Predicting Spike Activity in Neuronal Cultures
Gurel, T.
Egert, U.
Kandler, S.
De Raedt, L.
Rotter, S.
This paper appears in: Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Publication Date: 12-17 Aug. 2007
On page(s): 2942-2947
Location: Orlando, FL,
ISSN: 1098-7576
ISBN: 978-1-4244-1380-5
INSPEC Accession Number: 9811823
Digital Object Identifier: 10.1109/IJCNN.2007.4371428
Current Version Published: 2007-10-29
Abstract
Neuronal cultures are small living networks in a closed system. This paper investigates the question whether it is possible to discover the functional connectivity and to model the dynamics of such neuronal cultures. Doing so may contribute to a better understanding of neural information processing. We employ a machine learning approach, which constructs the functional connectivity map of a neuronal culture based on multiple spike trains of its spontaneous activity recorded with Multi-Electrode-Array (MEA) technology. The spike train of an electrode is modeled as a point process, where the firing probability depends on the finite spike history of all electrodes. To capture potential plasticity of the network, we employ a gradient descent method, which naturally allows for online learning. Several experiments with different cultures show that learned models can predict upcoming spike activity quite well.
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