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Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data.

Lynch EP, Houghton CJ - Front Neuroinform (2015)

Bottom Line: Spiking neuron models can accurately predict the response of neurons to somatically injected currents if the model parameters are carefully tuned.Predicting the response of in-vivo neurons responding to natural stimuli presents a far more challenging modeling problem.We apply this to parameter discovery in modeling two experimental data sets with spiking neurons; in-vitro current injection responses from a regular spiking pyramidal neuron are modeled using spiking neurons and in-vivo extracellular auditory data is modeled using a two stage model consisting of a stimulus filter and spiking neuron model.

View Article: PubMed Central - PubMed

Affiliation: School of Mathematics, Trinity College Dublin Dublin, Ireland ; Department of Computer Science, University of Bristol Bristol, UK.

ABSTRACT
Spiking neuron models can accurately predict the response of neurons to somatically injected currents if the model parameters are carefully tuned. Predicting the response of in-vivo neurons responding to natural stimuli presents a far more challenging modeling problem. In this study, an algorithm is presented for parameter estimation of spiking neuron models. The algorithm is a hybrid evolutionary algorithm which uses a spike train metric as a fitness function. We apply this to parameter discovery in modeling two experimental data sets with spiking neurons; in-vitro current injection responses from a regular spiking pyramidal neuron are modeled using spiking neurons and in-vivo extracellular auditory data is modeled using a two stage model consisting of a stimulus filter and spiking neuron model.

No MeSH data available.


Performance of the full model on 110 cell data sets plotted against measures of the reliability of the data under two metrics. (A) Shows the best coincidence factor plotted against the intrinsic reliability for each cell. (B) Shows the best van Rossum distance plotted against the cluster size—the average inter-trial van Rossum distance of the experimental data set. +refer to the individual data points; each +corresponds to an individual cell in the cohort of cells studied.
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Figure 7: Performance of the full model on 110 cell data sets plotted against measures of the reliability of the data under two metrics. (A) Shows the best coincidence factor plotted against the intrinsic reliability for each cell. (B) Shows the best van Rossum distance plotted against the cluster size—the average inter-trial van Rossum distance of the experimental data set. +refer to the individual data points; each +corresponds to an individual cell in the cohort of cells studied.

Mentions: The values of best coincidence factor and the best van Rossum distance obtained from the STRF-aEIF model on the validation data set are plotted in Figure 7 against corresponding estimates of the reliability of the data for each cell, that is, the average inter-trial coincidence factor and inter-trial van Rossum distance of the data. In Figure 7A the coincidence factor is plotted against the intrinsic reliability for each cell while in Figure 7B the van Rossum distance is plotted against the “cluster size”; this is the average inter-trial van Rossum distance of the set of experimental validation spike trains.


Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data.

Lynch EP, Houghton CJ - Front Neuroinform (2015)

Performance of the full model on 110 cell data sets plotted against measures of the reliability of the data under two metrics. (A) Shows the best coincidence factor plotted against the intrinsic reliability for each cell. (B) Shows the best van Rossum distance plotted against the cluster size—the average inter-trial van Rossum distance of the experimental data set. +refer to the individual data points; each +corresponds to an individual cell in the cohort of cells studied.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4403314&req=5

Figure 7: Performance of the full model on 110 cell data sets plotted against measures of the reliability of the data under two metrics. (A) Shows the best coincidence factor plotted against the intrinsic reliability for each cell. (B) Shows the best van Rossum distance plotted against the cluster size—the average inter-trial van Rossum distance of the experimental data set. +refer to the individual data points; each +corresponds to an individual cell in the cohort of cells studied.
Mentions: The values of best coincidence factor and the best van Rossum distance obtained from the STRF-aEIF model on the validation data set are plotted in Figure 7 against corresponding estimates of the reliability of the data for each cell, that is, the average inter-trial coincidence factor and inter-trial van Rossum distance of the data. In Figure 7A the coincidence factor is plotted against the intrinsic reliability for each cell while in Figure 7B the van Rossum distance is plotted against the “cluster size”; this is the average inter-trial van Rossum distance of the set of experimental validation spike trains.

Bottom Line: Spiking neuron models can accurately predict the response of neurons to somatically injected currents if the model parameters are carefully tuned.Predicting the response of in-vivo neurons responding to natural stimuli presents a far more challenging modeling problem.We apply this to parameter discovery in modeling two experimental data sets with spiking neurons; in-vitro current injection responses from a regular spiking pyramidal neuron are modeled using spiking neurons and in-vivo extracellular auditory data is modeled using a two stage model consisting of a stimulus filter and spiking neuron model.

View Article: PubMed Central - PubMed

Affiliation: School of Mathematics, Trinity College Dublin Dublin, Ireland ; Department of Computer Science, University of Bristol Bristol, UK.

ABSTRACT
Spiking neuron models can accurately predict the response of neurons to somatically injected currents if the model parameters are carefully tuned. Predicting the response of in-vivo neurons responding to natural stimuli presents a far more challenging modeling problem. In this study, an algorithm is presented for parameter estimation of spiking neuron models. The algorithm is a hybrid evolutionary algorithm which uses a spike train metric as a fitness function. We apply this to parameter discovery in modeling two experimental data sets with spiking neurons; in-vitro current injection responses from a regular spiking pyramidal neuron are modeled using spiking neurons and in-vivo extracellular auditory data is modeled using a two stage model consisting of a stimulus filter and spiking neuron model.

No MeSH data available.