Courses tagged with "Interest and debt" (74)

This course is for upper-level graduate students who are planning careers in computational neuroscience. This course focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. It develops basic tools such as Regularization including Support Vector Machines for regression and classification. It derives generalization bounds using both stability and VC theory. It also discusses topics such as boosting and feature selection and examines applications in several areas: Computer Vision, Computer Graphics, Text Classification, and Bioinformatics. The final projects, hands-on applications, and exercises are designed to illustrate the rapidly increasing practical uses of the techniques described throughout the course.

The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory. It starts with a review of classical statistical techniques, including Regularization Theory in RKHS for multivariate function approximation from sparse data. Next, VC theory is discussed in detail and used to justify classification and regression techniques such as Regularization Networks and Support Vector Machines. Selected topics such as boosting, feature selection and multiclass classification will complete the theory part of the course. During the course we will examine applications of several learning techniques in areas such as computer vision, computer graphics, database search and time-series analysis and prediction. We will briefly discuss implications of learning theories for how the brain may learn from experience, focusing on the neurobiology of object recognition. We plan to emphasize hands-on applications and exercises, paralleling the rapidly increasing practical uses of the techniques described in the subject.

This course is a seminar in real-time language comprehension. It considers models of sentence and discourse comprehension from the linguistic, psychology, and artificial intelligence literature, including symbolic and connectionist models. Topics include ambiguity resolution and linguistic complexity; the use of lexical, syntactic, semantic, pragmatic, contextual and prosodic information in language comprehension; the relationship between the computational resources available in working memory and the language processing mechanism; and the psychological reality of linguistic representations.

This course explores the organization of synaptic connectivity as the basis of neural computation and learning. Perceptrons and dynamical theories of recurrent networks including amplifiers, attractors, and hybrid computation are covered. Additional topics include backpropagation and Hebbian learning, as well as models of perception, motor control, memory, and neural development.

This course takes a 'back to the beginning' view that aims to better understand the end result. What might be the developmental processes that lead to the organization of 'booming, buzzing confusions' into coherent visual objects? This course examines key experimental results and computational proposals pertinent to the discovery of objects in complex visual inputs. The structure of the course is designed to get students to learn and to focus on the genre of study as a whole; to get a feel for how science is done in this field.

The course will start with an overview of the central and peripheral nervous systems (CNS and PNS), the development of their structure and major divisions. The major functional components of the CNS will then be reviewed individually. Topography, functional distribution of nerve cell bodies, ascending and descending tracts in the spinal cord. Brainstem organization and functional components, including cranial nerve nuclei, ascending / descending pathways, amine-containing cells, structure and information flow in the cerebellar and vestibular systems. Distribution of the cranial nerves, resolution of their skeletal and branchial arch components. Functional divisions of the Diencephalon and Telencephalon. The course will then continue with how these various CNS pieces and parts work together. Motor systems, motor neurons and motor units, medial and lateral pathways, cortical versus cerebellar systems and their functional integration. The sensory systems, visual, auditory and somatosensory. Olfaction will be covered in the context of the limbic system, which will also include autonomic control and the Papez circuit. To conclude, functional organization and information flow in the neocortex will be discussed.

An opportunity for graduate study of advanced subjects in Brain and Cognitive Sciences not included in other subject listings. The key topics covered in this course are Bipolar Disorder, Psychosis, Schizophrenia, Genetics of Psychiatric Disorder, DISC1, Ca++ Signaling, Neurogenesis and Depression, Lithium and GSK3 Hypothesis, Behavioral Assays, CREB in Addiction and Depressive Behaviors, The GABA System-I, The GABA System-II, The Glutamate Hypothesis of Schizophrenia, The Dopamine Pathway and DARPP32.

This series of research talks by members of the Department of Brain and Cognitive Sciences introduces students to different approaches to the study of the brain and mind.

Topics include:

• From Neurons to Neural Networks
• Prefrontal Cortex and the Neural Basis of Cognitive Control
• Hippocampal Memory Formation and the Role of Sleep
• The Formation of Internal Modes for Learning Motor Skills
• Look and See: How the Brain Selects Objects and Directs the Eyes
• How the Brain Wires Itself