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Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models.

Pozzorini C, Mensi S, Hagens O, Naud R, Koch C, Gerstner W - PLoS Comput. Biol. (2015)

Bottom Line: Here we demonstrate that, using a convex optimization procedure we previously introduced, a Generalized Integrate-and-Fire model can be accurately fitted with a limited amount of data.The model is capable of predicting both the spiking activity and the subthreshold dynamics of different cell types, and can be used for online characterization of neuronal properties.A protocol is proposed that, combined with emergent technologies for automatic patch-clamp recordings, permits automated, in vitro high-throughput characterization of single neurons.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Computational Neuroscience (LCN), Brain Mind Institute, School of Computer and Communication Sciences and School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.

ABSTRACT
Single-neuron models are useful not only for studying the emergent properties of neural circuits in large-scale simulations, but also for extracting and summarizing in a principled way the information contained in electrophysiological recordings. Here we demonstrate that, using a convex optimization procedure we previously introduced, a Generalized Integrate-and-Fire model can be accurately fitted with a limited amount of data. The model is capable of predicting both the spiking activity and the subthreshold dynamics of different cell types, and can be used for online characterization of neuronal properties. A protocol is proposed that, combined with emergent technologies for automatic patch-clamp recordings, permits automated, in vitro high-throughput characterization of single neurons.

No MeSH data available.


Related in: MedlinePlus

Schematic representation of the procedure used for GIF model parameter extraction.In Step 1 (first row), the experimental spike train Sdata(t) is extracted from the voltage trace Vdata(t) using a standard threshold-crossing method (left, dashed line). Parameters related to absolute refractoriness are extracted from the average spike shape (middle). In Step 2 (second row), given the injected current Itr(t) and the recorded potential Vdata, all the parameters θsub defining the dynamics of the subthreshold membrane potential (Eq 1) are extracted by performing a least-square multilinear regression on the membrane potential derivative . Since Eq 1 does not describe the membrane potential dynamics during action potentials, all the data close to spikes are discarded. In Step 3 (third row), the subthreshold parameters θsub are first used to compute the subthreshold voltage of the model . The parameters θth defining the dynamics of the firing threshold (left, dashed line) are then extracted by maximizing the probability (i.e., the log-likelihood) that the experimental spike train Sdata(t) was produced by the model, given the subthreshold dynamics .
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pcbi.1004275.g002: Schematic representation of the procedure used for GIF model parameter extraction.In Step 1 (first row), the experimental spike train Sdata(t) is extracted from the voltage trace Vdata(t) using a standard threshold-crossing method (left, dashed line). Parameters related to absolute refractoriness are extracted from the average spike shape (middle). In Step 2 (second row), given the injected current Itr(t) and the recorded potential Vdata, all the parameters θsub defining the dynamics of the subthreshold membrane potential (Eq 1) are extracted by performing a least-square multilinear regression on the membrane potential derivative . Since Eq 1 does not describe the membrane potential dynamics during action potentials, all the data close to spikes are discarded. In Step 3 (third row), the subthreshold parameters θsub are first used to compute the subthreshold voltage of the model . The parameters θth defining the dynamics of the firing threshold (left, dashed line) are then extracted by maximizing the probability (i.e., the log-likelihood) that the experimental spike train Sdata(t) was produced by the model, given the subthreshold dynamics .

Mentions: Given the intracellular voltage response Vdata(t) evoked in vitro by a controlled input current Itr(t), all of the GIF model parameters are extracted from experimental data (training set) using a three-step procedure (Fig 2) that we previously introduced [30, 31]. A detailed description of the fitting procedure can be found in the Materials and Methods section.


Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models.

Pozzorini C, Mensi S, Hagens O, Naud R, Koch C, Gerstner W - PLoS Comput. Biol. (2015)

Schematic representation of the procedure used for GIF model parameter extraction.In Step 1 (first row), the experimental spike train Sdata(t) is extracted from the voltage trace Vdata(t) using a standard threshold-crossing method (left, dashed line). Parameters related to absolute refractoriness are extracted from the average spike shape (middle). In Step 2 (second row), given the injected current Itr(t) and the recorded potential Vdata, all the parameters θsub defining the dynamics of the subthreshold membrane potential (Eq 1) are extracted by performing a least-square multilinear regression on the membrane potential derivative . Since Eq 1 does not describe the membrane potential dynamics during action potentials, all the data close to spikes are discarded. In Step 3 (third row), the subthreshold parameters θsub are first used to compute the subthreshold voltage of the model . The parameters θth defining the dynamics of the firing threshold (left, dashed line) are then extracted by maximizing the probability (i.e., the log-likelihood) that the experimental spike train Sdata(t) was produced by the model, given the subthreshold dynamics .
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4470831&req=5

pcbi.1004275.g002: Schematic representation of the procedure used for GIF model parameter extraction.In Step 1 (first row), the experimental spike train Sdata(t) is extracted from the voltage trace Vdata(t) using a standard threshold-crossing method (left, dashed line). Parameters related to absolute refractoriness are extracted from the average spike shape (middle). In Step 2 (second row), given the injected current Itr(t) and the recorded potential Vdata, all the parameters θsub defining the dynamics of the subthreshold membrane potential (Eq 1) are extracted by performing a least-square multilinear regression on the membrane potential derivative . Since Eq 1 does not describe the membrane potential dynamics during action potentials, all the data close to spikes are discarded. In Step 3 (third row), the subthreshold parameters θsub are first used to compute the subthreshold voltage of the model . The parameters θth defining the dynamics of the firing threshold (left, dashed line) are then extracted by maximizing the probability (i.e., the log-likelihood) that the experimental spike train Sdata(t) was produced by the model, given the subthreshold dynamics .
Mentions: Given the intracellular voltage response Vdata(t) evoked in vitro by a controlled input current Itr(t), all of the GIF model parameters are extracted from experimental data (training set) using a three-step procedure (Fig 2) that we previously introduced [30, 31]. A detailed description of the fitting procedure can be found in the Materials and Methods section.

Bottom Line: Here we demonstrate that, using a convex optimization procedure we previously introduced, a Generalized Integrate-and-Fire model can be accurately fitted with a limited amount of data.The model is capable of predicting both the spiking activity and the subthreshold dynamics of different cell types, and can be used for online characterization of neuronal properties.A protocol is proposed that, combined with emergent technologies for automatic patch-clamp recordings, permits automated, in vitro high-throughput characterization of single neurons.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Computational Neuroscience (LCN), Brain Mind Institute, School of Computer and Communication Sciences and School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.

ABSTRACT
Single-neuron models are useful not only for studying the emergent properties of neural circuits in large-scale simulations, but also for extracting and summarizing in a principled way the information contained in electrophysiological recordings. Here we demonstrate that, using a convex optimization procedure we previously introduced, a Generalized Integrate-and-Fire model can be accurately fitted with a limited amount of data. The model is capable of predicting both the spiking activity and the subthreshold dynamics of different cell types, and can be used for online characterization of neuronal properties. A protocol is proposed that, combined with emergent technologies for automatic patch-clamp recordings, permits automated, in vitro high-throughput characterization of single neurons.

No MeSH data available.


Related in: MedlinePlus