Graduate Courses in the PhD Program
(Please be advised due to many variables courses are subject to change; courses may be removed or added on a yearly basis)
Introductory survey course of research in complex systems and brain sciences at Florida Atlantic University, aimed at first semester graduate students.
This course provides an introduction to nonlinear dynamical system theory. Topics of discussion are one-, two- and higher dimensional flows, oscillator theory, maps, attractors, bifurcations, chaos. Many examples will be presented from the areas of current research in physics, chemistry, biology and neuroscience. Prospective students will be expected to have an elementary knowledge of calculus and a passing familiarity with ordinary differential equations (ODEs). Homework problems will be selectively discussed in class, occasionally as a presentation by the students.
Experimental design and statistical analysis of linear and nonlinear systems. Presents the classical statistical analysis and inference of linear systems that have a small number of noninteracting pieces and how those statistical methods and analysis procedures are different for nonlinear complex systems with many pieces that interact strongly with each other, such as fractals and chaos.
This course is intended for Graduate Students and is the first of a two-part dequence (6 credits total) which covers in depth the principles of neural science, including structure and function of cells in the nervous system, neurotransmitter systems, functional neuroanatomy, sensory processes, higher brain function, neural development and cellular mechanisms of learning and memory. Senior Undergraduates interested in taking this course should consult the instructors.
This course continues on from Neuroscience I with Functional Neuroanatomy. The Senses -- Vision, Hearing, Somatosensory, Smell and Taste are covered in this course. The course concludes with lectures on Neural Development and the Cellular Mechanisms of Learning and Memory. If time permits, a few special topics such as Neuroimmunology and Neural Networks will be given by the instructor or guest lecturers. Neuroscience I is a pre-requisite for this course.
An interdisciplinary survey of the neural basis of cognitive functions such as perception, attention, memory and language.
A survey of the field of mathematical biology, including dominance of the slow manifolds. A discussion of aspects of brainstem anatomy and physiology pertinent to behavior. Class work is supplemented by extensive lab work where students learn techniques such as electroencephalographic recording from freely moving animals, horseradish peroxidase (HRP) injection and staining and mounting of tissue for light microscopy.
A study of neural function, cortical electrogenesis, and theories of origin of the EEG, covering practical issues relating to EEG recording and basic methods of EEG analysis. Autocovariance, cross-covariance, Fourier analysis, autoregressive modeling, and digital filtering are some of the techniques covered.
An introduction to Synergetics, the theory of self-organizing structures, will be given along examples from biology, chemistry and physics. Deterministic and stochastic models will be studied, e.g. Laser equations, Navier-Stokes model, Ginzburg-Landau systems, Fokker-Planck theory, master equation. Prerequisite: Introduction to Nonlinear Dynamics and Chaos.
A study of the development of theoretical models of perception with an emphasis on human information-processing capacities.
A survey of various tools and methods in machine perception. Students will find data and neural networks to perform tasks with that data. This will include coding in MATLAB, PYTHON, and the use of other technological tools such as Docker, and Jupyter Notebook.
Covers a variety of imaging techniques in Neuroscience.
ISC 6930 / PSB 6930