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ConnectomesToANNs

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From reconstructions of neuronal circuits to anatomically realistic artificial n...

From reconstructions of neuronal circuits to anatomically realistic artificial neural networks Artificial neural networks (ANNs) have found applications in a wide variety of real-world problems. Despite this tremendous success, artificial intelligence systems still face major challenges due to their reliance on extensive tr... ver más

31/12/2023

MPG

150K€

Presupuesto del proyecto: 150K€

Líder del proyecto

MAXPLANCKGESELLSCHAFT ZUR FORDERUNG DER WISSE... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-12-31

ERC-2022-POC1: ERC PROOF OF CONCEPT GRANTS1 Objective:Objectives:

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No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

MPG

150.00K€ | Lider

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Duración del proyecto: 20 meses Fecha Inicio: 2022-04-26
Fecha Fin: 2023-12-31

Líder del proyecto

MAXPLANCKGESELLSCHAFT ZUR FORDERUNG DER WISSE... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 150K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Artificial neural networks (ANNs) have found applications in a wide variety of real-world problems. Despite this tremendous success, artificial intelligence systems still face major challenges due to their reliance on extensive training and large datasets. Recent reports indicate that the architecture of ANNs could be a prime target for reducing their training and data requirements. We hypothesize that such architectural features can be identified from neuronal networks in the brain, which have evolved to efficiently perform highly specialized functions. Recent advances in electron microscopy will soon provide detailed reconstructions of large-scale neuronal networks from different brain areas, species, developmental stages and/or pathological conditions. However, even if such data become available, directly transforming neuronal network reconstructions into ANNs will raise problems of interpretability, due to their enormous complexity, and generalizability, due to high inter-individual variability. Here, we will resolve these challenges by implementing a set of computational approaches that allow the extraction of rules that explain the wiring properties underlying dense connectomics data, the transfer of these anatomical principles into the design of ANN architectures, and the evaluation of how these principles impact performance on a battery of deep learning tasks. This unique methodology will lay the foundation for groundbreaking insights into how different network architectures facilitate specific brain functions, and also how the underlying anatomical principles can inform the development of more effective and efficient artificial intelligence systems. Our methodology will be publicly accessible online to scientists, but also to companies and non-profit organizations that seek to improve the performance or reduce training data requirements for applications of deep learning.

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