Recall dynamics of working memory networks: Modeling, analysis, and applications
The aim of this proposal is to build a combined model-based and data-driven mathematical framework for analysis and regulation of the recall dynamics of human working memory networks. Our findings will contribute to the realization of bio-inspired deep neural networks as well as the mechanistic understanding of the human WM.
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. 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. 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 analysis and regulation of the recall dynamics of human working memory networks. Our findings will contribute to the realization of bio-inspired deep neural networks as well as the mechanistic understanding of the human WM.
Time frame: 27.11.2022 - 31.3.2023
Funding: EU- Marie Sklodowska Curie Individual Fellowship
Project Website: RECAL LAB
Matin Jafarian is Assistant Professor at the Delft Center for Systems and Control (DCSC) of Delft University of Technology (since June 2020), where she has established and led the RECAL LAB (Nonlinear Network Control for Biological Memory) since 2022. In 2015, she obtained her PhD from the ENTEG Institute at the University of Groningen (the Netherlands), where she has also held a one-year postdoctoral position. She was a postdoctoral researcher at the Division of Decision and Control Systems at the KTH Royal Institute of Technology (Sweden) from 2016–2020. She obtained her B.Sc. and M.Sc. in Electrical Engineering-Control; for the latter, she was awarded a University of Twente scholarship in 2008. Her main research interest is the interplay of control theory and cognition, in particular memory. Her interdisciplinary research has been supported by the Marie-Curie Individual Fellowship in 2021, funded by the EU-Horizon 2020 Program, and a NWO open competition grant in 2022.