Neuroinformatics and Data Analytics

Neuroscience is increasingly a global challenge, with data and models produced by laboratories and research groups around the world.  The US BRAIN Initiative and other large scale neural efforts are producing data at ever increasing scales, with the necessary capabilities to consolidate and analyze this data being increasingly appreciated by the global neuroscience community.  


Sandia neuroscientists are contributing to the growing neuroinformatics communty’s effort by developing tools and computational infrastructure to facilitate data sharing and collaboration between experimentalists and theorists.  See sidebar for description of the N2A tool.

Neural Data Analytics

Consolidating the vast sources of neural data is only part of the challenge.  Deriving insights from high throughput neural experiments and interpreting concepts across experiments are looming challenges to the neuroscience community.  Sandia Neuroscience partners with experts around Sandia’s computing community who both develop sophisticated data analytics methods and engineer computing systems to enable their use at large scale.  Areas of interest include:

  • Graph Analytics
  • Image Processing
  • Machine Learning
  • Bioinformatics

Featured Project

Neurons to Algorithms

Image of N2A_logo.png

The Neurons to Algorithms software tool, aka N2A, is a software tool capable of compositionally building complex neural models from simple parts represented as systems of dynamical equations.  Because systems of dynamical equations can represent most physical processes in the brain, this enables the generation of robust, multiscale models that can leverage data and models originated at scales ranging from cellular processes to structural plasticity in neural circuits.

In addition to serving as a platform for consolidating neural models from across research efforts, N2A is being developed to serve as a front end “neural compiler” for neuromorphic hardware, including those being developed within Sandia.

For more information, see Rothganger et al., Frontiers in Neural Circuits, 2014

Selected References

Vineyard CM, Verzi SJ, James CD, and Aimone JB – “Quantifying Neural Information Content: A Caste Study of the Impact of Hippocampal Adult Neurogenesis” Proceedings of the International Joint Conference on Neural Networks 2016

Rothganger F, Warrender CE, Trumbo D, and Aimone JB – “N2A: a novel computational tool for modeling from neurons to algorithms” Frontiers in Neural Circuits. January2014