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.


Related in: MedlinePlus

Testing GIF model parameter extraction on in silico recordings from a detailed biophysical model: GIF model validation.(A) Fraction of the input current Itest(t) (top, gray) used for model validation; typical DBM response evoked by a single current injection (middle, black); DBM spiking activity in response to nine repetitive injections of the same input (bottom, black raster); PSTH constructed by averaging the nine spike trains smoothed with a rectangular window of 500 ms (bottom, black line). GIF model and GLM predictions are shown in red and blue, respectively. Dashed black lines represent 0 nA (top) and 0 Hz (bottom). (B)-(D) Performance comparison between GIF model (red) and GLM (blue) in predicting the DBM activity. Parameter extraction and model validation were repeated five times using different datasets. Each couple of open circles indicates the performance obtained by both models on a specific dataset. Bar plots indicate the mean and one standard deviation across repetitions. (B) Spike-timing prediction as quantified by  with precision Δ = 4 ms. (C) Mean prediction error ϵV on subthreshold membrane potential fluctuations. The GLM does not explicitly model the subthreshold membrane potential dynamics and is therefore not applicable (N/A). (D) GIF model spike-timing prediction (, with precision Δ = 4 ms) as a function of the training set size used for parameter extraction. Increasing the duration of the training set from 100 s to 120 s does not improve the GIF model predictive power ( = 0.80, s.d. 0.01, Ttr = 100 s;  = 0.80, s.d. 0.01, Ttr = 120 s; n = 10, paired Student t-test, t4 = 0.05, p = 0.97; n.s. > 0.05).
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004275.g006: Testing GIF model parameter extraction on in silico recordings from a detailed biophysical model: GIF model validation.(A) Fraction of the input current Itest(t) (top, gray) used for model validation; typical DBM response evoked by a single current injection (middle, black); DBM spiking activity in response to nine repetitive injections of the same input (bottom, black raster); PSTH constructed by averaging the nine spike trains smoothed with a rectangular window of 500 ms (bottom, black line). GIF model and GLM predictions are shown in red and blue, respectively. Dashed black lines represent 0 nA (top) and 0 Hz (bottom). (B)-(D) Performance comparison between GIF model (red) and GLM (blue) in predicting the DBM activity. Parameter extraction and model validation were repeated five times using different datasets. Each couple of open circles indicates the performance obtained by both models on a specific dataset. Bar plots indicate the mean and one standard deviation across repetitions. (B) Spike-timing prediction as quantified by with precision Δ = 4 ms. (C) Mean prediction error ϵV on subthreshold membrane potential fluctuations. The GLM does not explicitly model the subthreshold membrane potential dynamics and is therefore not applicable (N/A). (D) GIF model spike-timing prediction (, with precision Δ = 4 ms) as a function of the training set size used for parameter extraction. Increasing the duration of the training set from 100 s to 120 s does not improve the GIF model predictive power ( = 0.80, s.d. 0.01, Ttr = 100 s; = 0.80, s.d. 0.01, Ttr = 120 s; n = 10, paired Student t-test, t4 = 0.05, p = 0.97; n.s. > 0.05).

Mentions: The predictive power of both the GIF model and the GLM was then assessed on a test set obtained by simulating the DBM response to nine repetitive injections of a new 10-second current (Fig 6A). Both models achieved a similar performance and were able to predict around 80% of the spikes emitted by the DBM (temporal precision Δ = 4 ms; = 0.80, s.d. 0.01, GIF; = 0.79, s.d. 0.01, GLM; Fig 6B). Compared to the GLM, the GIF model presented two advantages. First, the GIF model, but not the GLM, explicitly modeled the dynamics of the membrane potential and could therefore predict the DBM subthreshold voltage with an average root mean squared error (RMSE) of 3.4 mV, s.d. 0.03 mV (variance explained ϵV = 74.3 %, s.d. 1.1%; Fig 6C). Second, the time required to perform parameter extraction was faster for the GIF model than for the GLM (TCPU = 86 s, GIF; TCPU = 143 s, GLM).


