Biophysically detailed modelling of microcircuits and beyond

Schutter, Erik De; Ekeberg, Örjan; Kotaleski, Jeanette Hellgren; Achard, Pablo; Lansner, Anders

doi:10.1016/j.tins.2005.08.002

Cited by 36 publications

(24 citation statements)

References 57 publications

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“…As previously indicated by Marder and colleagues (2005), however, this level of cellular understanding of itself "is only a starting point to understanding the dynamics of circuit operation." Modeling has been essential to thorough analysis of CPG networks (for recent reviews see De Schutter et al 2005;Marder et al 2005). Although much progress is now being made identifying the key neuronal elements that make up pattern generators in vertebrates, the real circuitry involved in any particular pattern generator is far from certain.…”

Section: Introductionmentioning

confidence: 99%

“…Our current level of understanding suggests that many neuronal elements serve in both functions (Hooper and DiCaprio 2004;Kristan et al 2005;Marder et al 2005). For example, some pattern generators produce segmentally distributed output, such as that controlling undulatory swimming Friesen 2000, 2002;De Schutter et al 2005;Grillner et al 2005;Kristan et al 2005), so that a conceptual separation between rhythm generation as expressed in side-to side or dorsal-ventral alternation and pattern formation as expressed in intersegmental coordination seems clear, although key elements involved in intersegmental coordination appear integral to rhythm generation. The crayfish swimmeret system stands in stark contrast here with network elements clearly separated along these functional lines (Mulloney et al 1998(Mulloney et al , 2006Paul and Mulloney 1985).…”

Section: Introductionmentioning

confidence: 99%

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A Central Pattern Generator Producing Alternative Outputs: Temporal Pattern of Premotor Activity

Norris

Weaver²,

Morris³

et al. 2006

Journal of Neurophysiology

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Central Pattern Generator Producing Alternative Outputs: Temporal Pattern of Premotor Activity

Norris

Weaver²,

Morris³

et al. 2006

Journal of Neurophysiology

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“…To this end, the most progress has been made in invertebrate motor pattern generating networks. Their few and individually identifiable neurons enable identification of the component neurons in these networks and characterization of their synaptic connections and membrane currents (for reviews, see: De Schutter et al 2005;Harris-Warrick 2002;Marder et al 2005;Marder and Calabrese 1996). The plethora of data on intrinsic and synaptic conductances and their kinetics from detailed electrophysiological studies has necessitated modeling to elucidate how intrinsic and synaptic conductances interact to shape neuronal and network electrical activity (e.g.…”

Section: Introductionmentioning

confidence: 99%

Endogenous and Half-Center Bursting in Morphologically Inspired Models of Leech Heart Interneurons

Tobin

Calabrese²

2006

Journal of Neurophysiology

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Based on a detailed morphology 'Full Model' of a leech heart interneuron, we previously developed a computationally-efficient, morphologically-inspired 'Reduced Model' to expedite tuning the model to produce endogenous bursting and alternating bursting when configured as a half-center oscillator (paired with reciprocally inhibitory synapses). To find conductance density distributions that produce endogenous bursting, we implemented a genetic algorithm automated parameter search. With multiple searches, we found eight parameter sets that produced endogenous bursting in the Reduced Model. When these parameter sets were applied to the Full Model, all produced endogenous bursting, although when the simulation time was extended from 80 s to 300 s, only four parameter sets produced sustained bursting in the Reduced Models. All parameter sets produced alternating half-center bursting in the Reduced and Full Models throughout 300 s. When conductance amplitudes were systematically varied for each of the four sustained burster sets, the effects on bursting activity differed, both for the same parameter set in the Reduced and Full Models and for different parameter sets with the same level of morphological detail. This implies that morphological detail can affect burst activity and that these parameter sets may represent different mechanisms for burst generation and/or regulation. We also tested the models with parameter variations that correspond to experimental manipulations. We conclude that whereas similar output can be achieved with multiple different parameter sets, perturbations such as conductance variations can highlight differences. Additionally, this work demonstrates both the utility and limitations of using simplified models to represent more morphologically-accurate models.

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“…2002;De Schutter et al 2005)but there are still very few concrete examples of such approaches in practice. On a technical level, Python based "glue" or its equivalent should be adequate to connect, for example a detailed biophysical model of a synapse run in MCell with a whole cell model run in Neuron or Genesis.…”

Section: Multi-level Interactionmentioning

confidence: 99%

Interoperability of Neuroscience Modeling Software: Current Status and Future Directions

et al. 2007

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Neuroscience increasingly uses computational models to assist in the exploration and interpretation of complex phenomena. As a result, considerable effort is invested in the development of software tools and technologies for numerical simulations and for the creation and publication of models. The diversity of related tools leads to the duplication of effort and hinders model reuse. Development practices and technologies that support interoperability between software systems therefore play an important role in making the modeling process more efficient and in ensuring that published models can be reliably and easily reused. Various forms of interoperability are possible including the development of portable model description standards, the adoption of common simulation languages or the use of standardized middleware. Each of these approaches finds applications within the broad range of current modeling activity. However more effort is required in many areas to enable new scientific questions to be addressed. Here we present the conclusions of the "Neuro-IT Interoperability of Simulators" workshop, held at the 11th computational neuroscience meeting in e-mail: erik@tnb.ua.ac.be. 19-20 2006; http://www.cnsorg.org). We assess the current state of interoperability of neural simulation software and explore the future directions that will enable the field to advance. NIH Public Access

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Biophysically detailed modelling of microcircuits and beyond

Cited by 36 publications

References 57 publications

A Central Pattern Generator Producing Alternative Outputs: Temporal Pattern of Premotor Activity

A Central Pattern Generator Producing Alternative Outputs: Temporal Pattern of Premotor Activity

Endogenous and Half-Center Bursting in Morphologically Inspired Models of Leech Heart Interneurons

Interoperability of Neuroscience Modeling Software: Current Status and Future Directions

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