AI Institute: Planning: BI4ALL: Understanding Biological
NSF 20-503 (Kording, K.): 10/2020 - 07/2022 NSF
The goal of this project is to plan an AI instiutition via several meetings and workshops
Collaborative Research: Transferable, Hierarchical, Expensive, Optimal, Robust, Interpretable Networks
NSF 20-540 (Vindal, R.): 09/2020 - 08/2025 NSF
The goal of this project is to develop a mathematical, statistical and computational framework that helps explain the success of current network arcitectures, understand its pitfalls, and guide the design of novel architectures with guaranteed confidence, robustness, interpretability, optimality, and transferability.
Graspy: A python package for rigorous statistical analysis of populations of attributed connectomes
NIH MH-19-147 (Vogelstein): 07/2020 - 06/2023 NIH
The goal of this project is to establish a state-of-the-art toolbox for analysis of connectomes, spanning taxa, scale, and complexity. we will develop and extend implementations to enable neurobiologists to (1) estimate latent structure from attributed connectomes, (2) identify meaningful clusters among populations of connectomes, and (3) detect relationships between connectomes and multivariate phenotypes.
NeuroNex2: Enabling Identification and Impact of Synaptic Weight in Functional Networks
NSF 2014862 (Harris, K.): 04/2020 - 03/2025 NSF
The goal is to develop the requisite technology to understand the impact of synaptic weight on functional networks
CAREER: Foundational Statistical Theory and Methods for Analysis of Populations of Attributed
NSF 17-537 (Vogelstein): 01/2020 - 12/2025 NSF
The goal is to establish foundational theory and methods for analyzing populations of attributed connectomes.
1R01MH120482-01 (Satterthwaite): 12/2019 - 11/2023 NIH
The goal of this proposal will be to provide a new data resource, yield reproducible growth charts of brain development, and delineate novel mechanisms regarding the developmental basis of psychopathology in youth.
Brain Networks in Mouse Models of Aging
NIH RO1AG066184-01 (Badea, A.): 12/01/2019 - 11/30/2023 NIH
The goal of this grant is to generate connectomes and RNA-seq transcriptomes to characterize and differentiate APOE mice as a model of aging.
Accessible technologies for high-throughput, whole-brain reconstructions of molecularly characterized mammalian neurons
RFA-MH-19-148 (Mueller, M.): 09/2019 - 08/2022 NIH
The goal of this grant will be to develop scalable and affordable cellular imaging and neuro-informatics tools, running preliminary experiments to connect the transcriptome to anatomy, in mice. Tools will be made available to researchers, to help accelerate the creation of detailed maps at cell resolution showing circuitry in whole brains.
SemiSynBio: Collaborative Research: YeastOns: Neural Networks Implemented in Communication Yeast Cells
NSF 1807546 (Schulman, E.): 07/16/2018 - 06/30/2021
The goal is to provide neuroscience and machine learning expertise to guide the design of the computational learning capabilities of the system.
Lifelong Learning Forests
FA8650-18-2-7834 (Vogelstein): 11/1/2017 - 10/31/2021
Our Lifelong Learning Forests (L2Fs) will learn continuously, selectively adapting to new environments and circumstances utilizing top-down feedback to impact low-level processing, with provable statistical guarantees, while maintaining computational tractability at scale.
This work is graciously supported by the Defense Advanced Research Projects Agency (DARPA) Lifelong Learning Machines program through contract FA8650-18-2-7834.
Continual Learning Across Synapses, Circuits, and Brain Areas
FA8650-18-2-7834 (Tolias, A.): 11/1/2017 - 10/31/2021
Our primary goal will be to develop the pre-processing analysis pipeline for the imaging data collected in this project.
This research has been supported by the Lifelong Learning Machines (L2M) program of the Defence Advanced Research Projects Agency (DARPA) via contract number HR0011-18-2-0025
Sensorimotor processing, decision-making, and internal states: towards a realistic multiscale circuit model of the larval zebrafish brain
1U19NS104653-01 (Engert): 09/01/2017 - 08/31/2022
Harvard University / Prime: NIH
The general goal of the proposal is to generate a realistic multiscale circuit model of the larval zebrafish’s brain – the multiscale virtual fish (MSVF). The model will span spatial ranges from the nanoscale at the synaptic level, to local microcircuits to inter-area connectivity - and its ultimate purpose is to explain and simulate the quantitative and qualitative nature of behavioral output across various timescales.
1707298 (Vogelstein): 07/01/2017 - 06/30/2019
NSF, 16-569 Neural System Cluster
We propose to lower the barrier to connecting data to analyses and models by providing a coherent cloud computational ecosystem that minimizes current bottlenecks in the scientific process.
