From reconstructions of neuronal circuits to anatomically realistic artificial neural networks

Project description

Brain networks useful for artificial intelligence

Artificial neural networks (ANNs) hold great opportunities and possibilities for advancements for many industries and services, nations, and organizations, from improved automation to ease of use and more efficiency. However, ANNs require extensive training and large amounts of data to accomplish even the most simplest of functions. By contrast, biological neural networks have evolved in the brain to perform highly complex functions efficiently. The ERC-funded ConnectomesToANNs project will provide several computational tools for capturing basic biological principles from reconstructions of neural networks in the brain in the design of ANNs. These 'biological' ANNs will improve the performance of artificial intelligence applications, while reducing training and data requirements.

Fields of science

• natural sciences › physical sciences › optics › microscopy › electron microscopy
• natural sciences › computer and information sciences › artificial intelligence › machine learning › deep learning
• natural sciences › computer and information sciences › artificial intelligence › computational intelligence

Programme(s)

• HORIZON.1.1 - European Research Council (ERC) Main Programme

Topic(s)

• ERC-2022-POC1 - ERC PROOF OF CONCEPT GRANTS1

Call for proposal

ERC-2022-POC1

See other projects for this call

Funding Scheme

HORIZON-AG-LS - HORIZON Lump Sum Grant

Coordinator

MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV

Net EU contribution

€ 150 000,00

Address

Hofgartenstrasse 8
80539 Munchen
Germany

Region

Bayern › Oberbayern › München, Kreisfreie Stadt

Activity type

Research Organisations

Links

Other funding

No data