Mutual information based measures on complex interdependent networks of neuro data sets
Author(s)
Abdul Razak, Fatimah
Type
Thesis
Abstract
We assume that even the simplest model of the brain is nonlinear and ‘causal’. Proceeding
with the first assumption, we need a measure that is able to capture nonlinearity and
hence Mutual Information whose variants includes Transfer Entropy is chosen. The second
assumption of ‘causality’ is defined in relation to prediction ala Granger causality. Both
these assumptions led us to Transfer Entropy. We take the simplest case of Transfer Entropy,
redefine it for our purposes of detecting causal lag and proceed with a systematic
investigation of this value. We start off with the Ising model and then moved on to created
an amended Ising model where we attempted to replicate ‘causality’. We do the same for
a toy model that can be calculated analytically and thus simulations can be compared to
its theoretical value. Lastly, we tackle a very interesting EEG data set where Transfer Entropy
shall be used on different frequency bands to display possible emergent property of
‘causality’ and detect possible candidates for causal lag on the data sets.
with the first assumption, we need a measure that is able to capture nonlinearity and
hence Mutual Information whose variants includes Transfer Entropy is chosen. The second
assumption of ‘causality’ is defined in relation to prediction ala Granger causality. Both
these assumptions led us to Transfer Entropy. We take the simplest case of Transfer Entropy,
redefine it for our purposes of detecting causal lag and proceed with a systematic
investigation of this value. We start off with the Ising model and then moved on to created
an amended Ising model where we attempted to replicate ‘causality’. We do the same for
a toy model that can be calculated analytically and thus simulations can be compared to
its theoretical value. Lastly, we tackle a very interesting EEG data set where Transfer Entropy
shall be used on different frequency bands to display possible emergent property of
‘causality’ and detect possible candidates for causal lag on the data sets.
Version
Open Access
Date Issued
2013-03
Date Awarded
2013-06
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
Advisor
Jensen, Henrik
Christensen, Kim
Sponsor
Malaysia ; Universiti Kebangsaan Malaysia
Publisher Department
Mathematics
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)