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Analysis of an Attractor Neural Network Model for Working Memory: A Control Theory Approach
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Working Memory (WM) is a general-purpose cognitive system responsible for temporaryholding information in service of higher order cognition systems, e.g. decision making. Inthis thesis we focus on a non-spiking model belonging to a special family of biologicallyinspired recurrent Artificial Neural Network aiming to account for human experimentaldata on free recall. Considering its modular structure, this thesis gives a networked systemrepresentation of WM in order to analyze its stability and synchronization properties.Furthermore, with the tools provided by bifurcation analysis we investigate the role of thedifferent parameters on the generated synchronized patterns. To the best of our knowledge,the proposed dynamical recurrent neural network has not been studied before froma control theory perspective.

Abstract [sv]

Arbetsminne är ett brett, övergripande kognitivt system som ansvarar för temporär informationslagringhos högre ordningens tänkande, såsom beslutsfattning. Denna masteravhandlingämnar sig åt att studera icke-spikande modeller tillhörande en speciell gren avbiologiskt inspirerade återkommande neuronnät, för att redogöra mänsklig experimentelldata för fenomenet free recall. Med avseende på dess modulära struktur, framför denna avhandlingen nätverkssystemsrepresentation av arbetsminne sådant att dess stabilitets- samtsynkroniseringsegenskaper kan granskas. Innebörden av olika systemparametrar av de genereradesynkroniseringsmönstren undersöktes genom användandet av bifurkationsanalys.Som vi förstår, har den föreslagna dynamiska återkommande neuronätet inte studerats frånett reglertekniskt perspektiv tidigare.

Place, publisher, year, edition, pages
2019. , p. 44
Series
TRITA-EECS-EX ; 2019:524
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-260079OAI: oai:DiVA.org:kth-260079DiVA, id: diva2:1354510
Educational program
Master of Science - Systems, Control and Robotics
Supervisors
Examiners
Available from: 2019-09-25 Created: 2019-09-25 Last updated: 2019-09-25Bibliographically approved

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