Limits...
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.


An illustration of the effect of filter width on van Rossum metric; (A) Shows two spike trains, one in black, the other in blue, (B) Shows the functions obtained by filtering the spike trains with a causal exponential with a time constant of τ = 10 ms, and (C) Shows the same trains filtered with time constant of 50 ms, 5 × τ.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4403314&req=5

Figure 2: An illustration of the effect of filter width on van Rossum metric; (A) Shows two spike trains, one in black, the other in blue, (B) Shows the functions obtained by filtering the spike trains with a causal exponential with a time constant of τ = 10 ms, and (C) Shows the same trains filtered with time constant of 50 ms, 5 × τ.

Mentions: Often, some sort of metric clustering based optimization routine is used to pick the best timescale τ for a data set (Victor and Purpura, 1997). Here, however, different timescales are used to explore different points in the spike trains in our genetic algorithm. Different values of τ make the metric sensitive to features on different timescales in the spike trains. A long timescale allows similarities in large scale features to be detected, for example, the mean firing rate, while a short timescale will be more sensitive to the differences between very similar spike trains. This is illustrated by the example in Figure 2. Figure 2A shows two spike trains. In Figure 2B the spike trains have been filtered to form functions with a short timescale. The functions formed by filtering the spike trains only significantly overlap when spikes from each train are close to each other. Figure 2C demonstrates that for a long timescale, very different spike trains can produce relatively similar functions. To demonstrate the effect of this on model optimization, we will later present the result of using a variable timescale which starts at a value of the order of the length of the target data set and is then reduced to a value roughly equal to the mean inter-spike interval.


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

Lynch EP, Houghton CJ - Front Neuroinform (2015)

An illustration of the effect of filter width on van Rossum metric; (A) Shows two spike trains, one in black, the other in blue, (B) Shows the functions obtained by filtering the spike trains with a causal exponential with a time constant of τ = 10 ms, and (C) Shows the same trains filtered with time constant of 50 ms, 5 × τ.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: An illustration of the effect of filter width on van Rossum metric; (A) Shows two spike trains, one in black, the other in blue, (B) Shows the functions obtained by filtering the spike trains with a causal exponential with a time constant of τ = 10 ms, and (C) Shows the same trains filtered with time constant of 50 ms, 5 × τ.
Mentions: Often, some sort of metric clustering based optimization routine is used to pick the best timescale τ for a data set (Victor and Purpura, 1997). Here, however, different timescales are used to explore different points in the spike trains in our genetic algorithm. Different values of τ make the metric sensitive to features on different timescales in the spike trains. A long timescale allows similarities in large scale features to be detected, for example, the mean firing rate, while a short timescale will be more sensitive to the differences between very similar spike trains. This is illustrated by the example in Figure 2. Figure 2A shows two spike trains. In Figure 2B the spike trains have been filtered to form functions with a short timescale. The functions formed by filtering the spike trains only significantly overlap when spikes from each train are close to each other. Figure 2C demonstrates that for a long timescale, very different spike trains can produce relatively similar functions. To demonstrate the effect of this on model optimization, we will later present the result of using a variable timescale which starts at a value of the order of the length of the target data set and is then reduced to a value roughly equal to the mean inter-spike interval.

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.