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.
Brain Computations and Connectivity is about how the brain works and elucidates what is computed in different brain systems and describes current biologically plausible computational approaches and models of how each of these brain systems computes.
Recenzijos
This neuronal network approach stands in contrast to connectionist approaches and also focuses exclusively on higher primate and human modeling. Helpful chapter highlights and several practical appendixes are provided, and the bibliography is excellent. * H. Storl, Augustana College (IL), CHOICE * This "bottom-up" approach to data-driven neuroscientific discovery serves as the perfect primer for those who study brain sciences, cognitive sciences, artificial intelligence, neuro-engineering, neuropsychology, and empirically oriented philosophy. * H. Storl, CHOICE * Brain Computations is the first complete attempt to summarize our current knowledge about computation in the brain, at a level a graduate can understand. ... This is a biologically grounded, full systems neuroscience textbook-which makes it one of a kind. ... Hippocampal memories, action selection in the striatum, orbitofrontal reward representations, emotion in the limbic system, cerebellar motor control, parietal spatial coordinate transforms, place fields, and posterior visual object recognition-all these can emerge from relatively simple rules. This is Rolls' unspoken but substantial grand unifying theory. (full review https://doi.org/10.1093/brain/awab477) * Brain * He concludes with 13 principles about how information in encoded in neural networks. This is almost the Holy Grail of neuroscience, the language of neurons, what makes us what we are. Yet, these ideas are presented in a simple unassuming scientific language ... * Nikolaos C. Aggelopoulos, Neurosurgery *
1. Introduction2. The ventral visual system3. The dorsal visual system4. The taste and flavor system5. The olfactory system6. The somatosensory system7. The auditory system8. The temporal cortex9. The hippocampus, memory, and spatial function10. The parietal cortex, spatial functions, and navigation11. The orbitofrontal cortex, amygdala, reward value, emotion, and decision-making12. The cingulate cortex13. The prefrontal cortex14. Language and syntax in the brain15. The motor cortical areas16. The basal ganglia17. Cerebellar cortex18. Cortical attractor dynamics and connectivity, stochasticity, psychiatric disorders, and aging19. Computations by different types of brain, and by artificial neural systemsAppendix A: Introduction to linear algebra for neural networksAppendix B: Neuronal network modelsAppendix C: Neuronal encoding, and information theoryAppendix D: Simulation software for neuronal networks, and information analysis of neuronal encodingBibliographyIndex
Professor Edmund T. Rolls performs full-time research at the Oxford Centre for Computational Neuroscience, and at the University of Warwick, and has performed research and teaching for many years as Professor of Experimental Psychology at the University of Oxford, and as Fellow and Tutor of Corpus Christi College, Oxford. His research links computational neuroscience to neurophysiological, human functional neuroimaging and neuropsychological studies in order to provide a fundamental basis for understanding how the brain operates in health and in disease.
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