The crayfish slow flexor muscle is used as a prep for recording intracellularly, using sharp electrodes, while stimulating afferent fibers. Students go from dissection to studying synaptic potentials.

Having practiced the crayfish slow flexor muscle prep in lab 1, students now use it to study the difference between facilitation, potentiation, and augmentation in synaptic transmission.

Students use recordings from afferent fibers conveying responses to muscle stretch from the crayfish muscle receptor organs to study differing patterns of adaptation and response kinetics in both the tonic and phasic muscle receptors organs in the crayfish abdomen.

Using the immobilized zebrafish larva students carry out calcium imaging of neural activity in the optic tectum during responses to imposed patterns of visual input and subsequently analyze their recordings to quantitatively describe single neuron response patterns to visual patterns.

Using the visual pathway of the blowfly, students record from an identified directionally sensitive interneuron (H1) and quantitatively characterize its responsivity to several features of visual stimuli adequate to activate H1.

The abdominal ganglion of the marine snail Aplysia provides a diverse collection of large central neurons within which intracellular recordings provide quantitative measurements of several basic patterns of intrinsic and synaptically-activated neural activity.

Using both wildtype and genetically engineered strains of the fruit fly, students record the patterns of male fly song production when critical neurons in the fly song control circuit are manipulated by application of light pulses that activate only neurons expressing a light-sensitive photoprotein.

Students learn to simulate winner-take-all dynamics for decision-making and learn to numerically simulate the dynamical evolution of probability distributions of solutions (Fokker-Planck equations). Students are charged with exploring which biologically-plausible parameter changes in their Fokker-Planck model of 2-alternative-forced-choice decision-making could best explain recent experimental data on muscimol inactivation-induced changes.

Students learn to train multi-layer perceptron (or “backprop”) models, and use them to train these networks in a variety of student-chosen problems.

Using a dataset from multielectrode, multi-area recordings from monkeys performing a selective attention task, students learn to calculate power and coherence using both autoregressive modeling and modern multi-taper methods, as well as Granger causality spectra. Students analyze these data to determine whether the brain’s attention network in fronto-parietal cortex provides feedback signals to visual cortex to enhance sensory processing.

Students learn to use, and then compare, modern blind source separation techniques, including principal component analysis, non-negative matrix factorization, factor analysis, and independent component analysis, to study to what extent one can recover muscle synergies given recorded muscle activity.

Students use an interactive version of the Temporal Context Model of memory recall that allows them to adjust parameters and explore how these parameters affect the model’s predictions, as well as to compare how well these predictions compare to an experimental data set.

Students observe a real-time demonstration of fMRI data collection, and then use pre-collected data from a full memory retrieval experiment to learn how to use modern multivariate pattern classification algorithms.

Students apply modern dense-scalp EEG recordings in an experiment to assess evidence regarding conflict-related negativity. Students compare their results to existing data and models in the literature.

This course combines modeling with applied math methods including PDE, probability, stochastic ODE, dynamical systems, cells as electrical circuits, Hodgkin-Huxely equation describing spikes in single neurons & bursting neurons (e.g., breathing, heartbeat, other rhythms), propagation of action potentials, reaction-diffusion equations, Hopfield-Grossberg neural nets, leaky accumulator models, drift-diffusion models, information theoretic approaches to analysis of neural spike trains.

Covers the tools and techniques that are crucial for effective use of computation in any discipline. Topics include structured programming in compiled versus scripting languages, software management tools, debugging, profiling and optimization, and parallel programming for both shared and distributed memory systems.

An introduction to numerical analysis and numerical algorithms useful for a wide range of problems. Topics in analysis include round-off versus truncation error, stability, consistency and convergence of algorithms. Topics in algorithms include methods for linear algebra, nonlinear root finding, ODEs, and elliptic, hyperbolic, and parabolic PDEs.

This course will focus on some of the most useful approaches to this broad problem, exploring both theoretical foundations and practical applications. Students will gain experience analyzing several kinds of data, including text, images and biological data. Topics will include classification, clustering, prediction, and dimensionality reduction.

Machine learning studies automatic methods for learning to make accurate predictions or useful decisions based on past observations. This course introduces theoretical machine learning, including mathematical models of machine learning, and the design and rigorous analysis of learning algorithms.  Likely topics include: bounds on the number of random examples needed to learn; learning from non-random examples in the on-line learning model; how to boost the accuracy of a weak learning algorithm; support-vector machines; maximum-entropy modeling; portfolio selection; game theory.

The course is an introduction to the theoretical foundations of machine learning and pattern recognition. A variety of classical and recent results in machine learning and statistical pattern classification are discussed. Topics include Bayesian classification, regression, regularization, maximum margin classification, kernels, neural networks and stochastic approximation.

A fundamental goal of cognitive neuroscience is to understand how psychological functions such as attention, memory, language, and decision-making arise from computations performed by assemblies of neurons in the brain. This course will provide an introduction to the use of connectionist models (also known as neural network or parallel distributed processing models) as a tool for exploring how psychological functions are implemented in the brain, and how they go awry in patients with brain damage.

A survey of fundamental principles in neurobiology at the biophysical, cellular, and system levels. Lectures will address the basis of the action potential, synaptic transmission, sensory physiology and motor control, development of the central nervous system, synaptic plasticity, and disease states. A central theme will be the understanding of systems phenomena in terms of cellular mechanisms (can be used as a first course in neuroscience for entering graduate students in Neuroscience who are coming from a different field and are not yet ready for the core curriculum).

Contemporary approaches to the study of neural development, emphasizing genetic and molecular techniques. Topics include generation, patterning, differentiation, migration and survival of neurons and glia, axon growth and guidance, target selection, synapse formation/elimination, activity-dependent remodeling of connectivity, and the relationship between neural development and behavior. Reading will be mainly from the primary literature with textbook reading provided for background. Classroom participation is required.

