Computational Neuroscience Breakthroughs through Innovative Data Management

Abstract

Abstract. Simulations have become key in many scientific disciplines to better understand natural phenomena. Neuroscientists, for example, build and simulate increasingly fine-grained models (including subcellular details, e.g., neurotransmitter) of the neocortex to understand the mechanisms causing brain diseases and to test new treatments in-silico. The sheer size and, more importantly, the level of detail of their models challenges today's spatial data management techniques. In collaboration with the Blue Brain project (BBP) we develop new approaches that efficiently enable analysis, navigation and discovery in spatial models of the brain. More precisely, we develop an index for the scalable and efficient execution of spatial range queries supporting model building and analysis. Furthermore, we enable navigational access to the brain models, i.e., the execution of of series of range queries where he location of each query depends on the previous ones. To efficiently support navigational access, we develop a method that uses previous query results to prefetch spatial data with high accuracy and therefore speeds up navigation. Finally, to enable discovery based on the range queries, we conceive a novel in-memory spatial join. The methods we develop considerably outperform the state of the art, but more importantly, they enable the neuroscientists to scale to building, simulating and analyzing massively bigger and more detailed brain models.

A Data Structure for Real-Time Aggregation Queries of Big Brain Networks

“…They are working on a local level of a single network with a fixed scale. Similar accounts for Sherbondy et al (Sherbondy et al 2005), who used queries on volumes of interest and pre-computed pathways to explore diffusion tensor imaging data, and Tauheed et al (Tauheed et al 2013), who developed spatial management techniques for dense spatial neuron simulations.…”

Efficient computation and communication management for all-pairs interactions

“…Spatial databases are unique in that tuples are related by their spatial relationship to one another. Tauheed et al (2013) introduced FLAT for neuroscientists. Indexes are stored in a tree structure with data at the leafs.…”

“…They were able to demonstrate far superior read performance compared to existing techniques. Tauheed et al (2013) also introduced SCOUT for neuroscientists. Past tuple sets accessed during queries are summarized and after learning from a few queries, SCOUT can predict which sets will likely need to be accessed and prefetch them to memory.…”

“…This increases hit rate and speeds up queries. Tauheed et al (2013) also introduced TOUCH. In-memory joins are a challenge because memory is a limited resource.…”