Title: Predicting spike activity in neuronal cultures
Authors: Guerel, Tayfun ×
Egert, Ulrich
Kandler, Steffen
De Raedt, Luc
Rotter, Stefan #
Issue Date: 2007
Publisher: IEEE
Host Document: Proceedings of the International Joint Conference on Neural Networks 2007 pages:2942-2947
Conference: International Joint Conference on Neural Networks (IJCNN) location:Orlando, Florida, USA date:August 12-17, 2007
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.
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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