstrakt attraktorminnessystem kan implementeras i en detaljerad modell baserad på data om hjärnbarken. Denna modell kan sedan analyseras med verktyg som simulerar experiment som kan utföras på en riktig hjärnbark. Hypotesen att hjärnbarken till avsevärd del fungerar som ett attraktorminne undersöks och visar sig leda till prediktioner för dess kopplingsstruktur. Jag diskuterar också metodologiska aspekter på beräkningsneurobiologin idag.

Abstract [en]

In this thesis I take a modular approach to cortical function. I investigate how the cerebral cortex may realise a number of basic computational tasks, within the framework of its generic architecture. I present novel mechanisms for certain assumed computational capabilities of the cerebral cortex, building on the established notions of attractor memory and sparse coding. A sparse binary coding network for generating efficient representations of sensory input is presented. It is demonstrated that this network model well reproduces the simple cell receptive field shapes seen in the primary visual cortex and that its representations are efficient with respect to storage in associative memory. I show how an autoassociative memory, augmented with dynamical synapses, can function as a general sequence learning network. I demonstrate how an abstract attractor memory system may be realised on the microcircuit level -- and how it may be analysed using tools similar to those used experimentally. I outline some predictions from the hypothesis that the macroscopic connectivity of the cortex is optimised for attractor memory function. I also discuss methodological aspects of modelling in computational neuroscience.

Place, publisher, year, edition, pages
Stockholm: KTH, 2006. xiv, 99 p.
Series
Trita-NA, ISSN 0348-2952 ; 2006:17
Keyword
cerebral cortex, neural networks, attractor memory, sequence learning, biological vision, generative models, serial order, computational neuroscience, dynamical synapses
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-4161 (URN)91-7178-478-0 (ISBN)
Public defence
2006-11-13, F3, KTH, Lindstedtsvägen 26, Stockholm, 14:15
Opponent
Supervisors
Note
QC 20100916Available from: 2006-11-01 Created: 2006-11-01 Last updated: 2010-09-16Bibliographically approved

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http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2006-85.pdf

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