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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.


Related in: MedlinePlus

Convergence of the van Rossum distance and coincidence factor in model finding runs. These simulations used synthetic model data as targets. The convergence was measured and averaged over 20 identical runs. The algorithm used the van Rossum distance, d, between the experimental and model spike trains, u and , as the fitness function. The van Rossum distance is normalized by its initial value. (A) shows the average performance of the algorithm. (B) shows the convergence behavior of the coincidence factor with different timescale choice in three cases; using a long timescale (T/2), a short timescale (1/f) and a varying timescale, decreasing from an initial value of T/2 down to 1/f. The dotted lines show the standard deviation from the mean (solid) lines.
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Figure 5: Convergence of the van Rossum distance and coincidence factor in model finding runs. These simulations used synthetic model data as targets. The convergence was measured and averaged over 20 identical runs. The algorithm used the van Rossum distance, d, between the experimental and model spike trains, u and , as the fitness function. The van Rossum distance is normalized by its initial value. (A) shows the average performance of the algorithm. (B) shows the convergence behavior of the coincidence factor with different timescale choice in three cases; using a long timescale (T/2), a short timescale (1/f) and a varying timescale, decreasing from an initial value of T/2 down to 1/f. The dotted lines show the standard deviation from the mean (solid) lines.

Mentions: The purpose of testing the algorithm on artificial target data was to show that the algorithm was capable of finding, near enough, a set of parameters which are known to exist somewhere in the search space. Figure 5 shows the convergence behavior of the algorithm in fitting an aEIF model to a data set consisting of 2 s of simulated aEIF response to a random current input. The population best van Rossum distance and the coincidence factor for the corresponding members were recorded at each iteration and averaged over 20 runs for this plot. As can be seen, a near perfect fit is achieved with a high degree of reliability. The coincidence factor consistently rose to a value close to one between the model and target on repeated runs of the algorithm on the target data generated by an aEIF model. A stringent coincidence window width of δ = 0.5 ms was used for this relatively easy optimization problem; the quantitative single neuron modeling competition used a value of δ = 2 ms in its benchmarking of fits to real in-vitro data.


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

Lynch EP, Houghton CJ - Front Neuroinform (2015)

Convergence of the van Rossum distance and coincidence factor in model finding runs. These simulations used synthetic model data as targets. The convergence was measured and averaged over 20 identical runs. The algorithm used the van Rossum distance, d, between the experimental and model spike trains, u and , as the fitness function. The van Rossum distance is normalized by its initial value. (A) shows the average performance of the algorithm. (B) shows the convergence behavior of the coincidence factor with different timescale choice in three cases; using a long timescale (T/2), a short timescale (1/f) and a varying timescale, decreasing from an initial value of T/2 down to 1/f. The dotted lines show the standard deviation from the mean (solid) lines.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Convergence of the van Rossum distance and coincidence factor in model finding runs. These simulations used synthetic model data as targets. The convergence was measured and averaged over 20 identical runs. The algorithm used the van Rossum distance, d, between the experimental and model spike trains, u and , as the fitness function. The van Rossum distance is normalized by its initial value. (A) shows the average performance of the algorithm. (B) shows the convergence behavior of the coincidence factor with different timescale choice in three cases; using a long timescale (T/2), a short timescale (1/f) and a varying timescale, decreasing from an initial value of T/2 down to 1/f. The dotted lines show the standard deviation from the mean (solid) lines.
Mentions: The purpose of testing the algorithm on artificial target data was to show that the algorithm was capable of finding, near enough, a set of parameters which are known to exist somewhere in the search space. Figure 5 shows the convergence behavior of the algorithm in fitting an aEIF model to a data set consisting of 2 s of simulated aEIF response to a random current input. The population best van Rossum distance and the coincidence factor for the corresponding members were recorded at each iteration and averaged over 20 runs for this plot. As can be seen, a near perfect fit is achieved with a high degree of reliability. The coincidence factor consistently rose to a value close to one between the model and target on repeated runs of the algorithm on the target data generated by an aEIF model. A stringent coincidence window width of δ = 0.5 ms was used for this relatively easy optimization problem; the quantitative single neuron modeling competition used a value of δ = 2 ms in its benchmarking of fits to real in-vitro data.

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.


Related in: MedlinePlus