? ? This interdisciplinary and multi-institutional training program seeks to support the education of future researchers who can apply the tools of Mathematics, Physics and Engineering to problems of Brain Research. Traditional Neuroscience uses reductionism to formulate hypotheses and tests them experimentally, while Theroretical and Computational Neuroscience builds on information Theory, Dynamical Systems Theory, and Computer Science to create theoretical models to be tested numerically. Collaborations of Neuroscientists, individually trained in experimental and computational approaches, are not unusual on the basis of experimental data. In extension of this, we advocate a synergistic use of both approaches to control the experiment itself, and propose to train pre- and postdoctoral students accordingly. Commensurate with our escalating knowledge of neural function, the complexity of experiments to analyze both healthy and diseased brain function is ever-increasing. In this situation, it is necessary to utilize the analytic and predictive nature of Theroretical and Computational Neuroscience not only between but rather during experiments. To meet this challenge, we will recruit both pre- post-doctoral students with previous training in Mathmatics, Physics and Engineering, and associate them with dual mentors of expertise in both theoretical and experimental Neuroscience. In addition to using theoretical tools, these students will be trained in state-of-the-art experimental methods, specifically those for complex multidimensional data acquisition, processing and visualization, as these are most prominent in Advanced Imaging Techniques. We have devised a curriculum to best educate these interdisciplinary students. For predoctoral students, training will be a well-balanced combination of classroom instruction and hands-on labs. For both pre- and postdoctoral students, there will be active journal clubs, mentor-guided research with an internship in the lab of the co-mentor, and conference presentations. Our training faculty of 23 is drawn from six institutions in and around the Texas Medical Center in Houston. All faculty members are also members of the Gulf Coast Consortium for Theoretical and Computational Neuroscience, which is part of the Gulf Coast Consortia for Interdisciplinary Bioscience Research and Training. Both our spectrum of represented disciplines and existing facilities makes this an ideal site for the proposed training program. ? ? ?
| Cadwell, Cathryn R; Palasantza, Athanasia; Jiang, Xiaolong et al. (2016) Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq. Nat Biotechnol 34:199-203 |
| Jiang, Xiaolong; Shen, Shan; Cadwell, Cathryn R et al. (2015) Principles of connectivity among morphologically defined cell types in adult neocortex. Science 350:aac9462 |
| McGinley, Matthew J; Vinck, Martin; Reimer, Jacob et al. (2015) Waking State: Rapid Variations Modulate Neural and Behavioral Responses. Neuron 87:1143-1161 |
| Baum, Sarah H; Beauchamp, Michael S (2014) Greater BOLD variability in older compared with younger adults during audiovisual speech perception. PLoS One 9:e111121 |
| Reimer, Jacob; Froudarakis, Emmanouil; Cadwell, Cathryn R et al. (2014) Pupil fluctuations track fast switching of cortical states during quiet wakefulness. Neuron 84:355-62 |
| Conner, Christopher R; Chen, Gang; Pieters, Thomas A et al. (2014) Category specific spatial dissociations of parallel processes underlying visual naming. Cereb Cortex 24:2741-50 |
| Hedrick, Kathryn R; Cox, Steven J (2013) Structure-preserving model reduction of passive and quasi-active neurons. J Comput Neurosci 34:1-26 |
| Wessel, Jan R; Conner, Christopher R; Aron, Adam R et al. (2013) Chronometric electrical stimulation of right inferior frontal cortex increases motor braking. J Neurosci 33:19611-9 |
| Baum, Sarah H; Martin, Randi C; Hamilton, A Cris et al. (2012) Multisensory speech perception without the left superior temporal sulcus. Neuroimage 62:1825-32 |
| Kellems, Anthony R; Chaturantabut, Saifon; Sorensen, Danny C et al. (2010) Morphologically accurate reduced order modeling of spiking neurons. J Comput Neurosci 28:477-94 |
Showing the most recent 10 out of 13 publications
