Project description
Computational neuroscience to advance Explainable AI
Recent progress in AI has heavily relied on deep artificial neural networks (ANNs) and machine learning (ML). These technologies have widespread applications in transportation, energy distribution, and medical diagnosis. The field of explainable AI (XAI) has emerged to address the challenge of making AI decisions more transparent, but it has struggled, so far, to do so effectively. Supported by the Marie Skłodowska-Curie Actions programme, the Neurosci-ANN project will apply novel techniques from computational neuroscience (CNS) to advance XAI and uncover critical insights that could help researchers revolutionise AI and robotics.
Objective
Recent progress in artificial intelligence (AI) has been mostly due to machine learning and, in particular, deep artificial neural networks (ANNs). Deep learning has an increasing presence in everyday life, including critical applications such as medical diagnosis, transportation, and energy distribution. In response to this, the field of Explainable AI (XAI) has generated much effort in terms of techniques and algorithms to address this problem. However, there is still no consensus on a suite of technology to address these challenges, progress has been extremely limited, and the formal properties of such systems are under-studied.
On the other hand, computational neuroscience (CNS) aims to discover the principles behind biological neural networks that enable the brain to support cognition, perception, and action. This project will employ the latest approaches and techniques used in the field of CNS to develop the field of XAI. Specifically, the first major goal will be to employ the methods of representational geometry and neural encoding manifolds (both proven to be effective in revealing meaningful neural relationships in previous studies) to reveal how activations of collections of artificial neurons in hidden layers are associated with the decision-making process of deep networks.
Second, the same methodology will be used to reveal novel insights from a variety of existing large-scale biological datasets. Finally, we will compare and contrast the encoding strategies of neural populations found various deep learning architectures with those observed in biological networks. A better understanding of the inner-workings of biological models could directly inform researchers on how to build novel artificial models that are more accurate, robust, and even economical during both training and inference in terms of data, time, and energy consumption.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences biological sciences neurobiology
- natural sciences mathematics pure mathematics geometry
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2024-PF-01
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40003 Segovia
Spain
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