ArticleBiological cybernetics2008
Predicting the activity phase of a follower neuron with A-current in an inhibitory network.
Article in Biological cybernetics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Short-term synaptic dynamics control the activity phase of neurons in an oscillatory network.eLife · 2019Article
- The membrane potential waveform of bursting pacemaker neurons is a predictor of their preferred frequency and the network cycle frequency.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2010Article
- The influence of the A-current on the dynamics of an oscillator-follower inhibitory network.SIAM journal on applied dynamical systems · 2009Article
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Authors and funding
3 authors.
Funding
Abstract
The transient potassium A-current is present in most neurons and plays an important role in determining the timing of action potentials. We examine the role of the A-current in the activity phase of a follower neuron in a rhythmic feed-forward inhibitory network with a reduced three-variable model and conduct experiments to verify the usefulness of our model. Using geometric analysis of dynamical systems, we explore the factors that determine the onset of activity in a follower neuron following release from inhibition. We first analyze the behavior of the follower neuron in a single cycle and find that the phase plane structure of the model can be used to predict the potential behaviors of the follower neuron following release from inhibition. We show that, depending on the relative scales of the inactivation time constant of the A-current and the time constant of the recovery variable, the follower neuron may or may not reach its active state following inhibition. Our simple model is used to derive a recursive set of equations to predict the contribution of the A-current parameters in determining the activity phase of a follower neuron as a function of the duration and frequency of the inhibitory input it receives. These equations can be used to demonstrate the dependence of activity phase on the period and duty cycle of the periodic inhibition, as seen by comparing the predictions of the model with the activity of the pyloric constrictor (PY) neurons in the crustacean pyloric network.
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