Computational Neuroscience PhD Programs

Computational neuroscience Ph.D. programs are concerned with the application of mathematical tools and theories in order to understand brain function. Here are nine courses explored in depth that computational neuroscience Ph.D. candidates may expect to take. Schools offering Anatomy & Physiology degrees can also be found in these popular choices.

Computational Neuroscience Ph.D. Admission Requirements and Courses

Admission requirements for computational neuroscience Ph.D. students may include a BA with a minimum GPA of 3.0, excellent GRE scores, letters of recommendation, and at least a year of lab research. Students enrolling in a Ph.D. computational neuroscience program should expect to spend at least 5 years engaged in academic learning. Throughout the journey of obtaining a Ph.D. in computational neuroscience, the following types of courses and a variety of disciplines such as computer science, electrical engineering, and physics may be used to understand how humans think and process information.

Ethical Conduct of Science

Ethics courses related to science often address the ethical problems that might arise from adulterating data, academic plagiarism, unethical treatment of test subjects, and other behaviors deemed unethical within the field of science. The objective of this kind of course is often to convince students of the importance of conducting ethical scientific research.

Statistics and Information Theory

Pertaining to the statistical methods of data analysis, this sort of course often addresses how to efficiently encode and decode neurons. Students should expect to learn receptive field analysis and Poisson models of neural encoding. Through statistical methods, students may also learn how to predict neural behavior. Crucial to understanding the brain is knowing how to interpret nonlinear variables. Being that so much information garnered from neuroscience experiments are multivariate, aspiring scientists must understand how to read complex information.

Behavioral Neuroscience

Despite having taken a similar class as an undergrad, Ph.D. candidates are often obliged to take a behavioral neuroscience class at the graduate level. Unlike undergraduate courses, this type of class typically explores brain activity as it relates to behaviors through adaptive and evolutionary lenses. Students may be expected to write grant proposals for novel science experiments that pertain to animal models.

Principles of Neurobiology

Principles of neurobiology typically addresses integral concepts such as cellular and molecular neurobiology. The objective of this course commonly is to illuminate students on the process of the neuron and how it interacts with animal nervous systems. Topics discussed in-depth include neurological diseases, synaptic development, and ion channels among others.

Glia and Neurons

This kind of course takes a deep dive into the molecular and cell biology of the nervous system. Topics explored may include the function of neural proteins, the cell biology of synapses, and the cellular biology of glia. Students may learn the techniques used to research each of the aforementioned topics.

Computational Strategies in Cognitive Neuroscience

A central question that computational neuroscience tries to answer is how the nervous system is related to behaviors such as object rejection, attention, action, memory, decision making, etc. In this sort of course students can discover the relationship by using psychophysical and functional imaging among other methods. Students are expected to already be familiar with mathematical and statistical research methods.

Cell Signaling

Students might explore the route a cell must take to transfer information from the cell surface to the nucleus. Also know as, signal transduction, students may learn all about how cells communicate and interact with their environment. Candidates are often tasked with poring over primary literature in order to construct scientific models that vary in degrees of complexity. Models may include ion channels and high-level vision.

Neural Control Of Movement

Seeking to enlighten students on how the brain controls movement, this sort of course often employs elements of biomechanics, optimal control, Bayesian inference, internal models, sensorimotor psychophysics, kinesiology, and muscle physiology to illustrate the brain's role in movement. Using a variety of topics can offer students a comprehensive understanding of how the brain controls sensorimotor function.

Neurons and Networks

Mathematical analysis may be applied to study the movement of neurons and their networks. Tools such as dynamical systems and stochastic processes may be used to examined course material. Other topics may include single-neuron excitability and oscillations, network synchrony, and statistical dynamics of large cell populations.

As outlined, the majority of classes offered to students seeking a Ph.D. in computational neuroscience use various research methods to explore brain function. While an eclectic mix of classes is offered, most use mathematical analysis to research the intricacies of the brain.

To continue researching, browse degree options below for course curriculum, prerequisites and financial aid information. Or, learn more about the subject by reading the related articles below:

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