Funding

Funding

Multiscale Generalized Correlation: A Unified Distance-Based Correlation Measure for Dependence Discovery

1712947 (Shen): 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.

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

1R01DC016784-01 (Ratnanather): 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.

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.

What Would Tukey Do?

FA8750-17-2-0112 (Priebe): 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

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.

Connectome Coding at the Synaptic Scale

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 understanding the learning process.

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): 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

SemiSynBio: Collaborative Research: YeastOns: Neural Networks Implemented in Communication Yeast Cells

NSF 1807546 (Schulman): 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.