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A general method to generate artificial spike train populations matching recorded neurons

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Abstract

We developed a general method to generate populations of artificial spike trains (ASTs) that match the statistics of recorded neurons. The method is based on computing a Gaussian local rate function of the recorded spike trains, which results in rate templates from which ASTs are drawn as gamma distributed processes with a refractory period. Multiple instances of spike trains can be sampled from the same rate templates. Importantly, we can manipulate rate-covariances between spike trains by performing simple algorithmic transformations on the rate templates, such as filtering or amplifying specific frequency bands, and adding behavior related rate modulations. The method was examined for accuracy and limitations using surrogate data such as sine wave rate templates, and was then verified for recorded spike trains from cerebellum and cerebral cortex. We found that ASTs generated with this method can closely follow the firing rate and local as well as global spike time variance and power spectrum. The method is primarily intended to generate well-controlled spike train populations as inputs for dynamic clamp studies or biophysically realistic multicompartmental models. Such inputs are essential to study detailed properties of synaptic integration with well-controlled input patterns that mimic the in vivo situation while allowing manipulation of input rate covariances at different time scales.

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Abbreviations

aGLR:

Adaptive Gaussian Local Rate template

saGLR:

Rate scaled adaptive Local Rate template

AST:

Artificial Spike Train

CV:

Coefficient of Variation

u:

u-fold increase of aGLR rate within single computed ISI (real valued number)

ISI:

Inter-spike Interval

LV:

Local Variation

MF:

Mossy fiber

MT:

Middle temporal area pyramidal neuron

PC:

Purkinje cell

PETH:

Peri Event Time Histogram

SF:

Shift Fraction

sGLR:

Slow Gaussian Local Rate template

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Acknowledgements

This work was supported in part by NIH grant R01NS067201 to D.Jaeger. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Cerebellar data were recorded in the Heck lab at UTHS.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Hamedan University of Technology, Hamedan, 65169-13733, Iran

    Samira Abbasi

  2. Department Biology, Emory University, Atlanta, GA, 30033, USA

    Selva Maran & Dieter Jaeger

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  1. Samira Abbasi
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  2. Selva Maran
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Abbasi, S., Maran, S. & Jaeger, D. A general method to generate artificial spike train populations matching recorded neurons. J Comput Neurosci 48, 47–63 (2020). https://doi.org/10.1007/s10827-020-00741-w

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