Abstract
Computational modeling is increasingly used to understand the function of neural circuits in systems neuroscience. These studies require models of individual neurons with realistic input–output properties. Recently, it was found that spiking models can accurately predict the precisely timed spike trains produced by cortical neurons in response to somatically injected currents, if properly fitted. This requires fitting techniques that are efficient and flexible enough to easily test different candidate models. We present a generic solution, based on the Brian simulator (a neural network simulator in Python), which allows the user to define and fit arbitrary neuron models to electrophysiological recordings. It relies on vectorization and parallel computing techniques to achieve efficiency. We demonstrate its use on neural recordings in the barrel cortex and in the auditory brainstem, and confirm that simple adaptive spiking models can accurately predict the response of cortical neurons. Finally, we show how a complex multicompartmental model can be reduced to a simple effective spiking model.
Highlights
An increasing number of studies in systems neuroscience rely on computational modeling to understand how function emerges from the interaction of individual neurons
It relies on vectorization and parallel computing techniques to achieve efficiency.We demonstrate its use on neural recordings in the barrel cortex and in the auditory brainstem, and confirm that simple adaptive spiking models can accurately predict the response of cortical neurons
The neuron models used in these studies are usually well established models [e.g., Hodgkin–Huxley (HH), integrateand-fire models, and variations], their parameters are generally gathered from a number of measurements in different neurons and preparations
Summary
An increasing number of studies in systems neuroscience rely on computational modeling to understand how function emerges from the interaction of individual neurons. The model of Rothman and Manis (2003), an established biophysical model of neurons in the cochlear nucleus (CN) of the auditory brainstem, includes ionic channels with properties measured in different neurons of the CN of guinea pigs, combined with a sodium channel and an Ih current derived from previous measurements in mammalian neurons in other areas (including the thalamus) This situation is hardly avoidable for practical reasons, but it raises two questions: (1) channel properties from heterogeneous neuron types, species or ages may not be compatible, and (2) there could be functionally relevant correlations between parameter values within the same neuron type (e.g., maximal conductances), which would be missed if the information about channels came from several independent neurons. It was found that simple phenomenological spiking models, such as integrate-and-fire models with adaptation, can predict the response of cortical neurons to somatically injected currents with surprising accuracy in spike timing
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