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Introducing heterogeneities in biological neuronal network models with distance-dependent connectivity
KTH, School of Engineering Sciences (SCI).
KTH, School of Engineering Sciences (SCI).
2018 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Introducerande av heterogeniteter i biologiska neuronala nätverksmodeller med avståndsberoende kopplingsförmåga (Swedish)
Abstract [en]

Computational studies of biological neuronal network dynamics are often conducted on isotropic and homogeneous network models where all neurons are assumed to be identical. Here, we look at three different spatially extended networks and introduce heterogeneities in the connectivity. The first resembles the classical isotropic approach and connections are equally probable to be made in every direction. In the other two networks we impose preferred directions in recurrent excitatory connections. All excitatory neurons in the second network has a higher probability to connect in one direction. In the third network nearby neurons prefer similar directions, but distant neurons remain uncorrelated. We further analyze the irregularity of spike-timings of individual neurons, the synchrony of the network, network oscillations, as well as the flow of activity. Finally, we show that meaningful behavior can be generated if nearby neurons prefer to connect in similar directions.

Abstract [sv]

Vid analys av dynamiken hos biologiska neuronala nätverk används ofta isotropa och homo- gena nätverksmodeller där alla neuroner antas vara identiska. Här tittar vi på tre nätverk med rumslig utbredning och introducerar heterogeniteter i kopplingarna. Det första påminner om det klassiska isotropa fallet och kopplingar görs med samma sannolikhet i alla riktningar. I de andra två nätverken inför vi kopplingsriktningar som är mer sannolika i de återkom- mande excitatoriska kopplingarna. Alla excitatoriska neuroner i det andra nätverket har en större sannolikhet att koppla i en riktning. I det tredje nätverket föredrar närliggande neu- roner att koppla i samma riktning, medan neuroner på större avstånd är oberoende. Vidare analyserar vi irreguljäriteten av tiderna då enstaka neuroner aktiveras, nätverkets synkroni- tet, nätverksoscillationer, samt flödet av aktiviten. Slutligen så visar vi att ett meningsfullt nätverksbeteende kan genereras om närliggande neuroner föredrar liknande kopplingsrikt- ningar.

Place, publisher, year, edition, pages
2018. , p. 39
Series
TRITA-SCI-GRU ; 2018-086
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-230745OAI: oai:DiVA.org:kth-230745DiVA, id: diva2:1219132
Supervisors
Examiners
Available from: 2018-06-15 Created: 2018-06-15 Last updated: 2018-06-15Bibliographically approved

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