Emory and Georgia Tech have steadily grown the number of faculty involved in computational neuroscience over the past 15 years. The research of these faculty stretch from cellular to systems and theoretical approaches. In 1997 the two Institutions formed a joint Department of Biomedical Engineering, further strengthening the highly collaborative atmosphere between researchers on both campuses. In addition both campuses have a strong track record both in undergraduate and graduate teaching. The proposed training program in computational neuroscience aims to capitalize on these strengths by formalizing an integrated approach to class work and research on both undergraduate and graduate levels. The strong NIH and NSF funded research programs of more than 15 principal investigators identified as computational neuroscientists range from detailed cellular computer simulations of neural dynamics to engineering approaches and the quantitative study of disease mechanisms underlying important disorders such as epilepsy and Parkinson's disease using computational methods. Therefore students will be exposed to multiple levels of approaches aimed ultimately at addressing medical questions. A highly qualified and diverse applicant pool for student fellowships under this program exists on both undergraduate and graduate levels, and will bring some applicants with a primarily background in the biological sciences to integrate computational approaches into their research, and vice versa brings more computational or theoretically oriented applicants in touch with biological experimental research. The program encompasses a cohort of 6 undergraduate and 6 graduate student fellows, who will absolve a rigorous curriculum in neurobiology and mathematical and computational methods through a core sequence of required classes as well as individually chosen electives. Undergraduate fellows will be funded for a period of two years in their junior and senior years, during which they will undertake specialized class work and research in a computational neuroscience lab. Undergraduate trainees will be primarily recruited from the Emory Neuroscience and Behavioral Biology and the Georgia Tech Biomedical Engineering majors, who bring a biological and quantitative strength to the program, respectively. Over 200 students join these majors annually, and we will only take applicants with a GPA of 3.5 or better and expressing an interest in future research graduate training. The graduate students in this program will be recruited from the applicant pools for the Neuroscience and Biomedical Engineering programs, which together receive more than 120 highly qualified applications each year. A special track for fellows in computational neuroscience will be announced on the program websites, that will also link to an extensive independent website describing this program. Graduate students will be funded for the first two years of their education, and then obtain individual training grants or be funded by research grants.
The use of computational approaches is becoming more and more important in biomedical research as the myriad of fact that we collect as scientists need to be put back together to enable a synthetic understanding of functioning systems. The training program From Cells to Systems and Applications: Computational Neuroscience Training at Emory &Georgia Tech makes a significant contribution to fulfilling the need for highly trained scientists capable of integrating experimental results using computational and theoretical approaches. A new generation of scientists with an integrated training in computational, neuroscience graduating from this program will raise our level of being able to simulate and understand the complexity of brain processes underlying neurological and psychiatric diseases.
| Srivastava, Kyle H; Holmes, Caroline M; Vellema, Michiel et al. (2017) Motor control by precisely timed spike patterns. Proc Natl Acad Sci U S A 114:1171-1176 |
| Blum, Kyle P; Lamotte D'Incamps, Boris; Zytnicki, Daniel et al. (2017) Force encoding in muscle spindles during stretch of passive muscle. PLoS Comput Biol 13:e1005767 |
| Ivanova, Tamara N; Gross, Christina; Mappus, Rudolph C et al. (2017) Familiarity with a vocal category biases the compartmental expression of Arc/Arg3.1 in core auditory cortex. Learn Mem 24:612-621 |
| Amadei, Elizabeth A; Johnson, Zachary V; Jun Kwon, Yong et al. (2017) Dynamic corticostriatal activity biases social bonding in monogamous female prairie voles. Nature 546:297-301 |
| Hoffmann, Lukas A; Saravanan, Varun; Wood, Alynda N et al. (2016) Dopaminergic Contributions to Vocal Learning. J Neurosci 36:2176-89 |
| Versteeg, Chris S; Ting, Lena H; Allen, Jessica L (2016) Hip and ankle responses for reactive balance emerge from varying priorities to reduce effort and kinematic excursion: A simulation study. J Biomech 49:3230-3237 |
| Puntkattalee, M Jane; Whitmire, Clarissa J; Macklin, Alix S et al. (2016) Directional acuity of whole-body perturbations during standing balance. Gait Posture 48:77-82 |
| Shepard, Kathryn N; Lin, Frank G; Zhao, Charles L et al. (2015) Behavioral relevance helps untangle natural vocal categories in a specific subset of core auditory cortical pyramidal neurons. J Neurosci 35:2636-45 |
| Tang, Claire; Chehayeb, Diala; Srivastava, Kyle et al. (2014) Millisecond-scale motor encoding in a cortical vocal area. PLoS Biol 12:e1002018 |
| Singh, Vijay; Tchernookov, Martin; Butterfield, Rebecca et al. (2014) Director field model of the primary visual cortex for contour detection. PLoS One 9:e108991 |
Showing the most recent 10 out of 11 publications
