Parabolic bursting in an excitable system coupled with a slow oscillation
SIAM Journal on Applied Mathematics
Synchrony in excitatory neural networks
Neural Computation
Computation in a single Neuron: Hodgkin and Huxley revisited
Neural Computation
What causes a Neuron to spike?
Neural Computation
Single neuron computation: From dynamical system to feature detector
Neural Computation
Type i membranes, phase resetting curves, and synchrony
Neural Computation
Journal of Computational Neuroscience
Linking dynamical and functional properties of intrinsically bursting neurons
Journal of Computational Neuroscience
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Neurons in the nervous system exhibit an outstanding variety of morphological and physiological properties. However, close to threshold, this remarkable richness may be grouped succinctly into two basic types of excitability, often referred to as type I and type II. The dynamical traits of these two neuron types have been extensively characterized. It would be interesting, however, to understand the information-processing consequences of their dynamical properties. To that end, here we determine the differences between the stimulus features inducing firing in type I and type II neurons. We work with both realistic conductance-based models and minimal normal forms. We conclude that type I neurons fire in response to scale-free depolarizing stimuli. Type II neurons, instead, are most efficiently driven by input stimuli containing both depolarizing and hyperpolarizing phases, with significant power in the frequency band corresponding to the intrinsic frequencies of the cell.