Project description DEENESFRITPL 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. Show the project objective Hide the project objective 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. Fields of science natural sciencesbiological sciencesneurobiologycognitive neurosciencenatural sciencescomputer and information sciencesartificial intelligencecomputational intelligence Keywords Nonlinear dynamical systems Memory and cognition Deep neural networks Network control systems Programme(s) H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility Topic(s) MSCA-IF-2020 - Individual Fellowships Call for proposal H2020-MSCA-IF-2020 See other projects for this call Funding Scheme MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF) Coordinator TECHNISCHE UNIVERSITEIT DELFT Net EU contribution € 175 572,48 Address STEVINWEG 1 2628 CN Delft Netherlands See on map Region West-Nederland Zuid-Holland Delft en Westland Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 175 572,48
