Abstracts

RATIONALE: In previous work we reported studies of neural network models that can reproduce the spread of synchronous bursting activity in response to brief current stimulation. The objective of the present studies is to establish how various frequencies of periodic stimulation of a neural network affect the firing patterns of single neurons and the spatiotemporal patterns of activity of neurons in the entire network.
METHODS: The model consists of a square array of locally connected excitatory neurons (2500 neurons). A regenerative neuron model (Hodgkin[ndash]Huxley type) is used to simulate single neurons. For simulations of the spread of bursting activity, each neuron is synaptically connected with two randomly chosen neurons from its immediate neighborhood of eight neurons. The activity in the network is triggered by one neuron, which receives a periodic depolarizing input current during simulation.
RESULTS: Periodic stimulation with a frequency in a range of 2 [ndash] 10 Hz causes a sequence of propagating activity waves through the network. The observed frequencies of bursts in single neurons can vary over a range of 1.5 [ndash] 5 Hz and the maximal burst frequency depends mainly on the recovery time from afterhyperpolarization process in the neurons. Long periodic stimulation leads to complex wave interactions resembling behaviors seen in a number of different distributed non-linear chemical systems and the turning of the periodic stimulation does not result in cessation of this activity.
CONCLUSIONS: Model networks of locally connected excitable neurons are capable of producing patterns of activity similar to the spread of epileptic seizure activity in neocortex. The firing frequency of neurons in the network is determined by the intrinsic properties of the membrane model. Long periodic stimulation produces complex self-sustained oscillations in the network. Patterns of bursting in intact animal models may similarly be affected by specific network properties.
Support: NIH grant NS 38958.