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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.


Data preprocessing: Active Electrode Compensation.(A) Schematic representation of the Active Electrode Compensation technique used to correct for the experimental bias known to occur when the same patch-clamp electrode is used to simultaneously inject and record from a single neuron. The artifactual voltage Ve(t) across the pipette is estimated by filtering the input current I(t) with the electrode filter κe(t). The intracellular membrane potential Vdata(t) if finally obtained by subtracting the artifactual voltage Ve(t) from the recorded signal Vrec(t). (B) Typical optimal linear filter κopt(t) between the subthreshold input current Isub(t) and the recorded signal Vsub(t). To estimate the electrode filter, an exponential fit is performed on the tail of κopt(t) (dashed black). Inset: Magnification of the y-axis illustrating the good accuracy of the exponential fit (dashed black) on the tail of the optimal linear filter κopt(t) (red). (C) Typical electrode filter κe(t) obtained by subtracting the exponential fit from the optimal linear filter κopt(t) (see panel B). Since in vitro recordings were performed with the standard bridge compensation technique, the electrode filter κe(t) is characterized by a strong initial negative peak. The characteristic timescale of the electrode filter τe was measured by performing an exponential fit (dashed black) on κe(t). Inset: distribution of the electrode timescales τe measured in ten different recordings included in this study. (D) Comparison between recorded signal Vrec(t) (black) and membrane potential Vdata(t) (red) obtained after AEC. Inset: zoom indicating that AEC operates as a low-pass filter by removing high-frequency components from the acquired signal. Scale bars: 30 ms, 5 mV.
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pcbi.1004275.g007: Data preprocessing: Active Electrode Compensation.(A) Schematic representation of the Active Electrode Compensation technique used to correct for the experimental bias known to occur when the same patch-clamp electrode is used to simultaneously inject and record from a single neuron. The artifactual voltage Ve(t) across the pipette is estimated by filtering the input current I(t) with the electrode filter κe(t). The intracellular membrane potential Vdata(t) if finally obtained by subtracting the artifactual voltage Ve(t) from the recorded signal Vrec(t). (B) Typical optimal linear filter κopt(t) between the subthreshold input current Isub(t) and the recorded signal Vsub(t). To estimate the electrode filter, an exponential fit is performed on the tail of κopt(t) (dashed black). Inset: Magnification of the y-axis illustrating the good accuracy of the exponential fit (dashed black) on the tail of the optimal linear filter κopt(t) (red). (C) Typical electrode filter κe(t) obtained by subtracting the exponential fit from the optimal linear filter κopt(t) (see panel B). Since in vitro recordings were performed with the standard bridge compensation technique, the electrode filter κe(t) is characterized by a strong initial negative peak. The characteristic timescale of the electrode filter τe was measured by performing an exponential fit (dashed black) on κe(t). Inset: distribution of the electrode timescales τe measured in ten different recordings included in this study. (D) Comparison between recorded signal Vrec(t) (black) and membrane potential Vdata(t) (red) obtained after AEC. Inset: zoom indicating that AEC operates as a low-pass filter by removing high-frequency components from the acquired signal. Scale bars: 30 ms, 5 mV.

Mentions: Since the same patch-clamp electrode was used to simultaneously stimulate and record from single neurons, the acquired signal Vrec(t) is a biased version of the real membrane potential Vdata(t) [32, 33]. This bias is due to the voltage drop Ve(t) across the patch-clamp electrode and was removed using a technique called Active Electrode Compensation (AEC, see Materials and Methods and Fig 7A). In AEC [32, 33], the electrode is modeled as an arbitrarily complex linear filter κe(t) estimated at the beginning of the experiment from the optimal linear filter κopt(t) between a 10-second subthreshold current Isub(t) and the recorded response Vsub(t) (Fig 7B). For all subsequent injections, we estimated the voltage drop across the electrode Ve(t) by convolving the input current with the electrode filter κe(t) (Fig 7C). We finally recovered the membrane potential Vdata(t) by subtracting Ve(t) from the recorded signal Vrec(t) (Fig 7A and 7D):Vdata(t)=Vrec(t)-Ve(t).(8)


