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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.


The GIF model accurately predicts both the subthreshold and the spiking activity of cortical neurons.(A) Block representation of the GIF model. The membrane acts as a low-pass filter κ(t) on the input current I(t) to produce the modeled potential V(t). The exponential nonlinearity (escape-rate) transforms this voltage into an instantaneous firing intensity λ(t), according to which spikes are generated. Each time a spike is emitted, both a current η(t) and a movement of the firing threshold γ(t) are triggered. (B) The GIF model accurately predicts the occurrence of individual spikes with millisecond precision. To evaluate the predictive power of the GIF model, the response of a L5 pyramidal neuron to a fluctuating input current (top, the horizontal dashed line represents 0 nA) has been recorded intracellularly (middle, black). The same protocol was repeated nine times to assess the reliability of the neural response (bottom, black raster). The GIF model (with parameters extracted using a different dataset) was able to accurately predict both the subthreshold (middle, red) and the spiking response (bottom, red raster) of the cell.
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pcbi.1004275.g001: The GIF model accurately predicts both the subthreshold and the spiking activity of cortical neurons.(A) Block representation of the GIF model. The membrane acts as a low-pass filter κ(t) on the input current I(t) to produce the modeled potential V(t). The exponential nonlinearity (escape-rate) transforms this voltage into an instantaneous firing intensity λ(t), according to which spikes are generated. Each time a spike is emitted, both a current η(t) and a movement of the firing threshold γ(t) are triggered. (B) The GIF model accurately predicts the occurrence of individual spikes with millisecond precision. To evaluate the predictive power of the GIF model, the response of a L5 pyramidal neuron to a fluctuating input current (top, the horizontal dashed line represents 0 nA) has been recorded intracellularly (middle, black). The same protocol was repeated nine times to assess the reliability of the neural response (bottom, black raster). The GIF model (with parameters extracted using a different dataset) was able to accurately predict both the subthreshold (middle, red) and the spiking response (bottom, red raster) of the cell.

Mentions: The GIF model discussed in this study [31, 37] is a leaky integrate-and-fire model augmented with a spike-triggered current η(t), a moving threshold γ(t) and the escape rate mechanism [38, 39] for stochastic spike emission (Fig 1A). This model is able to predict both the spiking activity and the subthreshold dynamics of individual neurons (Fig 1B), and it is flexible enough to capture the behavior of different neuronal cell types [37].


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)

The GIF model accurately predicts both the subthreshold and the spiking activity of cortical neurons.(A) Block representation of the GIF model. The membrane acts as a low-pass filter κ(t) on the input current I(t) to produce the modeled potential V(t). The exponential nonlinearity (escape-rate) transforms this voltage into an instantaneous firing intensity λ(t), according to which spikes are generated. Each time a spike is emitted, both a current η(t) and a movement of the firing threshold γ(t) are triggered. (B) The GIF model accurately predicts the occurrence of individual spikes with millisecond precision. To evaluate the predictive power of the GIF model, the response of a L5 pyramidal neuron to a fluctuating input current (top, the horizontal dashed line represents 0 nA) has been recorded intracellularly (middle, black). The same protocol was repeated nine times to assess the reliability of the neural response (bottom, black raster). The GIF model (with parameters extracted using a different dataset) was able to accurately predict both the subthreshold (middle, red) and the spiking response (bottom, red raster) of the cell.
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getmorefigures.php?uid=PMC4470831&req=5

pcbi.1004275.g001: The GIF model accurately predicts both the subthreshold and the spiking activity of cortical neurons.(A) Block representation of the GIF model. The membrane acts as a low-pass filter κ(t) on the input current I(t) to produce the modeled potential V(t). The exponential nonlinearity (escape-rate) transforms this voltage into an instantaneous firing intensity λ(t), according to which spikes are generated. Each time a spike is emitted, both a current η(t) and a movement of the firing threshold γ(t) are triggered. (B) The GIF model accurately predicts the occurrence of individual spikes with millisecond precision. To evaluate the predictive power of the GIF model, the response of a L5 pyramidal neuron to a fluctuating input current (top, the horizontal dashed line represents 0 nA) has been recorded intracellularly (middle, black). The same protocol was repeated nine times to assess the reliability of the neural response (bottom, black raster). The GIF model (with parameters extracted using a different dataset) was able to accurately predict both the subthreshold (middle, red) and the spiking response (bottom, red raster) of the cell.
Mentions: The GIF model discussed in this study [31, 37] is a leaky integrate-and-fire model augmented with a spike-triggered current η(t), a moving threshold γ(t) and the escape rate mechanism [38, 39] for stochastic spike emission (Fig 1A). This model is able to predict both the spiking activity and the subthreshold dynamics of individual neurons (Fig 1B), and it is flexible enough to capture the behavior of different neuronal cell types [37].

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.