Abstract
BackgroundAn approach to investigate brain function/dysfunction is to simulate neuron circuits on a computer. A problem, however, is that detailed neuron descriptions are computationally expensive and this handicaps the pursuit of realistic network investigations, where many neurons need to be simulated.ResultsWe confront this issue; we employ a novel reduction algorithm to produce a 2 compartment model of the cerebellar Purkinje neuron from a previously published, 1089 compartment model. It runs more than 400 times faster and retains the electrical behavior of the full model. So, it is more suitable for inclusion in large network models, where computational power is a limiting issue. We show the utility of this reduced model by demonstrating that it can replicate the full model’s response to alcohol, which can in turn reproduce experimental recordings from Purkinje neurons following alcohol application.ConclusionsWe show that alcohol may modulate Purkinje neuron firing by an inhibition of their sodium-potassium pumps. We suggest that this action, upon cerebellar Purkinje neurons, is how alcohol ingestion can corrupt motor co-ordination. In this way, we relate events on the molecular scale to the level of behavior.
Highlights
An approach to investigate brain function/dysfunction is to simulate neuron circuits on a computer
We use our Purkinje neuron model – with its detailed and reduced versions - to deliver a hypothesis that can accommodate these observations. We suggest that these results can be understood if alcohol action upon Purkinje cells is via an inhibition of its Na+/K+ pumps
In the left column their activity is shown in entirety, in the right column their dendritic highthreshold calcium spikes are shown at higher resolution
Summary
An approach to investigate brain function/dysfunction is to simulate neuron circuits on a computer. Researchers study neural circuits and find it useful to represent each neuron and synapse as as possible, ignoring much of the biological detail This demarcation is principally because the computational complexity of the former is not conducive to the scaling of the latter. There is a great potential for bridging the gap between these two levels of enquiry, using network models with neurons of intermediate biological fidelity and moderate computational complexity. These studies can unify cellular and networks studies and identify how distinctive single neuron behaviors are important to network and system function
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