(submitted to Computational Neuroscience '96 , Bower J.M. ed.)
Cortical neurons in the intact brain show irregular spiking patterns. The origin of this irregularity is unknown. We study theoretically the hypothesis that this irregularity is due to approximate balance between the inhibitory and excitatory inputs into the cortical neurons. We model the cortical circuits by sparsely connected networks of inhibitory and excitatory neurons with relatively strong synapses. We show that a state with balanced excitatory and inhibitory inputs into the cells emerges without fine tuning of the parameters. The balanced state is characterized by strongly chaotic dynamics, even with constant external input. Despite the highly non-linear dynamics of single neurons, such networks respond linearly to external inputs and track changing stimuli on a time scale much smaller than the integration time constant of the neuron.
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