Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons.

By Astrid A. Prinz, C. P. Billimoria, Eve Marder. Journal of Neurophysiology, 2003

414 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/7a9dcbeb9c4c1490b53719f25cc9c81c131b377d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Statistical and Nonlinear Physics

Citations in the corpus

Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. (2005, background): “Besides, it has been shown that spike patterns remain unchanged along certain directions of the parameter space of detailed conductance-based models (Goldman et al. 2001; Prinz et al. 2003), which suggests that these models could be simpliﬁed without drastic consequences for the spiking behavior.”
An efficient automated parameter tuning framework for spiking neural networks (2014, methodology): “…of searching the parameter space were used to examine a three-neuron model of a lobster stomatogastric circuit by creating large databases of compartmental neurons with varying membrane conductance values and testing the resulting functional behavior of this circuit (Prinz et al., 2003, 2004).”
Neuronal homeostasis: time for a change? (2011, methodology): “This anticipated later work which has employed substantial computational resources to explore the parameter spaces of cells with a particular intrinsic behaviour and entire networks (Prinz et al. 2003, 2004; Taylor et al. 2006, 2009; Sobie, 2009).”
Flexible statistical inference for mechanistic models of neural dynamics (2017, methodology): “In the absence of likelihoods, the standard approach in neuroscience has been to use heuristic parameter-fitting methods [2, 8, 9]: distance measures are defined on multiple features of interest, and brute-force search [10, 11] or evolutionary algorithms [2, 9, 12, 13] (neither of which scales to hi”
Cell Types, Network Homeostasis, and Pathological Compensation from a Biologically Plausible Ion Channel Expression Model. (2015, positive): “Underlying this problem is the fact that the set of conductance densities that produces a specific kind of activity comprises disparate solutions with a complicated distribution (Prinz et al., 2003; Taylor et al., 2006, 2009).”
Variability, compensation, and modulation in neurons and circuits (2011, background): “…in many cases, to construct families of models rather than single highly tuned models to capture the behavior of individual neurons and circuits (4 – 8, 12, 13, 22, 62, 63), and ( ii ) the importance of trying to measure as many system components as possible within an individual is now evident.”
Complex Parameter Landscape for a Complex Neuron Model (2006, background): “[15] have proposed to use systematic sampling of the parameter space as a way to tune a model’s parameters.”
Cell Types, Network Homeostasis, and Pathological Compensation from a Biologically Plausible Ion Channel Expression Model (2014, positive): “…states of ion channels–can be wildly disparate and nevertheless give rise to highly specific physiological properties that are essential for a functioning nervous system (Bhalla and Bower 1993; Golowasch et al., 2002; Marder and Goaillard 2006; Prinz et al., 2003, 2004; Taylor et al., 2009).”
Homeostatic control of neural activity: from phenomenology to molecular design. (2006, result): “The authors discovered that hundreds of different combinations of ion channel densities could produce a firing pattern nearly identical to that observed in vivo (Goldman et al. 2001, Golowasch et al. 2002, Prinz et al. 2003).”
NEUROMODULATION OF NEURONAL CIRCUITS: BACK TO THE FUTURE (2012, background): “Indeed, random assignment of parameters to a single neuron or a circuit will lead to significantly more failures than successful models (Prinz, 2010; Prinz et al., 2003a, 2004; Taylor et al., 2009).”