Typically only offered at the doctoral degree level as a specialization in neuroscience, students can pursue a PhD or Doctor of Medicine (MD) in Computational Neuroscience. Students in these degree programs gain a well-rounded background in neuroscience and further explore the mathematical analysis of the field. Here we discuss the program more in-depth and some of the common admissions requirements.
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Information for Graduate Degree Programs in Computational Neuroscience
Doctoral degree programs in neuroscience with a concentration in computational neuroscience typically require a thesis and provide students with hands-on learning opportunities through laboratory research and coursework. Although students at this level can generally pick and choose many of their courses to align with their research interests and career goals, some of the common graduate course topics are examined below.
Courses in cellular neuroscience examine the function and structure of the nervous system at the molecular and/or cellular level. Students learn about synapse formation and neuron interactions, nerve growth, diseases of the nervous system and other topics. These courses also discuss the role that these structures and processes play in memory, behavior and learning.
These courses generally focus on how the structure and function of neurons are related to behavior in humans but may also touch on behavior in animals as well. Students study these systems of neurons from the behavioral, cognitive and physiological perspectives. These courses may explore current research in the field and may offer hands-on learning opportunities in a laboratory component where students work with live animals.
Methods in Neuroscience
Methods courses in neuroscience survey the various modeling and data analysis methods in the field and may be offered at an introductory and advanced level. These courses use computer software, mathematical models and graphical methods to examine neural activity. Specific topics students may explore include single neuron models, reverse correlation, cable theory, information theory and more.
Courses in neurodynamics examine the dynamics of neurons and the mathematics and models used to describe these dynamics. Specific topics that students explore may include the biophysical properties of neurons, spatially extended models, plasticity, phase-plane analysis and more. Some of these courses may include a lab component and require students to complete a final project.
Courses in neuronal computation are similar to courses in neurodynamics but begin looking at the properties and dynamics of networks of neurons or neuronal systems. Students study the mathematical models used to describe these dynamics and explore how these dynamics affect learning, behavior, memory and other concepts. Specific topics in these courses may include associative networks, feedforward networks, firing rate models, asynchronous vs synchronous states and more.
Common Entrance Requirements
Applicants to doctoral programs in computational neuroscience generally need to include their official transcripts, GRE scores, letters of recommendation, a personal statement and a resume or CV with their application. Some programs may also require students to include any previous research experience on their application. Specific prerequisites for these programs vary by school, but may include coursework in calculus, biology, chemistry, physics and other scientific disciplines. Given the discipline's computational emphasis, students should consider completing advanced mathematic or statistics courses to fulfill curriculum expectations. Some programs may also require applicants to hold a bachelor's degree specifically in biology, psychology or another quantitative science or engineering field.
Graduate programs in computational neuroscience are usually offered as a specialization in a neuroscience doctoral degree program. These degree programs typically require a thesis and coursework that is heavy in mathematics and neuroscience.