Master of Science in Neuroscience
The Masters of Science in Neuroscience at the University of Hartford is interdisciplinary in nature, providing experiences at the interface of biology, psychology, and other natural sciences. The program is designed to meet the needs of both full-time and part- time non-traditional students with most courses and laboratories offered in the evenings.
In addition, the Neuroscience Master’s Program offers two options. The thesis option requires a six-credit thesis and is designed for students who are interested in research positions or who plan to pursue doctoral training in the future. The non-thesis option requires a 3-credit research paper where a particular applied/clinical interest is integrated with coursework taken. This option is intended as an appropriate terminal degree for students pursuing advancement in non-research positions.
The MS in Neuroscience may be completed on either a full-time or part-time basis.
Contact Information for the Program
Paola Sacchetti, PhD
Adam Silver, PhD
Course Requirements for the Master of Science in Neuroscience Major
Learning Outcomes for the Master of Science in Neuroscience
- Students will display mastery of the anatomy of the nervous systems at the cellular and systems level including distribution of sensory and motor control areas both in the brain and spinal cord.
- Students will display mastery of the molecular and cellular biology of the nervous system.
- Students will display mastery of the relationship between the physiology of the nervous system and behavior under healthy and pathological situations.
- Students will display the ability to recognize, research and follow the timely issues in neuroscience as it relates to science, industry, health care and the general public.
- Students will display a mastery of oral and written communication of original scholarly biological research.
- Students in the thesis track will demonstrate a working knowledge of the scientific method including hypothesis formation, experimental design, statistical analysis of research data and interpretation.