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)

Testing GIF model parameter extraction on in silico recordings from a detailed biophysical model: GIF model validation.(A) Fraction of the input current Itest(t) (top, gray) used for model validation; typical DBM response evoked by a single current injection (middle, black); DBM spiking activity in response to nine repetitive injections of the same input (bottom, black raster); PSTH constructed by averaging the nine spike trains smoothed with a rectangular window of 500 ms (bottom, black line). GIF model and GLM predictions are shown in red and blue, respectively. Dashed black lines represent 0 nA (top) and 0 Hz (bottom). (B)-(D) Performance comparison between GIF model (red) and GLM (blue) in predicting the DBM activity. Parameter extraction and model validation were repeated five times using different datasets. Each couple of open circles indicates the performance obtained by both models on a specific dataset. Bar plots indicate the mean and one standard deviation across repetitions. (B) Spike-timing prediction as quantified by  with precision Δ = 4 ms. (C) Mean prediction error ϵV on subthreshold membrane potential fluctuations. The GLM does not explicitly model the subthreshold membrane potential dynamics and is therefore not applicable (N/A). (D) GIF model spike-timing prediction (, with precision Δ = 4 ms) as a function of the training set size used for parameter extraction. Increasing the duration of the training set from 100 s to 120 s does not improve the GIF model predictive power ( = 0.80, s.d. 0.01, Ttr = 100 s;  = 0.80, s.d. 0.01, Ttr = 120 s; n = 10, paired Student t-test, t4 = 0.05, p = 0.97; n.s. > 0.05).
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004275.g006: Testing GIF model parameter extraction on in silico recordings from a detailed biophysical model: GIF model validation.(A) Fraction of the input current Itest(t) (top, gray) used for model validation; typical DBM response evoked by a single current injection (middle, black); DBM spiking activity in response to nine repetitive injections of the same input (bottom, black raster); PSTH constructed by averaging the nine spike trains smoothed with a rectangular window of 500 ms (bottom, black line). GIF model and GLM predictions are shown in red and blue, respectively. Dashed black lines represent 0 nA (top) and 0 Hz (bottom). (B)-(D) Performance comparison between GIF model (red) and GLM (blue) in predicting the DBM activity. Parameter extraction and model validation were repeated five times using different datasets. Each couple of open circles indicates the performance obtained by both models on a specific dataset. Bar plots indicate the mean and one standard deviation across repetitions. (B) Spike-timing prediction as quantified by with precision Δ = 4 ms. (C) Mean prediction error ϵV on subthreshold membrane potential fluctuations. The GLM does not explicitly model the subthreshold membrane potential dynamics and is therefore not applicable (N/A). (D) GIF model spike-timing prediction (, with precision Δ = 4 ms) as a function of the training set size used for parameter extraction. Increasing the duration of the training set from 100 s to 120 s does not improve the GIF model predictive power ( = 0.80, s.d. 0.01, Ttr = 100 s; = 0.80, s.d. 0.01, Ttr = 120 s; n = 10, paired Student t-test, t4 = 0.05, p = 0.97; n.s. > 0.05).
Mentions: The predictive power of both the GIF model and the GLM was then assessed on a test set obtained by simulating the DBM response to nine repetitive injections of a new 10-second current (Fig 6A). Both models achieved a similar performance and were able to predict around 80% of the spikes emitted by the DBM (temporal precision Δ = 4 ms; = 0.80, s.d. 0.01, GIF; = 0.79, s.d. 0.01, GLM; Fig 6B). Compared to the GLM, the GIF model presented two advantages. First, the GIF model, but not the GLM, explicitly modeled the dynamics of the membrane potential and could therefore predict the DBM subthreshold voltage with an average root mean squared error (RMSE) of 3.4 mV, s.d. 0.03 mV (variance explained ϵV = 74.3 %, s.d. 1.1%; Fig 6C). Second, the time required to perform parameter extraction was faster for the GIF model than for the GLM (TCPU = 86 s, GIF; TCPU = 143 s, GLM).

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