TY - GEN
T1 - Decoding network activity from LFPS
T2 - A computational approach
AU - Mahmud, Mufti
AU - Travalin, Davide
AU - Hussain, Amir
PY - 2012
Y1 - 2012
N2 - Cognition is one of the main capabilities of mammal brain and understanding it thoroughly requires decoding brain's information processing pathways which are composed of networks formed by complex connectivity between neurons. Mostly, scientists rely on local field potentials (LFPs) averaged over a number of trials to study the effect of stimuli on brain regions under investigation. However, this may not be the right approach when trying to understand the exact neuronal network underlying the neuronal signals. As the LFPs are lumped activity of populations of neurons, their shapes provide fingerprints of the underlying networks. The method presented in this paper extracts shape information of the LFPs, calculate the corresponding current source density (CSD) from the LFPs and decode the underlying network activity. Through simulated LFPs it has been found that differences in LFP shapes lead to different network activity.
AB - Cognition is one of the main capabilities of mammal brain and understanding it thoroughly requires decoding brain's information processing pathways which are composed of networks formed by complex connectivity between neurons. Mostly, scientists rely on local field potentials (LFPs) averaged over a number of trials to study the effect of stimuli on brain regions under investigation. However, this may not be the right approach when trying to understand the exact neuronal network underlying the neuronal signals. As the LFPs are lumped activity of populations of neurons, their shapes provide fingerprints of the underlying networks. The method presented in this paper extracts shape information of the LFPs, calculate the corresponding current source density (CSD) from the LFPs and decode the underlying network activity. Through simulated LFPs it has been found that differences in LFP shapes lead to different network activity.
KW - Local field potentials
KW - brain activity
KW - current source density
KW - neuronal signal
KW - neuronal signal analysis
UR - https://www.scopus.com/pages/publications/84869015220
U2 - 10.1007/978-3-642-34475-6_70
DO - 10.1007/978-3-642-34475-6_70
M3 - Conference contribution
AN - SCOPUS:84869015220
SN - 9783642344749
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 584
EP - 591
BT - Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings
ER -