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Recall dynamics of working memory networks: Modeling, analysis, and applications

Project description

Mathematical framework for working memory networks

Human memory is a powerful mental process comprising several modules responsible for processing, learning, and recalling received stimuli. Working memory (WM) holds and processes temporary information for performing cognitive tasks. Understanding human memory functioning is central to deciphering its role in cognitive health and use in artificial intelligence (AI) developments like deep neural networks (DNN). However, the robustness of these networks when their input is perturbed is a crucial open issue. The EU-funded ReWoMeN project will develop a combined model-based and data-driven mathematical framework to understand the recall dynamics of human WM networks (ReWoMeN) for robust DNN realisation and contribute to the mechanistic understanding of the human WM.

Objective

Memory and learning are human central cognitive abilities. The importance of understanding human memory functioning is evident from its central role in our cognitive health as well as its role as the main inspiration behind developments in artificial intelligence, in particular artificial deep neural networks (DNN). Despite considerable progress in the recent years in the area of DNNs, robustness of these networks is an important open issue. In particular, noise robustness, i.e. DNNs are fragile in maintaining the correct predictions if their input is perturbed. In contrast, a healthy human’s memory system maintains performance despite perturbed inputs. This motivates us to learn from the biological neuronal networks of human memory for a more robust DNN. The human memory is composed of several modules responsible for processing, learning, and recalling the received information. Among the memory modules is the working memory (WM) which is responsible for holding and processing information in a temporary fashion and in service of higher order cognitive tasks, e.g. decision making. The short-term nature of the WM makes it a great example for designing dynamic DNNs, which are useful in safety critical applications in uncertain environments. The aim of this proposal is to build a combined model-based and data-driven mathematical framework for understanding Recall dynamics of human Working Memory Networks (ReWoMeN) for realization of a robust DNN as well as contributing to the mechanistic understanding of the human WM. ReWoMeN address three main challenges including derivation of a biologically plausible system-level model to account for the measured data of human experience of WM recalling, analysis of such a complex model for explaining and predicting WM behavior, and comparing the robustness of our WM model with a recurrent DNN in an image recognition application.

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) H2020-MSCA-IF-2020

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Coordinator

TECHNISCHE UNIVERSITEIT DELFT
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 175 572,48
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 175 572,48
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