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Research Spending & Results

Award Detail

Doing Business As Name:University of Illinois at Urbana-Champaign
  • Qing Cao
  • (217) 300-8327
  • Jianjun Cheng
  • Charles M Schroeder III
  • Jian Peng
  • Kai Zhang
Award Date:09/19/2021
Estimated Total Award Amount: $ 3,600,000
Funds Obligated to Date: $ 1,200,000
  • FY 2021=$1,200,000
Start Date:10/01/2021
End Date:09/30/2026
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.083
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:GCR: Synthetic Neurocomputers for Cognitive Information Processing
Federal Award ID Number:2121003
DUNS ID:041544081
Parent DUNS ID:041544081
Program:GCR-Growing Convergence Resear
Program Officer:
  • Dragana Brzakovic
  • (703) 292-5033

Awardee Location

Street:1901 South First Street
Awardee Cong. District:13

Primary Place of Performance

Organization Name:Board of Trustees of the University of Illinois
Street:506 S. Wright St.
Cong. District:13

Abstract at Time of Award

The project brings together material scientists and electrical engineers who build synthetic 3D scaffolds with embedded electronic and optoelectronic devices, neuroscientists who culture neural cells on the 3D scaffold to form biological neural networks with precisely defined 3D topology and integrated multimodal information interfaces, chemists and chemical engineers who synthesize functional molecules for controlling the neural cell placement, development, and activity, and computer scientists who operate the neurocomputer prototype and extract its information-coding and processing algorithms with machine-learning methods. The goals of the project are to contribute to the grand challenge of reverse engineering the brain and open up new computing paradigms based on cultured biological neural networks to propel machine learning and artificial intelligence to the next level. The neurocomputer prototype pursued in the project employs biological neuronal circuits engineered into well-defined 3D topologies reminiscent of deep-neural-network models as the information-processing units. Electronic and optoelectronic devices will be integrated with each neural cell to administer and monitor the neuronal and synaptic activities based on electrophysiology, optogenetics, and neurochemistry. The fabricated neurocomputer prototype will then be utilized to perform various learning and computing tasks such as image recognition and space navigation. Neural code and learning algorithms will be extracted using a combination of experiment and simulations based on spike generation models and recurrent neural network models. The results will help reveal how complex living neural networks function and provide a technologically transformative approach to information-processing machines. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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