scholarly journals The SONATA Data Format for Efficient Description of Large-Scale Network Models

2019 ◽  
Author(s):  
Kael Dai ◽  
Juan Hernando ◽  
Yazan N. Billeh ◽  
Sergey L. Gratiy ◽  
Judit Planas ◽  
...  
Keyword(s):  
Network Architecture ◽  
High Performance ◽  
Large Scale ◽  
Computational Models ◽  
Model Building ◽  
Network Models ◽  
Data Format ◽  
Large Scale Network ◽  
Scale Network ◽  
Biological Realism

AbstractIncreasing 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.

scholarly journals The SONATA Data Format for Efficient Description of Large-Scale Network Models

2019 ◽  
Author(s):  
Kael Dai ◽  
Juan Hernando ◽  
Yazan N. Billeh ◽  
Sergey L. Gratiy ◽  
Judit Planas ◽  
...  
Keyword(s):  
Large Scale ◽  
Network Models ◽  
Data Format ◽  
Large Scale Network ◽  
Scale Network

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

2020 ◽  
Vol 16 (2) ◽  
pp. e1007696 ◽  
Author(s):  
Kael Dai ◽  
Juan Hernando ◽  
Yazan N. Billeh ◽  
Sergey L. Gratiy ◽  
Judit Planas ◽  
...  
Keyword(s):  
Large Scale ◽  
Network Models ◽  
Data Format ◽  
Large Scale Network ◽  
Scale Network

scholarly journals Local axonal conduction delays underlie precise timing of a neural sequence

2019 ◽  
Author(s):  
Robert Egger ◽  
Yevhen Tupikov ◽  
Kalman A. Katlowitz ◽  
Sam E. Benezra ◽  
Michel A. Picardo ◽  
...  
Keyword(s):  
Cognitive Processes ◽  
Large Scale ◽  
Critical Role ◽  
Network Models ◽  
Axonal Conduction ◽  
Large Scale Network ◽  
Rat Neocortex ◽  
Scale Network ◽  
Sequential Activation ◽  
Local Circuits

SUMMARYSequential activation of neurons has been observed during various behavioral and cognitive processes and is thought to play a critical role in their generation. Here, we studied a circuit in the songbird forebrain that drives the performance of adult courtship song. In this region, known as HVC, neurons are sequentially active with millisecond precision in relation to behavior. Using large-scale network models, we found that HVC sequences could only be accurately produced if sequentially active neurons were linked with long and heterogeneous axonal conduction delays. Although such latencies are often thought to be negligible in local microcircuits, we empirically determined that HVC interconnections were surprisingly slow, generating delays up to 22 ms. An analysis of anatomical reconstructions suggests that similar processes may also occur in rat neocortex, supporting the notion that axonal conduction delays can sculpt the dynamical repertoire of a range of local circuits.

scholarly journals Towards a Better Understanding of Large-Scale Network Models

2012 ◽  
Vol 20 (2) ◽  
pp. 408-421 ◽  
Author(s):  
Guoqiang Mao ◽  
Brian D. O. Anderson
Keyword(s):  
Large Scale ◽  
Network Models ◽  
Large Scale Network ◽  
Scale Network

scholarly journals Pyntacle: a parallel computing-enabled framework for large-scale network biology analysis

GigaScience ◽  
2020 ◽  
Vol 9 (10) ◽  
Author(s):  
Luca Parca ◽  
Mauro Truglio ◽  
Tommaso Biagini ◽  
Stefano Castellana ◽  
Francesco Petrizzelli ◽  
...  
Keyword(s):  
Parallel Computing ◽  
High Performance ◽  
Large Scale ◽  
Critical Groups ◽  
Natural Systems ◽  
Performance Framework ◽  
Large Scale Network ◽  
Potential Applications ◽  
Scale Network ◽  
Computational Resources

Abstract Background Some natural systems are big in size, complex, and often characterized by convoluted mechanisms of interaction, such as epistasis, pleiotropy, and trophism, which cannot be immediately ascribed to individual natural events or biological entities but that are often derived from group effects. However, the determination of important groups of entities, such as genes or proteins, in complex systems is considered a computationally hard task. Results We present Pyntacle, a high-performance framework designed to exploit parallel computing and graph theory to efficiently identify critical groups in big networks and in scenarios that cannot be tackled with traditional network analysis approaches. Conclusions We showcase potential applications of Pyntacle with transcriptomics and structural biology data, thereby highlighting the outstanding improvement in terms of computational resources over existing tools.

scholarly journals Bridging large-scale neuronal recordings and large-scale network models using dimensionality reduction

2018 ◽  
Author(s):  
Ryan C Williamson ◽  
Brent Doiron ◽  
Matt A Smith ◽  
Byron M Yu
Keyword(s):  
Dimensionality Reduction ◽  
Large Scale ◽  
Common Ground ◽  
Network Models ◽  
Neuronal Population ◽  
Dimensional Structure ◽  
Neural Network Modeling ◽  
Population Activity ◽  
Large Scale Network ◽  
Scale Network

A long-standing goal in neuroscience has been to bring together neuronal recordings and neural network modeling to understand brain function. Neuronal recordings can inform the development of network models, and network models can in turn provide predictions for subsequent experiments. Traditionally, neuronal recordings and network models have been related using single-neuron and pairwise spike train statistics. We review here recent studies that have begun to relate neuronal recordings and network models based on the multi-dimensional structure of neuronal population activity, as identified using dimensionality reduction. This approach has been used to study working memory, decision making, motor control, and more. Dimensionality reduction has provided common ground for incisive comparisons and tight interplay between neuronal recordings and network models.

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