DocumentCode :
1723045
Title :
Application of Poisson-based hidden Markov models to in vitro neuronal data
Author :
Xydas, Dimitris ; Spencer, Matthew C. ; Downes, Julia H. ; Hammond, Mark W. ; Becerra, Victor M. ; Warwick, Kevin ; Whalley, Benjamin J. ; Nasuto, Slawomir J.
Author_Institution :
Cybern. Res. Group, Univ. of Reading, Reading, UK
fYear :
2010
Firstpage :
1
Lastpage :
6
Abstract :
Recent advances in electrophysiological techniques have made it possible to culture in vitro biological networks and closely monitor ensemble neuronal activity using multi-electrode recording techniques. One of the main challenges in this area of research is attempting to understand how intrinsic activity is propagated within these neuronal networks and how it may be manipulated via external stimuli in order to harness their computational capacity. This raises the question of what similarities and differences arise between spontaneous and evoked responses and how external stimulation can be optimally applied in order to robustly control the neuronal plasticity of neuronal cultures. In this paper we present in detail an application of machine learning methods, specifically hidden Markov models with Poisson-based output distributions, with which we aim to perform comparative studies between spontaneous and evoked neuronal activity over different ages of network development.
Keywords :
Markov processes; learning (artificial intelligence); medical computing; neural nets; neurophysiology; Poisson-based hidden Markov models; Poisson-based output distributions; in vitro neuronal data; machine learning methods; network development; neuronal cultures; neuronal networks; neuronal plasticity; Analytical models; Computational modeling; Data models; Electrodes; Hidden Markov models; In vitro; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cybernetic Intelligent Systems (CIS), 2010 IEEE 9th International Conference on
Conference_Location :
Reading
Print_ISBN :
978-1-4244-9023-3
Electronic_ISBN :
978-1-4244-9024-0
Type :
conf
DOI :
10.1109/UKRICIS.2010.5898094
Filename :
5898094
Link To Document :
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