Descripción del proyecto
Un marco matemático para las redes de memoria funcional
La memoria humana es un proceso mental potente que comprende diferentes módulos que procesan, aprenden y recuerdan los estímulos recibidos. La memoria funcional (WM, por sus siglas en inglés) almacena y procesa información temporal para realizar tareas cognitivas. Comprender cómo funciona la memoria humana es esencial para dilucidar su papel en la salud cognitiva y para su uso en aplicaciones de inteligencia artificial como, por ejemplo, las redes neuronales profundas (RNP). Con todo, aún se desconoce la solidez de estas redes cuando se alteran sus aferencias. En el proyecto ReWoMeN, financiado con fondos europeos, se desarrollará un marco matemático combinado basado en modelos y datos a fin de comprender la dinámica de evocación de las redes de WM humana (ReWoMeN), que permite un funcionamiento preciso de las RNP, y contribuir a la comprensión mecanicista de la WM humana.
Objetivo
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.
Ámbito científico
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Régimen de financiación
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinador
2628 CN Delft
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