Learning to Dream, Dreaming to Learn

The importance of sleep for healthy brain function is widely acknowledged. However, it remains mysterious how the sleeping brain, disconnected from the outside world and plunged into the fantastic experiences of dreams, is actively learning. A main feature of dreams is the generation of new realistic sensory experiences in absence of external input, from the combination of diverse memory elements. How do cortical networks host the generation of these sensory experiences during sleep? What function could these generated experiences serve? In this thesis, we attempt to answer these questions using an original, computational approach inspired by modern artificial intelligence. In light of existing cognitive theories and experimental data, we suggest that cortical networks implement a generative model of the sensorium that is systematically optimized during wakefulness and sleep states. By performing network simulations on datasets of natural images, our results not only propose potential mechanisms for dream generation during sleep states, but suggest that dreaming is an essential feature for learning semantic representations throughout mammalian development

Analysis and extension of hierarchical temporal memory for multivariable time series

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, junio de 201

Brain Computations and Connectivity [2nd edition]

This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics

Multimodal sensory representation for object classification via neocortically-inspired algorithm

This study reports our initial work on multimodal sensory representation for object classification. To form a sensory representation we used the spatial pooling phase of the Hierarchical Temporal Memory - a Neocortically-inspired algorithm. The classification task was carried out on the Washington RGB-D dataset in which the employed method provides extraction of non-hand engineered representations (or features) from different modalities which are pixel values (RGB) and depth (D) information. These representations, both early and lately fused, were used as inputs to a machine learning algorithm to perform object classification. The obtained results show that using multimodal representations significantly improve (by 5 %) the classification performance compared to a when a single modality is used. The results also indicate that the performed method is effective for multimodal learning and different sensory modalities are complementary for the object classification. Therefore, we envision that this method can be employed for object concept formation that requires multiple sensory information to execute cognitive tasks