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

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.

Synthetic Data Resource and Benchmarks for Time Cell Analysis and Detection Algorithms

“…Synthetic neural activity datasets are valuable in at least two main ways: evaluating algorithms for detection of important activity features, and for delivering stimuli to in vitro and simulated neurons, so as to provide a more physiological context in which to study input-output properties ( Abbasi et al, 2020 ). While we have deployed our synthetic dataset for the specific purpose of comparing time cell detection algorithms, we suggest that it could also be useful for evaluating sequence analysis algorithms ( Ikegaya et al, 2004 ; Foster and Wilson, 2006 ; Villette et al, 2015 ).…”

Synthetic Data Resource and Benchmarks for Time Cell Analysis and Detection Algorithms

“…Out of these TI-Bo exhibited slightly more Type I errors (higher False Negatives), described by the slightly lower Precision values, as compared to pAUC-Bo. The value of synthetic data in experimental science Synthetic neural activity datasets are valuable in at least two main ways: evaluating algorithms for detection of important activity features, and for delivering stimuli to in-vitro and simulated neurons, so as to provide a more physiological context in which to study input-output properties (Abbasi, Maran, and Jaeger 2020). While we have deployed our synthetic dataset for the specific purpose of comparing time-cell detection algorithms, we suggest that it could also be useful for evaluating sequence analysis algorithms (Foster and Wilson 2006;Ikegaya et al 2004;Villette et al 2015).…”

Modeling synaptic integration of bursty and beta oscillatory inputs in ventromedial motor thalamic neurons in normal and parkinsonian states

“…To represent presynaptic activity, we generated artificial spike trains ( Abbasi et al, 2020 ), with firing rates consistent with experimental estimates (DRI-l: 30 Hz; MOD: 1.1 Hz; RTN: 10 Hz; SNR: 50 Hz; Sirota et al, 2005 ; Huh and Cho, 2016 ; Barrientos et al, 2019 ; Inagaki et al, 2022 ). Specifically, we used an algorithm that generates a random sequence of interspike intervals picked from a γ distribution with a refractory period and “shape” parameter set a priori ( Abbasi et al, 2020 ).…”

“…To represent presynaptic activity, we generated artificial spike trains ( Abbasi et al, 2020 ), with firing rates consistent with experimental estimates (DRI-l: 30 Hz; MOD: 1.1 Hz; RTN: 10 Hz; SNR: 50 Hz; Sirota et al, 2005 ; Huh and Cho, 2016 ; Barrientos et al, 2019 ; Inagaki et al, 2022 ). Specifically, we used an algorithm that generates a random sequence of interspike intervals picked from a γ distribution with a refractory period and “shape” parameter set a priori ( Abbasi et al, 2020 ). We targeted generic spike properties with a coefficient of variation of interspike intervals (CV ISI ) of 0.45 (as observed in SNR during in vivo recordings; Lobb and Jaeger, 2015 ), obtained with a shape of 5 ( ), and a refractory period of 3 ms.…”