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)

Data preprocessing: Active Electrode Compensation.(A) Schematic representation of the Active Electrode Compensation technique used to correct for the experimental bias known to occur when the same patch-clamp electrode is used to simultaneously inject and record from a single neuron. The artifactual voltage Ve(t) across the pipette is estimated by filtering the input current I(t) with the electrode filter κe(t). The intracellular membrane potential Vdata(t) if finally obtained by subtracting the artifactual voltage Ve(t) from the recorded signal Vrec(t). (B) Typical optimal linear filter κopt(t) between the subthreshold input current Isub(t) and the recorded signal Vsub(t). To estimate the electrode filter, an exponential fit is performed on the tail of κopt(t) (dashed black). Inset: Magnification of the y-axis illustrating the good accuracy of the exponential fit (dashed black) on the tail of the optimal linear filter κopt(t) (red). (C) Typical electrode filter κe(t) obtained by subtracting the exponential fit from the optimal linear filter κopt(t) (see panel B). Since in vitro recordings were performed with the standard bridge compensation technique, the electrode filter κe(t) is characterized by a strong initial negative peak. The characteristic timescale of the electrode filter τe was measured by performing an exponential fit (dashed black) on κe(t). Inset: distribution of the electrode timescales τe measured in ten different recordings included in this study. (D) Comparison between recorded signal Vrec(t) (black) and membrane potential Vdata(t) (red) obtained after AEC. Inset: zoom indicating that AEC operates as a low-pass filter by removing high-frequency components from the acquired signal. Scale bars: 30 ms, 5 mV.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004275.g007: Data preprocessing: Active Electrode Compensation.(A) Schematic representation of the Active Electrode Compensation technique used to correct for the experimental bias known to occur when the same patch-clamp electrode is used to simultaneously inject and record from a single neuron. The artifactual voltage Ve(t) across the pipette is estimated by filtering the input current I(t) with the electrode filter κe(t). The intracellular membrane potential Vdata(t) if finally obtained by subtracting the artifactual voltage Ve(t) from the recorded signal Vrec(t). (B) Typical optimal linear filter κopt(t) between the subthreshold input current Isub(t) and the recorded signal Vsub(t). To estimate the electrode filter, an exponential fit is performed on the tail of κopt(t) (dashed black). Inset: Magnification of the y-axis illustrating the good accuracy of the exponential fit (dashed black) on the tail of the optimal linear filter κopt(t) (red). (C) Typical electrode filter κe(t) obtained by subtracting the exponential fit from the optimal linear filter κopt(t) (see panel B). Since in vitro recordings were performed with the standard bridge compensation technique, the electrode filter κe(t) is characterized by a strong initial negative peak. The characteristic timescale of the electrode filter τe was measured by performing an exponential fit (dashed black) on κe(t). Inset: distribution of the electrode timescales τe measured in ten different recordings included in this study. (D) Comparison between recorded signal Vrec(t) (black) and membrane potential Vdata(t) (red) obtained after AEC. Inset: zoom indicating that AEC operates as a low-pass filter by removing high-frequency components from the acquired signal. Scale bars: 30 ms, 5 mV.
Mentions: Since the same patch-clamp electrode was used to simultaneously stimulate and record from single neurons, the acquired signal Vrec(t) is a biased version of the real membrane potential Vdata(t) [32, 33]. This bias is due to the voltage drop Ve(t) across the patch-clamp electrode and was removed using a technique called Active Electrode Compensation (AEC, see Materials and Methods and Fig 7A). In AEC [32, 33], the electrode is modeled as an arbitrarily complex linear filter κe(t) estimated at the beginning of the experiment from the optimal linear filter κopt(t) between a 10-second subthreshold current Isub(t) and the recorded response Vsub(t) (Fig 7B). For all subsequent injections, we estimated the voltage drop across the electrode Ve(t) by convolving the input current with the electrode filter κe(t) (Fig 7C). We finally recovered the membrane potential Vdata(t) by subtracting Ve(t) from the recorded signal Vrec(t) (Fig 7A and 7D):Vdata(t)=Vrec(t)-Ve(t).(8)

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.