This proposal is for a five-year T90/R90 training program in Computational Neuroscience at Brandeis University containing the Research Education Program component (R90), the Undergraduate Research Training Component (R90), and the Predoctoral Research Training Program (T90). The proposed program requests funding to support 6 undergraduates (R90), 4 NRSA eligible graduate students (T90), and 2 non-NRSA eligible graduate students (R90) each year. Our goal is to identify early career students and provide them the formal and informal education and training they will need to develop into the next generation of computational/quantitative neuroscientists. The long-term goals and objectives are to increase the number of scientists with strong quantitative and modeling skills in neuroscience, as this cohort will be needed to understand brain function in health and disease. The 13 training faculty has research expertise from human cognition to cellular and molecular neuroscience, so a wide range of research problems, at numerous levels of analysis are available to trainees. The training faculty were chosen because they have demonstrated commitment to the use of theoretical and computational methods to understand the nervous system in health and disease. Students will take courses in computational neuroscience, obtain skills in building models of neurons, synapses, and networks, and employ these in a variety of independent research projects. Two cohorts of prospective trainees will be targeted: 1) Individuals in degree programs in Physics, Math, and Computer Science who wish to work in neuroscience. This cohort already has significant quantitative skills, but needs training in neuroscience. 2) Individuals in degree programs in Biology, Biochemistry, Neuroscience, or Psychology who wish to learn to employ quantitative and computational methods as part of their ability to tackle important problems in neuroscience. In addition to course work and laboratory research, students and trainees will be engaged in a large number of other activities designed to enhance their speaking skills, writing skills, and ability to collaborate with other scientists. All students and trainees will receive training in the responsible conduct of science, and students and trainees from underrepresented populations will be included.
Alleviating the burden of neurological and psychiatric disorders will require a cohort of investigators who can use computational and theoretical tools to understand brain function in health and disease. This program will train undergraduates and graduate students to use quantitative modeling methods and statistics to reveal features of brain function and disease not possible without these quantitative approaches.
|Popovi?, Marjena; Stacy, Andrea K; Kang, Mihwa et al. (2018) Development of Cross-Orientation Suppression and Size Tuning and the Role of Experience. J Neurosci 38:2656-2670|
|Tang, Wenbo; Jadhav, Shantanu P (2018) Sharp-wave ripples as a signature of hippocampal-prefrontal reactivation for memory during sleep and waking states. Neurobiol Learn Mem :|
|Moore, Anna R; Richards, Sarah E; Kenny, Katelyn et al. (2018) Rem2 stabilizes intrinsic excitability and spontaneous firing in visual circuits. Elife 7:|
|Abruzzi, Katharine C; Zadina, Abigail; Luo, Weifei et al. (2017) RNA-seq analysis of Drosophila clock and non-clock neurons reveals neuron-specific cycling and novel candidate neuropeptides. PLoS Genet 13:e1006613|
|Zielinski, Mark C; Tang, Wenbo; Jadhav, Shantanu P (2017) The role of replay and theta sequences in mediating hippocampal-prefrontal interactions for memory and cognition. Hippocampus :|
|Christie, Ian K; Miller, Paul; Van Hooser, Stephen D (2017) Cortical amplification models of experience-dependent development of selective columns and response sparsification. J Neurophysiol 118:874-893|
|Keller, Arielle S; Payne, Lisa; Sekuler, Robert (2017) Characterizing the roles of alpha and theta oscillations in multisensory attention. Neuropsychologia 99:48-63|
|Tang, Wenbo; Shin, Justin D; Frank, Loren M et al. (2017) Hippocampal-Prefrontal Reactivation during Learning Is Stronger in Awake Compared with Sleep States. J Neurosci 37:11789-11805|
|Roy, Arani; Osik, Jason J; Ritter, Neil J et al. (2016) Optogenetic spatial and temporal control of cortical circuits on a columnar scale. J Neurophysiol 115:1043-62|
|Hengen, Keith B; Torrado Pacheco, Alejandro; McGregor, James N et al. (2016) Neuronal Firing Rate Homeostasis Is Inhibited by Sleep and Promoted by Wake. Cell 165:180-191|
Showing the most recent 10 out of 15 publications