Funding

Funding

Current

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.

Reproducible Imaging-based brain growth charts for psychiatry

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
NSF

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
DARPA

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
DARPA

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.

NeuroNex Innovation Award: Towards Automatic Analysis of Multi-Terabyte Cleared Brains

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.

Completed

Connectome Coding at the Synaptic Scale

128503 (Vogelstein): 1/01/2018 - 12/31/2019
Schmidt Sciences

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.

CRCNS US-German Res Prop: functional computational anatomy of the auditory cortex

1R01DC016784-01 (Ratnanather J.): 07/01/2017 - 06/30/2020
NIH

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
NSF

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
DARPA

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.