Open Source Brain: A Collaborative Resource for Visualizing, Analyzing, Simulating, and Developing Standardized Models of Neurons and Circuits

Gleeson, Padraig; Carandini, Matteo; Marin, Bóris; Quintana, Adrián; Earnshaw, Matt; Sadeh, Sadra; Piasini, Eugenio; Birgiolas, Justas; Cannon, Robert C.; Cayco-Gajic, N. Alex; Crook, Sharon; Davison, Andrew P.; Durá-Bernal, Salvador; Ecker, András; Hines, Michael L.; Idili, Giovanni; Lanore, Frédéric; Larson, Stephen; Lytton, William W.; Majumdar, Amitava; McDougal, Robert A.; Sivagnanam, Subhashini; Solinas, Sergio; Stanislovas, Rokas; Albada, Sacha J. van; Geit, Werner Van; Silver, R. Angus

doi:10.1016/j.neuron.2019.05.019

Cited by 69 publications

(72 citation statements)

References 90 publications

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“…New modelling studies have started to account for more biological realism, for example by studying the effect of including multiple subtypes of inhibitory neurons (Litwin-Kumar, Rosenbaum and Doiron, 2016) or nonlinear neuronal transfer functions (Ahmadian, Rubin and Miller, 2013;Rubin, Van Hooser and Miller, 2015;Hennequin et al, 2018). Another extension of the original model to include more biological details (Sadeh et al, 2017;Gleeson et al, 2019) concluded that observation of the paradoxical effect may depend on the size of perturbation of the inhibitory population; a result which was in fact corroborated by subsequent experimental studies (Li et al, 2019;Sanzeni et al, 2019). Further theoretical and experimental exploration of ISNs can, therefore, reveal new insights about the dynamical and functional properties of cortical networks.…”

Section: Introductionmentioning

confidence: 99%

Patterned perturbation of inhibition can reveal the dynamical structure of neural processing

Sadeh

Clopath

2020

eLife

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Perturbation of neuronal activity is key to understanding the brain’s functional properties, however, intervention studies typically perturb neurons in a nonspecific manner. Recent optogenetics techniques have enabled patterned perturbations, in which specific patterns of activity can be invoked in identified target neurons to reveal more specific cortical function. Here, we argue that patterned perturbation of neurons is in fact necessary to reveal the specific dynamics of inhibitory stabilization, emerging in cortical networks with strong excitatory and inhibitory functional subnetworks, as recently reported in mouse visual cortex. We propose a specific perturbative signature of these networks and investigate how this can be measured under different experimental conditions. Functionally, rapid spontaneous transitions between selective ensembles of neurons emerge in such networks, consistent with experimental results. Our study outlines the dynamical and functional properties of feature-specific inhibitory-stabilized networks, and suggests experimental protocols that can be used to detect them in the intact cortex.

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Section: Introductionmentioning

confidence: 99%

Patterned perturbation of inhibition can reveal the dynamical structure of neural processing

Sadeh

Clopath

2020

eLife

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“…For this task, scientists need tools that are up to the challenge. Simulation engines, such as NEURON [16], NEST [17], Brian [18], GENESIS [19], MOOSE [20], Xolotl [21], and others offer high computational performance, and recently a number of software interfaces (e.g., neuro-Construct [22], PyNN [23], NetPyNE [24], Open Source Brain [25], and the Allen Institute's Brain Modeling ToolKit (BMTK, https://alleninstitute.github.io/bmtk/; [26]) have been developed that allow users to interact with these engines without mastering the underlying programming environments of individual simulators. However, the utility of these tools is limited without a broadly applicable, flexible, and high-performance modeling data format.…”

Section: Introductionmentioning

confidence: 99%

The SONATA data format for efficient description of large-scale network models

et al. 2020

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Increasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation, as well as sharing of such large-scale models, a broadly applicable, flexible, and high-performance data format is necessary. To address this need, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. It is designed for memory and computational efficiency and works across multiple platforms. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is used in multiple modeling and visualization tools, and we also provide reference Application Programming Interfaces and model examples to catalyze further adoption. SONATA format is free and open for the community to use and build upon with the goal of enabling efficient model building, sharing, and reproducibility.

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“…Moreover, community-sourced terminologies [11,14,21,38] and domain-specific markup languages [16,24,18] provide human-interpretable controlled vocabularies and machine-readable file formats, respectively. Efforts are also underway to generate standardized data models [15,39,36] and to formalize related concepts into robust ontologies [20,23,25]. As a result, full-text information retrieval systems are becoming indispensable research aids [13,22,28,29].…”

Section: Introductionmentioning

confidence: 99%

An open-source framework for neuroscience metadata management applied to digital reconstructions of neuronal morphology

2020

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Research advancements in neuroscience entail the production of a substantial amount of data requiring interpretation, analysis, and integration. The complexity and diversity of neuroscience data necessitate the development of specialized databases and associated standards and protocols. NeuroMorpho.Org is an online repository of over one hundred thousand digitally reconstructed neurons and glia shared by hundreds of laboratories worldwide. Every entry of this public resource is associated with essential metadata describing animal species, anatomical region, cell type, experimental condition, and additional information relevant to contextualize the morphological content. Until recently, the lack of a user-friendly, structured metadata annotation system relying on standardized terminologies constituted a major hindrance in this effort, limiting the data release pace. Over the past 2 years, we have transitioned the original spreadsheet-based metadata annotation system of NeuroMorpho.Org to a custom-developed, robust, web-based framework for extracting, structuring, and managing neuroscience information. Here we release the metadata portal publicly and explain its functionality to enable usage by data contributors. This framework facilitates metadata annotation, improves terminology management, and accelerates data sharing. Moreover, its open-source development provides the opportunity of adapting and extending the code base to other related research projects with similar requirements. This metadata portal is a beneficial web companion to NeuroMorpho.Org which saves time, reduces errors, and aims to minimize the barrier for direct knowledge sharing by domain experts. The underlying framework can be progressively augmented with the integration of increasingly autonomous machine intelligence components.

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Open Source Brain: A Collaborative Resource for Visualizing, Analyzing, Simulating, and Developing Standardized Models of Neurons and Circuits

Cited by 69 publications

References 90 publications

Patterned perturbation of inhibition can reveal the dynamical structure of neural processing

Patterned perturbation of inhibition can reveal the dynamical structure of neural processing

The SONATA data format for efficient description of large-scale network models

An open-source framework for neuroscience metadata management applied to digital reconstructions of neuronal morphology

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