Descripción del proyecto
Métodos matemáticos reducen la complejidad y revelan la representación de objetos en el cerebro
Dilucidar la representación cognitiva y neuronal de los objetos constituye una cuestión fundamental de la ciencia cognitiva. Uno de los principales escollos radica en la paradoja entre cuán competentes somos para reconocer objetos y la ingentes cantidad de información que tenemos sobre cada objeto. Una solución consiste en comprender cómo se organizan las representaciones neurales y asumir que, quizá, los principios que se aplican convencionalmente a los datos sensoriales también son aplicables a la información de objetos de alto nivel. El proyecto ContentMAP, financiado con fondos europeos, está utilizando técnicas teóricas y experimentales para reducir patrones cognitivos y neuronales de alta dimensión a representaciones de baja dimensión en la superficie cortical. El modelo de codificación del proyecto debería poder predecir la «huella» neuronal de cada objeto y describir con éxito la forma en que los objetos se representan en el cerebro.
Objetivo
Our ability to recognize an object amongst many others is one of the most important features of the human mind. However, object recognition requires tremendous computational effort, as we need to solve a complex and recursive environment with ease and proficiency. This challenging feat is dependent on the implementation of an effective organization of knowledge in the brain. In ContentMAP I will put forth a novel understanding of how object knowledge is organized in the brain, by proposing that this knowledge is topographically laid out in the cortical surface according to object-related dimensions that code for different types of representational content – I will call this contentotopic mapping. To study this fine-grain topography, I will use a combination of fMRI, behavioral, and neuromodulation approaches. I will first obtain patterns of neural and cognitive similarity between objects, and from these extract object-related dimensions using a dimensionality reduction technique. I will then parametrically manipulate these dimensions with an innovative use of a visual field mapping technique, and test how functional selectivity changes across the cortical surface according to an object’s score on a target dimension. Moreover, I will test the tuning function of these contentotopic maps. Finally, to mirror the complexity of implementing a high-dimensional manifold onto a 2D cortical sheet, I will aggregate the topographies for the different dimensions into a composite map, and develop an encoding model to predict neural signatures for each object. To sum up, ContentMAP will have a dramatic impact in the cognitive sciences by describing how the stuff of concepts is represented in the brain, and providing a complete description of how fine-grain representations and functional selectivity within high-level complex processes are topographically implemented.
Ámbito científico
Palabras clave
Programa(s)
Régimen de financiación
ERC-STG - Starting GrantInstitución de acogida
3004-531 Coimbra
Portugal