We would like to acknowledge generous support from National Science Foundation (NSF) under NSF Award Number EEC-1707298.
Connectome Coding at the Synaptic Scale
128503 (Vogelstein): 1/01/2018 - 12/31/2019
This project will study learning and plasticity at an unprecedented scale, revealing the dynamics of large populations of synapses comprising an entire local cortical circuit. No previously conducted experiment could answer the questions about the dynamics of large populations of synapses, which is crucial to the learning process.
1R01DC016784-01 (Ratnanather J.): 07/01/2017 - 06/30/2020
The goal of this project is to create a robust computational framework for analyzing the cortical ribbon in a specific region: the auditory cortex.
Multiscale Generalized Correlation: A Unified Distance-Based Correlation Measure for Dependence Discovery
1712947 (Shen, C.): 05/01/2017 - 04/30/2020
This project aims to establish a unified methodology framework for statistical testing in highdimensional, noisy, big data, through theoretical advancements, comprehensive simulations, and real data experiments.
This work was partially supported by the National Science Foundation award DMS1712947.
What Would Tukey Do?
FA8750-17-2-0112 (Priebe, C.): 10/01/2016 - 09/30/2020
The goal is to develop theory & methods for generating a discoverable archive of data modeling primitives and for automatically selecting model primitives and for composing selected primitives into complex modeling pipelines based on user-specified data and outcome(s) of interest.
The authors are grateful for the support by the XDATA program of the Defense Advanced Research Projects Agency (DARPA) administered through Air Force Research Laboratory contract FA8750-12-2-0303
NeuroNex Technology Hub: Towards the International Brain Station for Accelerating and Democratizing Neuroscience Data Analysis and Modeling
NSF 16-569 (Vogelstein): 2017 - 2019 NSF
We proposed to lower the barrier to connecting data to analyses and models by providing a coherent cloud computational ecosystem that minimizes current bottlenecks in the scientific process.
The Brain Ark
90076467 (Vogelstein): 2017 - 2018 DARPA
Characterize the statistical properties of the individual graphs, to identify circuit motifs, both that specialize in a species specific fashion, and that are preserved across species. As a test, will compare the connectomes of sea lions and coyotes.
The International Brain Station
90071826 (Vogelstein): 2017 - 2018 The Kavli Foundation
Take the first few steps towards building the international brain station.
Brain Comp Infra: EAGER: BrainLab CI: Collaborative, Community Experiments
ACI-1649880 (Miller B.): 2017 - 2018 NSF
The BrainLab CI prototype system will deploy an experimental-management infrastructure that allows users to construct community-wide experiments that implement data and metadata controls on the inclusion and exclusion of data.
A Scientific Planning Workshop for Coordinating Brain Research Around the Globe
NSF 1637376 Part 1 of 2 (Vogelstein): 2016 - 2019 NSF
This travel grant is for the expressed purposes of gathering researchers from around the globe to discuss the new way to further brain research during part one of a two day conference.
A Scientific Planning Workshop for Coordinating Brain Research Around the Globe
NSF 1637376 Part 2 of 2 (Vogelstein): 2016 - 2019 NSF
This travel grant is for the expressed purposes of gathering researchers from around the globe to further discuss advancements in brain research during the second part of a two day conference.
From RAGs to Riches: Utilizing Richly Attributed Graphs to Reason from
N66001-15-C-40401 (Vogelstein): 2015 - 2018 DARPA
Multiple, large, multifarious brain imaging datasets are rapidly becoming standards in neuroscience. Yet, we lack the tools to analyze individual datasets, much less populations thereof. Therefore, we will develop theory and methods to analyze and otherwise make such data available.
Scalable Grain Graph Analyses Using Big-Memory, High-IPS Compute Architectures
N66001-14-1-4028 (Burns, R.): 2014 - 2016 DARPA
Build software infrastructure to enable analytics on billion node, terabyte sized networks using commodity hardware.
Synaptomes of Mouse and Man
NIH R01NS092474 (Smith, S.):2014 - 2019 NIH The major goals of this project are to discover the synaptic diversity and complexity in mammalian brains, specifically comparing and contrasting humans with mice, the leading experimental animal.
CRCNS: Data Sharing: The EM open Connectome Project
RO1EB16411 (Burns, R.): 2012 – 2015 National Institute of Biomedical Imaging and Bioengineering
Develop cyberinfrastructure to support management, visualization, storage, and analysis of large-scale electron microscopy data.