Spiking behavior and epileptiform oscillations in a discrete model of cortical neural networks |
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Authors: | Daniel Volk |
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Institution: | 1. Institute for Theoretical Physics, Cologne University, 50937 Köln, Germany |
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Abstract: | Summary We investigate the phenomenon of epileptiform activity using a discrete model of cortical neural networks. Our model is reduced
to the elementary features of neurons and assumes simplified dynamics of action potentials and postsynaptic potentials. The
discrete model provides a comparably high simulation speed which allows the rendering of phase diagrams and simulations of
large neural networks in reasonable time. Further the reduction to the basic features of neurons provides insight into the
essentials of a possible mechanism of epilepsy. Our computer simulations suggest that the detailed dynamics of postsynaptic
and action potentials are not indispensable for obtaining epileptiform behavior on the system level. The simulation results
of autonomously evolving networks exhibit a regime in which the network dynamics spontaneously switch between fluctuating
and oscillating behavior and produce isolated network spikes without external stimulation. Inhibitory neurons have been found
to play an important part in the synchronization of neural firing: an increased number of synapses established by inhibitory
neurons onto other neurons induces a transition to the spiking regime. A decreased frequency accompanying the hypersynchronous
population activity has only occurred with slow inhibitory postsynaptic potentials. |
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Keywords: | Epilepsy neural network model cellular automaton synchronous activity inhibitory synapses |
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