Abstract
Neuronal responses are often characterized by the firing rate as a function of the stimulus mean, or the f-I curve. We introduce a novel classification of neurons into Types A, B-, and B+ according to how f-I curves are modulated by input fluctuations. In Type A neurons, the f-I curves display little sensitivity to input fluctuations when the mean current is large. In contrast, Type B neurons display sensitivity to fluctuations throughout the entire range of input means. Type B- neurons do not fire repetitively for any constant input, whereas Type B+ neurons do. We show that Type B+ behavior results from a separation of time scales between a slow and fast variable. A voltage-dependent time constant for the recovery variable can facilitate sensitivity to input fluctuations. Type B+ firing rates can be approximated using a simple "energy barrier" model.
Original language | English (US) |
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Pages (from-to) | 277-290 |
Number of pages | 14 |
Journal | Journal of Computational Neuroscience |
Volume | 27 |
Issue number | 2 |
DOIs | |
State | Published - 2009 |
Keywords
- Dynamical systems
- Gain
- Hodgkin-Huxley
- Noise
- Phase portrait
- Single neuron
- Slow AHP
- Slowad aptation
- Stimulus fluctuations
- Time scales
- f-I curve
ASJC Scopus subject areas
- Sensory Systems
- Cognitive Neuroscience
- Cellular and Molecular Neuroscience