@article {5660,
	title = {Effective stimuli for constructing reliable neuron models.},
	journal = {PLoS Computational Biology},
	volume = {7},
	year = {2011},
	month = {2011 Aug},
	pages = {e1002133},
	abstract = {<p>The rich dynamical nature of neurons poses major conceptual and technical challenges for unraveling their nonlinear membrane properties. Traditionally, various current waveforms have been injected at the soma to probe neuron dynamics, but the rationale for selecting specific stimuli has never been rigorously justified. The present experimental and theoretical study proposes a novel framework, inspired by learning theory, for objectively selecting the stimuli that best unravel the neuron{\textquoteright}s dynamics. The efficacy of stimuli is assessed in terms of their ability to constrain the parameter space of biophysically detailed conductance-based models that faithfully replicate the neuron{\textquoteright}s dynamics as attested by their ability to generalize well to the neuron{\textquoteright}s response to novel experimental stimuli. We used this framework to evaluate a variety of stimuli in different types of cortical neurons, ages and animals. Despite their simplicity, a set of stimuli consisting of step and ramp current pulses outperforms synaptic-like noisy stimuli in revealing the dynamics of these neurons. The general framework that we propose paves a new way for defining, evaluating and standardizing effective electrical probing of neurons and will thus lay the foundation for a much deeper understanding of the electrical nature of these highly sophisticated and non-linear devices and of the neuronal networks that they compose.</p>
},
	keywords = {Action Potentials, Algorithms, Animals, Computational Biology, Electric Conductivity, Electrophysiology, Interneurons, Mice, Models, Neurological, Neurons, Patch-Clamp Techniques, Pyramidal Cells, Rats, Wistar},
	issn = {1553-7358},
	doi = {10.1371/journal.pcbi.1002133},
	author = {Druckmann, Shaul and Berger, Thomas K and Sch{\"u}rmann, Felix and Hill, Sean and Markram, Henry and Segev, Idan}
}
@article {5827,
	title = {Evaluating automated parameter constraining procedures of neuron models by experimental and surrogate data.},
	journal = {Biological Cybernetics},
	volume = {99},
	year = {2008},
	month = {2008 Nov},
	pages = {371-9},
	abstract = {<p>Neuron models, in particular conductance-based compartmental models, often have numerous parameters that cannot be directly determined experimentally and must be constrained by an optimization procedure. A common practice in evaluating the utility of such procedures is using a previously developed model to generate surrogate data (e.g., traces of spikes following step current pulses) and then challenging the algorithm to recover the original parameters (e.g., the value of maximal ion channel conductances) that were used to generate the data. In this fashion, the success or failure of the model fitting procedure to find the original parameters can be easily determined. Here we show that some model fitting procedures that provide an excellent fit in the case of such model-to-model comparisons provide ill-balanced results when applied to experimental data. The main reason is that surrogate and experimental data test different aspects of the algorithm{\textquoteright}s function. When considering model-generated surrogate data, the algorithm is required to locate a perfect solution that is known to exist. In contrast, when considering experimental target data, there is no guarantee that a perfect solution is part of the search space. In this case, the optimization procedure must rank all imperfect approximations and ultimately select the best approximation. This aspect is not tested at all when considering surrogate data since at least one perfect solution is known to exist (the original parameters) making all approximations unnecessary. Furthermore, we demonstrate that distance functions based on extracting a set of features from the target data (such as time-to-first-spike, spike width, spike frequency, etc.){\textendash}rather than using the original data (e.g., the whole spike trace) as the target for fitting-are capable of finding imperfect solutions that are good approximations of the experimental data.</p>
},
	keywords = {Algorithms, Animals, Models, Neocortex, Neurological, Neurons, Organ Culture Techniques, Patch-Clamp Techniques, Rats, Wistar},
	issn = {1432-0770},
	doi = {10.1007/s00422-008-0269-2},
	author = {Druckmann, Shaul and Berger, Thomas K and Hill, Sean and Sch{\"u}rmann, Felix and Markram, Henry and Segev, Idan}
}
