Automated optimization of a reduced layer 5 pyramidal cell model based on experimental data

Bahl, Armin; Stemmler, Martin B.; Herz, Andreas V. M.; Roth, Arnd

doi:10.1016/j.jneumeth.2012.04.006

Cited by 80 publications

(128 citation statements)

References 64 publications

(103 reference statements)

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“…For example, the model considered here sustains firing only over a limited range of input intensities, and a more accurate neuron with more voltage-activated conductances ( e.g . the I h conductance or even a large set of spatially-distributed conductances [95, 96]) might lead to a neuron that has a greater dynamic range. Another limitation of the current study is a lack of inhibition.…”

Section: Discussionmentioning

confidence: 99%

A consensus layer V pyramidal neuron can sustain interpulse-interval coding

Singh

Levy

2017

PLoS ONE

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In terms of a single neuron’s long-distance communication, interpulse intervals (IPIs) are an attractive alternative to rate and binary codes. As a proxy for an IPI, a neuron’s time-to-spike can be found in the biophysical and experimental intracellular literature. Using the current, consensus layer V pyramidal neuron, the present study examines the feasibility of IPI-coding and examines the noise sources that limit the information rate of such an encoding. In descending order of importance, the noise sources are (i) synaptic variability, (ii) sodium channel shot-noise, followed by (iii) thermal noise. The biophysical simulations allow the calculation of mutual information, which is about 3.0 bits/spike. More importantly, while, by any conventional definition, the biophysical model is highly nonlinear, the underlying function that relates input intensity to the defined output variable is linear. When one assumes the perspective of a neuron coding via first hitting-time, this result justifies a pervasive and simplifying assumption of computational modelers—that a class of cortical neurons can be treated as linearly additive, computational devices.

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Section: Discussionmentioning

confidence: 99%

A consensus layer V pyramidal neuron can sustain interpulse-interval coding

Singh

Levy

2017

PLoS ONE

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“…2B) over ranges, due possibly to several factors including state dependent neuromodulation (e.g., Marder et al 2015). Possibly for these reasons, systematic methods for the development of biophysical single cell models do not seem to be reported in the literature, with present methods ranging from hand tuning to several types of automated search algorithms (Bhalla and Bower, 1993, Prinz et al, 2003, Druckmann et al, 2007, Hemond et al, 2008, Pospischil et al, 2008, Marder and Taylor, 2011, Bahl et al, 2012, Forren et al, 2012). …”

Section: Discussionmentioning

confidence: 99%

Distinct current modules shape cellular dynamics in model neurons

et al. 2016

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Numerous intrinsic currents are known to collectively shape neuronal membrane potential dynamics, or neuronal signatures. Although how sets of currents shape specific signatures such as spiking characteristics or oscillations has been studied individually, it is less clear how a neuron’s suite of currents jointly shape its entire set of signatures. Biophysical conductance based models of neurons represent a viable tool to address this important question. We hypothesized that currents are grouped into distinct modules that shape specific neuronal characteristics or signatures, such as resting potential, sub-threshold oscillations, and spiking waveforms, for several classes of neurons. For such a grouping to occur, the currents within one module should have minimal functional interference with currents belonging to other modules. This condition is satisfied if the gating functions of currents in the same module are grouped together on the voltage axis; in contrast, such functions are segregated along the voltage axis for currents belonging to different modules. We tested this hypothesis using four published example case models and found it to be valid for these classes of neurons. This insight into the neurobiological organization of currents also suggests an intuitive, systematic, and robust methodology to develop biophysical single cell models with multiple biological characteristics applicable for both hand- and automated- tuning approaches. We illustrate the methodology using two example case rodent pyramidal neurons, from the lateral amygdala and the hippocampus. The methodology also helped reveal that a single core compartment model could capture multiple neuronal properties. Such biophysical single compartment models have potential to improve the fidelity of large network models.

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“…Still, our method could be expanded to explore the morphological effect on active dendritic properties (Torben-Nielsen and Stiefel 2010;Vetter et al 2001), as well as on the interplay ("ping pong") between dendritic and axosomatic firing (Hay et al 2011;Larkum et al 1999;Schaefer et al 2003). The method and insights we provide will also be useful in producing models with reduced morphologies (Bahl et al 2012).…”

Section: Model Caveats and Future Perspectivesmentioning

confidence: 98%

Preserving axosomatic spiking features despite diverse dendritic morphology

Hay

Schürmann

Markram

et al. 2013

Journal of Neurophysiology

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Hay E, Schürmann F, Markram H, Segev I. Preserving axosomatic spiking features despite diverse dendritic morphology. J Neurophysiol 109: 2972-2981, 2013. First published March 27, 2013 doi:10.1152/jn.00048.2013.-Throughout the nervous system, cells belonging to a certain electrical class (e-class)-sharing high similarity in firing response properties-may nevertheless have widely variable dendritic morphologies. To quantify the effect of this morphological variability on the firing of layer 5 thick-tufted pyramidal cells (TTCs), a detailed conductance-based model was constructed for a threedimensional reconstructed exemplar TTC. The model exhibited spike initiation in the axon and reproduced the characteristic features of individual spikes, as well as of the firing properties at the soma, as recorded in a population of TTCs in young Wistar rats. When using these model parameters over the population of 28 three-dimensional reconstructed TTCs, both axonal and somatic ion channel densities had to be scaled linearly with the conductance load imposed on each of these compartments. Otherwise, the firing of model cells deviated, sometimes very significantly, from the experimental variability of the TTC e-class. The study provides experimentally testable predictions regarding the coregulation of axosomatic membrane ion channels density for cells with different dendritic conductance load, together with a simple and systematic method for generating reliable conductance-based models for the whole population of modeled neurons belonging to a particular e-class, with variable morphology as found experimentally.

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Automated optimization of a reduced layer 5 pyramidal cell model based on experimental data

Cited by 80 publications

References 64 publications

A consensus layer V pyramidal neuron can sustain interpulse-interval coding

A consensus layer V pyramidal neuron can sustain interpulse-interval coding

Distinct current modules shape cellular dynamics in model neurons

Preserving axosomatic spiking features despite diverse dendritic morphology

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