TY - GEN
T1 - Good-enough brain model
T2 - 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014
AU - Papalexakis, Evangelos E.
AU - Fyshe, Alona
AU - Sidiropoulos, Nicholas D.
AU - Talukdar, Partha Pratim
AU - Mitchell, Tom M.
AU - Faloutsos, Christos
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2014
Y1 - 2014
N2 - Given a simple noun such as apple, and a question such as "is it edible?", what processes take place in the human brain? More specifically, given the stimulus, what are the interactions between (groups of) neurons (also known as functional connectivity) and how can we automatically infer those interactions, given measurements of the brain activity? Furthermore, how does this connectivity differ across different human subjects? In this work we present a simple, novel good-enough brain model, or GeBM in short, and a novel algorithm Sparse-SysId, which are able to effectively model the dynamics of the neuron interactions and infer the functional connectivity. Moreover, GeBM is able to simulate basic psychological phenomena such as habituation and priming (whose definition we provide in the main text). We evaluate GeBM by using both synthetic and real brain data. Using the real data, GeBM produces brain activity patterns that are strikingly similar to the real ones, and the inferred functional connectivity is able to provide neuroscientific insights towards a better understanding of the way that neurons interact with each other, as well as detect regularities and outliers in multi-subject brain activity measurements.
AB - Given a simple noun such as apple, and a question such as "is it edible?", what processes take place in the human brain? More specifically, given the stimulus, what are the interactions between (groups of) neurons (also known as functional connectivity) and how can we automatically infer those interactions, given measurements of the brain activity? Furthermore, how does this connectivity differ across different human subjects? In this work we present a simple, novel good-enough brain model, or GeBM in short, and a novel algorithm Sparse-SysId, which are able to effectively model the dynamics of the neuron interactions and infer the functional connectivity. Moreover, GeBM is able to simulate basic psychological phenomena such as habituation and priming (whose definition we provide in the main text). We evaluate GeBM by using both synthetic and real brain data. Using the real data, GeBM produces brain activity patterns that are strikingly similar to the real ones, and the inferred functional connectivity is able to provide neuroscientific insights towards a better understanding of the way that neurons interact with each other, as well as detect regularities and outliers in multi-subject brain activity measurements.
KW - brain activity analysis
KW - brain functional connectivity
KW - control theory
KW - neuroscience
KW - system identification
UR - http://www.scopus.com/inward/record.url?scp=84907025300&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84907025300&partnerID=8YFLogxK
U2 - 10.1145/2623330.2623639
DO - 10.1145/2623330.2623639
M3 - Conference contribution
AN - SCOPUS:84907025300
SN - 9781450329569
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 95
EP - 104
BT - KDD 2014 - Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
Y2 - 24 August 2014 through 27 August 2014
ER -