Projects per year
Abstract
Recovering brain connectivity from tract tracing data is an important computational problem in the neurosciences. Mesoscopic connectome reconstruction was previously formulated as a structured matrix regression problem (Harris et al. in Neural Information Processing Systems, 2016), but existing techniques do not scale to the whole-brain setting. The corresponding matrix equation is challenging to solve due to large scale, ill-conditioning, and a general form that lacks a convergent splitting. We propose a greedy low-rank algorithm for the connectome reconstruction problem in very high dimensions. The algorithm approximates the solution by a sequence of rank-one updates which exploit the sparse and positive definite problem structure. This algorithm was described previously (Kressner and Sirković in Numer Lin Alg Appl 22(3):564–583, 2015) but never implemented for this connectome problem, leading to a number of challenges. We have had to design judicious stopping criteria and employ efficient solvers for the three main sub-problems of the algorithm, including an efficient GPU implementation that alleviates the main bottleneck for large datasets. The performance of the method is evaluated on three examples: an artificial “toy” dataset and two whole-cortex instances using data from the Allen Mouse Brain Connectivity Atlas. We find that the method is significantly faster than previous methods and that moderate ranks offer a good approximation. This speedup allows for the estimation of increasingly large-scale connectomes across taxa as these data become available from tracing experiments. The data and code are available online.
Original language | English |
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Article number | 9 |
Journal | The Journal of Mathematical Neuroscience |
Volume | 9 |
Issue number | 1 |
Early online date | 14 Nov 2019 |
DOIs | |
Publication status | Published - 1 Dec 2019 |
Keywords
- Computational neuroscience
- Low-rank approximation
- Matrix equations
- Networks
ASJC Scopus subject areas
- Neuroscience (miscellaneous)
Fingerprint
Dive into the research topics of 'Greedy low-rank algorithm for spatial connectome regression'. Together they form a unique fingerprint.Projects
- 2 Finished
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Tensor product numerical methods for high-dimensional problems in probability and quantum calculations
Dolgov, S. (PI)
1/01/16 → 31/12/18
Project: Research council
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Sergey Dolgov Fellowship - Tensor Product Numerical Methods for High-Dimensional Problems in Probablility and Quantum Calculations
Scheichl, R. (PI)
Engineering and Physical Sciences Research Council
1/01/16 → 31/12/18
Project: Research council
Equipment
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Balena High Performance Computing (HPC) System
Facility/equipment: Equipment
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High Performance Computing (HPC) Facility
Chapman, S. (Manager)
University of BathFacility/equipment: Facility