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We live in a rapidly changing digital world marked by technological advances, and fraught with online information constantly growing thanks to the Internet revolution and the online social applications in particular. Formal learning acquired in traditional ...
In many real world applications we do not have access to fully-labeled training data, but only to a list of possible labels. This is the case, e.g., when learning visual classifiers from images downloaded from the web, using just their text captions or tag ...
In motor learning, training a task B can disrupt improvements of performance of a previously learned task A, indicating that learning needs consolidation. An influential study suggested that this is the case also for visual perceptual learning [1]. Using t ...
How learning and memory is achieved in the brain is a central question in neuroscience. Key to today’s research into information storage in the brain is the concept of synaptic plasticity, a notion that has been heavily influenced by Hebb’s (1949) postulat ...
We generalise the problem of inverse reinforcement learning to multiple tasks, from multiple demonstrations. Each one may represent one expert trying to solve a different task, or as different experts trying to solve the same task. Our main contribution is ...
Neuroplastin-65 (Np65) is a brain-specific cell adhesion molecule belonging to the immunoglobulin superfamily. Homophilic trans-interaction of Np65 mediates adhesion between cells and modulates synaptic plasticity. This interaction solely occurs through th ...
Perceptual learning is the ability to improve perception through practice. Perceptual learning is usually specific for the task and features learned. For example, improvements in performance for a certain stimulus do not transfer if the stimulus is rotated ...
Perceptual learning is learning to perceive and is essential for all forms of perception and learning. For a long time it was believed that perceptual learning was a simple process suitable as a model for studying general mechanism of learning. Researchers ...
In the context of the development of hybrid choice models (HCM), we are interested in understanding better how to design models which can account for the latent information contained in new data types (psychometrics, data from smartphones, etc.) for which ...
In this article we introduce JULIDE, a software toolkit developed to perform the 3D reconstruction, intensity normalization, volume standardization by 3D image registration and voxel-wise statistical analysis of autoradiographs of mouse brain sections. Thi ...