Introduction to the biophysics of nerve cells and synapses, and the mathematical descriptions of neurons and neural networks. How do networks of neurons represent information, and how do they compute with it? The course will survey computational modeling and data analysis methods for neuroscience. Representation of visual information, navigation through space, short-term memory and decision-making will be some of the issues considered from a mathematical/computational viewpoint.

This course will focus on original scientific literature and class discussion with readings that center on major problems and current research in neuroscience.

Designed for students in the biological sciences, this course focuses on the application of mathematical methods to biological problems. Intended to provide a basic grounding in mathematical modeling and data analysis for students who might not have pursued further study in mathematics. Topics include differential equations, linear algebra, difference equations, and probability. Each topic will have a lecture component and computer laboratory component. Students will work extensively with the computing package Matlab. No previous computing experience necessary.

Advanced seminar that reflects current research on brain and behavior.

This course aims to introduce students to advanced statistical and machine learning methods for analyzing of neural data, with an emphasis on methods derived from regression (supervised) and latent factor (unsupervised) models. Each technique is illustrated via applications to neural datasets. The course covers methods for analyzing single and multi-neuron spike train data, calcium imaging and fMRI datasets. 

Consistent with requirements of federal training grants, this class broaches significant ethical issues that face scientists. These issues include: i) scientific integrity & misconduct; ii) mentoring; iii) peer review in grants and papers; iv) humans subjects and animals research; v) collaborations and conflicts of interest; iv) the scientist as a responsible member of society.

The premise of this seminar is that an understanding of the neural basis of behavior can be gained by examining species-typical behaviors. Each animal species has evolved neural solutions to specific problems posed to them by their environment. The course will focus primarily on forebrain mechanisms in mammals, highlighting the unique environmental problems that a species must solve and the ways in which the brains of these animals implement their solutions. Some example model systems include prey capture by bats, monogamy and aggression in voles, and eye gaze processing by primates.

Seminar designed to expose students to a modern, integrative view of animal learning phenomena from experimental psychology, through the lens of computational models and current neuroscientific knowledge. At the psychological level we will concentrate on classical and instrumental conditioning. Computationally, we will view these as exemplars of prediction learning and action selection, the pillars of reinforcement learning. Neurally, we will focus on the roles of dopamine and the basal ganglia at the systems level. Students will see how the study of animal decision making can inform us about the computations that take place in the brain.

An analysis of cellular processes and regulatory factors that underlie vertebrate brain development and the development of behavior. Topics include: neurogenesis, neuronal migration, cell death, synapse formation, dendritic differentiation, as well as the influences of neurotransmitters, hormones, trophic factors and experience on developmental processes and behavior. In addition, conditions that induce abnormal brain development, and potentially result in the development of psychopathology, will be considered.

This course focuses on clinical depression, utilizing it as a model topic for scientific discourse. This topic is ideal for this purpose because it intersects a broad range of issues. The course focuses on a neurobiological approach to this personally and societally important subject. Topics range from the molecular to the clinical.

Seminar designed to expose students to current research on the cellular and molecular basis of learning and memory, providing an up-to-date analysis of what is and is not known about the neurobiology of learning and memory. We begin with a review of the model systems used to study learning and memory, including an analysis of the translational validity of certain model systems. We then deal with different forms of plasticity (synaptic and structural) as they pertain to learning and memory during development and adulthood. Finally, we apply some of these findings to evaluate the current status of research on aging and Alzheimer's.

This course will provide an introduction for advanced students on the use of functional brain imaging in cognitive neuroscience research. The first third of the course will cover the foundations of brain imaging in neurophysiology, imaging physics, experimental design, and image analysis. The rest of the course will be an examination of innovations in experimental design and methods of analysis that have opened new areas of cognitive neuroscience to inquiry using functional brain imaging.

The brain is made up of billions of neurons, each sending and receiving signals from thousands of other neurons. This densely connected network of neurons gives rise to rich spatial and temporal dynamics. This course will investigate these dynamics. The course will present experimental results from systems-level neuroscience and then discuss the theoretical implications of these findings, particularly as they relate to higher-order, cognitive behaviors.

Neurodevelopmental disorders – autism spectrum disorder (ASD), attention deficit/hyperactivity disorder, developmental dyslexia and dyspraxia – affect approximately 1 in 6 children and present major challenges to the affected, their parents and caretakers, and the educational system. In this course, we will study the typical development of cognitive functions, related to attention, reading, social behavior, math and others, as well as their atypical trajectories observed in neurodevelopmental disorders. We will discuss the neural basis, clinical symptoms and interventional strategies of major neurodevelopmental conditions. Students will have the opportunity to engage in hands-on experience with children afflicted with dyslexia and ASD through observational studies performed in special education schools. The course is aimed at students with an interest in clinical psychology, global health, the teacher prep program, premeds and concentrators in the neuroscience program with an interest in clinical applications.  

A fundamental property of human action is its orientation toward specific desired outcomes or goals. Understanding the computations & neural mechanisms underlying this goal-directedness is a central challenge for both psychology and neuroscience. We'll review major theories characterizing the role of goals in behavior, from cognitive, social & developmental psychology, animal behavior research and artificial intelligence. Having established this conceptual context, we'll review a wide range of neuroscientific data to sketch out the neural substrates of goal-directed behavior, considering the neural basis of goal evaluation, selection, representation & pursuit.

Examination of issues in the responsible conduct of scientific research, including the definition of scientific misconduct, mentoring, authorship, peer review, grant practices, use of humans and of animals as subjects, ownership of data, and conflict of interest. Class will consist primarily of the discussion of cases. Required for all first and second year graduate students in the Department of Psychology. Open to other graduate students.