Courses tagged with "Information environments" (48)

This course considers molecular control of neural specification, formation of neuronal connections, construction of neural systems, and the contributions of experience to shaping brain structure and function. Topics include: neural induction and pattern formation, cell lineage and fate determination, neuronal migration, axon guidance, synapse formation and stabilization, activity-dependent development and critical periods, development of behavior.

This course begins with a study of the role of dynamics in the general physics of the atmosphere, the consideration of the differences between modeling and approximation, and the observed large-scale phenomenology of the atmosphere. Only then are the basic equations derived in rigorous manner. The equations are then applied to important problems and methodologies in meteorology and climate, with discussions of the history of the topics where appropriate. Problems include the Hadley circulation and its role in the general circulation, atmospheric waves including gravity and Rossby waves and their interaction with the mean flow, with specific applications to the stratospheric quasi-biennial oscillation, tides, the super-rotation of Venus' atmosphere, the generation of atmospheric turbulence, and stationary waves among other problems. The quasi-geostrophic approximation is derived, and the resulting equations are used to examine the hydrodynamic stability of the circulation with applications ranging from convective adjustment to climate.

This class provides a general introduction to the diverse roles of microorganisms in natural and artificial environments. It will cover topics including: cellular architecture, energetics, and growth; evolution and gene flow; population and community dynamics; water and soil microbiology; biogeochemical cycling; and microorganisms in biodeterioration and bioremediation.

Current research on the evolution and development of cognition and affect, including intuitive physics, biology, and psychology, language, emotions, sexuality, and social relations.

Students will be exposed to all aspects of a cutting-edge technique in modern electrophysiology, in a highly structured, team oriented environment. The research projects will probe the neural mechanisms of learning and memory through tetrode array recordings coupled with patterned microstimulation. Due to the broad nature of tasks to be completed, coupled with the team oriented approach we will be employing, we are interested in students with a wide variety of laboratory experience and skill levels.

This course deals with the specific functions of neurons, the interactions of neurons in development, and the organization of neuronal ensembles to produce behavior. Topics covered include the analysis of mutations, and molecular analysis of the genes required for nervous system function. In particular, this course focuses on research work done with nematodes, fruit flies, mice, and humans.

Surveys the literature on the cognitive and neural organization of human memory and learning. Includes consideration of working memory and executive control, episodic and semantic memory, and implicit forms of memory. Emphasizes integration of cognitive theory with recent insights from functional neuroimaging (e.g., fMRI and PET).

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.

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.

Lectures, reading, and discussion of current theory and data concerning the psychology and biology of language acquisition. Emphasizes learning of syntax and morphology, together with some discussion of phonology, and especially research relating grammatical theory and learnability theory to empirical studies of children.

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.

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.

Roles of neural plasticity in learning and memory and in development of invertebrates and mammals. An in-depth critical analysis of current literature of molecular, cellular, genetic, electrophysiological, and behavioral studies. Discussion of original papers supplemented by introductory lectures.

Lectures and discussions in this course cover the clinical, behavioral, and molecular aspects of the brain aging processes in humans. Topics include the loss of memory and other cognitive abilities in normal aging, as well as neurodegenerative conditions such as Parkinson's and Alzheimer's diseases. Discussions based on readings taken from primary literature explore the current research in this field.

Numerical methods for solving problems arising in heat and mass transfer, fluid mechanics, chemical reaction engineering, and molecular simulation. Topics: numerical linear algebra, solution of nonlinear algebraic equations and ordinary differential equations, solution of partial differential equations (e.g. Navier-Stokes), numerical methods in molecular simulation (dynamics, geometry optimization). All methods are presented within the context of chemical engineering problems. Familiarity with structured programming is assumed. The examples will use MATLAB®.

Acknowledgements

The instructor would like to thank Robert Ashcraft, Sandeep Sharma, David Weingeist, and Nikolay Zaborenko for their work in preparing materials for this course site.

The applications of pattern recognition techniques to problems of machine vision is the main focus for this course. Topics covered include, an overview of problems of machine vision and pattern classification, image formation and processing, feature extraction from images, biological object recognition, bayesian decision theory, and clustering.

Probability theory captures a number of essential characteristics of human cognition, including aspects of perception, reasoning, belief revision, and learning. Expressions of degree of belief were used in language long before people began codifying the laws of probability theory. This course explores the history and debates over codifying the laws of probability, how probability theory applies to specific cognitive processes, how it relates to the human understanding of causality, and how new computational approaches to causal modeling provide a framework for understanding human probabilistic reasoning.

This class is suitable for advanced undergraduates or graduate students specializing in cognitive science, artificial intelligence, and related fields.

To provide instruction and dialog on practical ethical issues relating to the responsible conduct of human and animal research in the brain and cognitive sciences. Specific emphasis will be placed on topics relevant to young researchers including data handling, animal and human subjects, misconduct, mentoring, intellectual property, and publication.

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