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

Project aim

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  deep  neural network (DNN)  as well as contributing to the mechanistic understanding of the human WM. ReWoMeN addresses three main challenges including derivation of a biologically plausible system-level model to account for the measured data of human experience of working memory (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 a cognitive task.

Project summary

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. 

Partnering organisations

Key facts

Time frame: - 31.3.2023

Funding: EU- Marie Sklodowska Curie Individual Fellowship