Limits...
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.


Schematic representation of the protocol for high-throughput single-neuron characterization.To characterize the properties of the electrode required for AEC, the experimental protocol starts with the injection of a short subthreshold current. While the filtering properties of the patch clamp are estimated (AEC box, left), the training dataset is collected. After training set collection, the raw data are preprocessed with AEC (AEC box, right). Then, in parallel with GIF model parameter extraction and successive spike timing prediction, the test dataset is collected by injecting nine repetitions of the same time-dependent current. Finally, after complete acquisition of the test set, the similarity measure  between the observed and the predicted spike trains is computed. Overall, GIF model parameter extraction and validation requires around five minutes.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4470831&req=5

pcbi.1004275.g004: Schematic representation of the protocol for high-throughput single-neuron characterization.To characterize the properties of the electrode required for AEC, the experimental protocol starts with the injection of a short subthreshold current. While the filtering properties of the patch clamp are estimated (AEC box, left), the training dataset is collected. After training set collection, the raw data are preprocessed with AEC (AEC box, right). Then, in parallel with GIF model parameter extraction and successive spike timing prediction, the test dataset is collected by injecting nine repetitions of the same time-dependent current. Finally, after complete acquisition of the test set, the similarity measure between the observed and the predicted spike trains is computed. Overall, GIF model parameter extraction and validation requires around five minutes.

Mentions: Based on the results reported in the previous section, we designed a protocol for the fit and the validation of GIF models on in vitro intracellular recordings (Fig 4). The protocol is conceptually divided in two phases. In the first part, a training set is acquired by recording the single-neuron response to a fluctuating input Itr(t) lasting for Ttr = 100 seconds and generated according to Eqs 5–6. These data are then used for parameter extraction. In the second part of the protocol, nine repetitive injections of a new 10-second current Itest(t) are performed with an interstimulus interval of 10 seconds, so as to allow the cell to recover. These data (test set) are then used to quantify the predictive power of the GIF model with the spike-train similarity measure . Since all the computations required for parameter extraction and model validation can be performed on the fly, the whole protocol requires 5 minutes and is suitable for high-throughput.


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 protocol for high-throughput single-neuron characterization.To characterize the properties of the electrode required for AEC, the experimental protocol starts with the injection of a short subthreshold current. While the filtering properties of the patch clamp are estimated (AEC box, left), the training dataset is collected. After training set collection, the raw data are preprocessed with AEC (AEC box, right). Then, in parallel with GIF model parameter extraction and successive spike timing prediction, the test dataset is collected by injecting nine repetitions of the same time-dependent current. Finally, after complete acquisition of the test set, the similarity measure  between the observed and the predicted spike trains is computed. Overall, GIF model parameter extraction and validation requires around five minutes.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004275.g004: Schematic representation of the protocol for high-throughput single-neuron characterization.To characterize the properties of the electrode required for AEC, the experimental protocol starts with the injection of a short subthreshold current. While the filtering properties of the patch clamp are estimated (AEC box, left), the training dataset is collected. After training set collection, the raw data are preprocessed with AEC (AEC box, right). Then, in parallel with GIF model parameter extraction and successive spike timing prediction, the test dataset is collected by injecting nine repetitions of the same time-dependent current. Finally, after complete acquisition of the test set, the similarity measure between the observed and the predicted spike trains is computed. Overall, GIF model parameter extraction and validation requires around five minutes.
Mentions: Based on the results reported in the previous section, we designed a protocol for the fit and the validation of GIF models on in vitro intracellular recordings (Fig 4). The protocol is conceptually divided in two phases. In the first part, a training set is acquired by recording the single-neuron response to a fluctuating input Itr(t) lasting for Ttr = 100 seconds and generated according to Eqs 5–6. These data are then used for parameter extraction. In the second part of the protocol, nine repetitive injections of a new 10-second current Itest(t) are performed with an interstimulus interval of 10 seconds, so as to allow the cell to recover. These data (test set) are then used to quantify the predictive power of the GIF model with the spike-train similarity measure . Since all the computations required for parameter extraction and model validation can be performed on the fly, the whole protocol requires 5 minutes and is suitable for high-throughput.

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.