Robust Parameter Selection for Compartmental Models of Neurons Using Evolutionary Algorithms

West, Rogene M. Eichler; Wilcox, George L.

doi:10.1007/978-1-4757-9800-5_13

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…There have been a number of studies where GAs were used to search the parameter space of biophysically realistic single-neuron simulations (Achard and De Schutter 2006;Eichler West 1997;Keren et al 2005;Vanier and Bower 1999). In these studies, GAs were used to find the conductance densities for a model of a hippocampal pyramidal neuron so that it would most closely reproduce experimental data.…”

Section: Relation To Previous Workmentioning

confidence: 99%

“…The application of our method will help elucidate the computational significance of these differences. The studies mentioned in the preceding text that use GAs to fit models to experimental data (Achard and De Schutter 2006;Eichler West 1997;Keren et al 2005;Vanier and Bower 1999) indicate that GAs can find solutions in higher-dimensional parameters spaces that include the parameters for active conductances.…”

Section: Future Directionsmentioning

confidence: 99%

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Mapping Function Onto Neuronal Morphology

Stiefel¹,

Sejnowski²

2007

Journal of Neurophysiology

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Neurons have a wide range of dendritic morphologies the functions of which are largely unknown. We used an optimization procedure to find neuronal morphological structures for two computational tasks: first, neuronal morphologies were selected for linearly summing excitatory synaptic potentials (EPSPs); second, structures were selected that distinguished the temporal order of EPSPs. The solutions resembled the morphology of real neurons. In particular the neurons optimized for linear summation electrotonically separated their synapses, as found in avian nucleus laminaris neurons, and neurons optimized for spike-order detection had primary dendrites of significantly different diameter, as found in the basal and apical dendrites of cortical pyramidal neurons. This similarity makes an experimentally testable prediction of our theoretical approach, which is that pyramidal neurons can act as spike-order detectors for basal and apical inputs. The automated mapping between neuronal function and structure introduced here could allow a large catalog of computational functions to be built indexed by morphological structure.

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Section: Relation To Previous Workmentioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

Mapping Function Onto Neuronal Morphology

Stiefel¹,

Sejnowski²

2007

Journal of Neurophysiology

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“…In a similar way, several researchers in neurobiology are using evolutionary algorithms, either for setting synaptic weights of neural network models (Eurich, Roth, Schwegler, & Wiggers, 1995;Eurich, Schwegler, & NVoesler, 1997), or for determining conductance parameters of biophysical models of single neurons (West & Wilcox, 1997). From a general point of view, note that, although the GA can be very useful for demonstrating that a model can produce a specific behavior (by finding efficient sets of unknown variables), it is less useful for invalidating a hypothetical model, as an inability to find successful variable instantiations may be due to failings of the model or to problems with the GA set up, or both.…”

Section: Ga For Neurobiological Modelingmentioning

confidence: 99%

Evolving Swimming Controllers for a Simulated Lamprey with Inspiration from Neurobiology

1999

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This paper presents how neural swimming controllers for a simulated lamprey can be developed using evolutionary algorithms. A genetic algorithm is used for evolving the architecture of a connectionist model which determines the muscular activity of a simulated body in interaction with water. This work is inspired by the biological model developed by Ekeberg which reproduces the central pattern generator observed in the real lamprey (Ekeberg, 1993). In evolving artificial controllers, we demonstrate that a genetic algorithm can be an interesting design technique for neural controllers and that there exist alternative solutions to the biological connectivity. A variety of neural controllers are evolved which can produce the pattern of oscillations necessary for swimming. These patterns can be modulated through the external excitation applied to the network in order to vary the speed and the direction of swimming. The best evolved controllers cover larger ranges of frequencies, phase lags and speeds of swimming than Ekeberg's model. We also show that the same techniques for evolving artificial solutions can be interesting tools for developing neurobiological models. In particular, biologically plausible controllers can be developed with ranges of oscillation frequency much closer to those observed in the real lamprey than Ekeberg's hand-crafted model.

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Minimal Hodgkin–Huxley type models for different classes of cortical and thalamic neurons

et al. 2008

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We review here the development of Hodgkin-Huxley (HH) type models of cerebral cortex and thalamic neurons for network simulations. The intrinsic electrophysiological properties of cortical neurons were analyzed from several preparations, and we selected the four most prominent electrophysiological classes of neurons. These four classes are "fast spiking", "regular spiking", "intrinsically bursting" and "low-threshold spike" cells. For each class, we fit "minimal" HH type models to experimental data. The models contain the minimal set of voltage-dependent currents to account for the data. To obtain models as generic as possible, we used data from different preparations in vivo and in vitro, such as rat somatosensory cortex and thalamus, guinea-pig visual and frontal cortex, ferret visual cortex, cat visual cortex and cat association cortex. For two cell classes, we used automatic fitting procedures applied to several cells, which revealed substantial cell-to-cell variability within each class. The selection of such cellular models constitutes a necessary step towards building network simulations of the thalamocortical system with realistic cellular dynamical properties.

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Robust Parameter Selection for Compartmental Models of Neurons Using Evolutionary Algorithms

Cited by 5 publications

References 9 publications

Mapping Function Onto Neuronal Morphology

Mapping Function Onto Neuronal Morphology

Evolving Swimming Controllers for a Simulated Lamprey with Inspiration from Neurobiology

Minimal Hodgkin–Huxley type models for different classes of cortical and thalamic neurons

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