Calcium regulation of HCN channels supports persistent activity in a multiscale model of neocortex

Neymotin, Samuel A.; McDougal, Robert A.; Bulanova, Anna; Zeki, Mustafa; Lakatos, Péter L.; Terman, David; Hines, Michael L.; Lytton, William W.

doi:10.1016/j.neuroscience.2015.12.043

Cited by 36 publications

(37 citation statements)

References 145 publications

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“…GENESIS [42], MOOSE [28], [46], and NEURON [40] are simulators that aspire to the largest multiscale range, from molecular [22], [47], [48] to large networks [49], [50], with the ability to simulate across these multiple scales [51]. These simulators generally encourage the use of multicompartmental models of neurons, utilizing neuroanatomically-acquired dendritic tree morphologies approximated as tree-structured collections of frusta and cylinders.…”

Section: Neuroscience Simulatorsmentioning

confidence: 99%

“…There exists a number of neuroscience related models in SBML format, as well as models that are of general interest in biology. Researchers have also developed models that include both Computational Neuroscience and Systems Biology components, creating, for example, a multiscale simulation of a neuron or network with subcomponents modified from SBML models and other sources [51], [113]. SBMLMerge assists with merging separate SBML files into a combined model [114].…”

Section: Declarative Model Descriptionsmentioning

confidence: 99%

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Reproducibility in Computational Neuroscience Models and Simulations

McDougal

Bulanova

Lytton

2016

IEEE Trans. Biomed. Eng.

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Objective Like all scientific research, computational neuroscience research must be reproducible. Big data science, including simulation research, cannot depend exclusively on journal articles as the method to provide the sharing and transparency required for reproducibility. Methods Ensuring model reproducibility requires the use of multiple standard software practices and tools, including version control, strong commenting and documentation, and code modularity. Results Building on these standard practices, model sharing sites and tools have been developed that fit into several categories: 1. standardized neural simulators, 2. shared computational resources, 3. declarative model descriptors, ontologies and standardized annotations; 4. model sharing repositories and sharing standards. Conclusion A number of complementary innovations have been proposed to enhance sharing, transparency and reproducibility. The individual user can be encouraged to make use of version control, commenting, documentation and modularity in development of models. The community can help by requiring model sharing as a condition of publication and funding. Significance Model management will become increasingly important as multiscale models become larger, more detailed and correspondingly more difficult to manage by any single investigator or single laboratory. Additional big data management complexity will come as the models become more useful in interpreting experiments, thus increasing the need to ensure clear alignment between modeling data, both parameters and results, and experiment.

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Section: Neuroscience Simulatorsmentioning

confidence: 99%

Section: Declarative Model Descriptionsmentioning

confidence: 99%

Reproducibility in Computational Neuroscience Models and Simulations

McDougal

Bulanova

Lytton

2016

IEEE Trans. Biomed. Eng.

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“…Whereas multiscale models are rapidly developing in other areas of neuroscience 180,187-190 , they are only starting to be utilized in AD 191 . Therefore, we examine below how multiscale modelling in other diseases and biological systems can enable realistic and rapid examination of the effect of AD genetic variants, in a way that is useful for clinical progress.…”

Section: Multiscale Models Of Admentioning

confidence: 99%

“…Indeed, the absence of constraining data is a challenge for modeling efforts in many domains of biology, and has no easy solution. However large-scale and multiscale models have yielded useful insights in several areas of biology, including whole-cell modeling 210 , cancer 177 , cardiology 176,178 , immunology 175 and neuroscience 190,192,211,212 . Moreover, the data required to constrain multiscale models is rapidly accumulating as part of the data-heavy, multicontinental collaboration initiatives led by private and public funders 213-215 , such as the large-scale coordination of open science for target discovery in the Accelerating Medicines Partnership for Alzheimer Disease (AMP-AD) .…”

Section: Multiscale Models Of Admentioning

confidence: 99%

Genetic variants in Alzheimer disease — molecular and brain network approaches

et al. 2016

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Genetic studies in late-onset Alzheimer disease (LOAD) are aimed at identifying core disease mechanisms and providing potential biomarkers and drug candidates to improve clinical care for AD. However, due to the complexity of LOAD, including pathological heterogeneity and disease polygenicity, extracting actionable guidance from LOAD genetics has been challenging. Past attempts to summarize the effects of LOAD-associated genetic variants have used pathway analysis and collections of small-scale experiments to hypothesize functional convergence across several variants. In this review, we discuss how the study of molecular, cellular and brain networks provides additional information on the effect of LOAD-associated genetic variants. We then discuss emerging combinations of omic data types in multiscale models, which provide a more comprehensive representation of the effect of LOAD-associated genetic variants at multiple biophysical scales. Further, we highlight the clinical potential of mechanistically coupling genetic variants and disease phenotypes with multiscale brain models.

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“…The field of computational neuroscience has advanced significantly beyond artificial neural networks by using explicit experimental data to build biomimetic models of brain dynamics that can then be used to perform tasks [1–3]. The brain functions at many different but interdependent spatial and temporal scales, ranging from molecular interactions at the single neuron level, to small circuits of thousands of neurons, to information exchange between multiple areas involving millions of neurons.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis

Durá-Bernal¹,

Neymotin²,

Kerr³

et al. 2017

IBM J. Res. & Dev.

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Biomimetic simulation permits neuroscientists to better understand the complex neuronal dynamics of the brain. Embedding a biomimetic simulation in a closed-loop neuroprosthesis, which can read and write signals from the brain, will permit applications for amelioration of motor, psychiatric, and memory-related brain disorders. Biomimetic neuroprostheses require real-time adaptation to changes in the external environment, thus constituting an example of a dynamic data-driven application system. As model fidelity increases, so does the number of parameters and the complexity of finding appropriate parameter configurations. Instead of adapting synaptic weights via machine learning, we employed major biological learning methods: spike-timing dependent plasticity and reinforcement learning. We optimized the learning metaparameters using evolutionary algorithms, which were implemented in parallel and which used an island model approach to obtain sufficient speed. We employed these methods to train a cortical spiking model to utilize macaque brain activity, indicating a selected target, to drive a virtual musculoskeletal arm with realistic anatomical and biomechanical properties to reach to that target. The optimized system was able to reproduce macaque data from a comparable experimental motor task. These techniques can be used to efficiently tune the parameters of multiscale systems, linking realistic neuronal dynamics to behavior, and thus providing a useful tool for neuroscience and neuroprosthetics.

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Calcium regulation of HCN channels supports persistent activity in a multiscale model of neocortex

Cited by 36 publications

References 145 publications

Reproducibility in Computational Neuroscience Models and Simulations

Reproducibility in Computational Neuroscience Models and Simulations

Genetic variants in Alzheimer disease — molecular and brain network approaches

Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis

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