We are requesting the continuation of a training program in theoretical neuroscience with funds to support 4 predoctoral trainees. Our goal is to train students who combine exceptional skills in mathematics, statistics, modeling and machine learning with a deep understanding of neurobiology. The complexity of neural systems and of the data that we can now obtain demands researchers with these skills if we are to realize the neuroscience community's goals of achieving a mechanistic understanding of nervous system function and making significant progress in the treatment of neural disorders and mental illness. Training will occur at the Center for Theoretical Neuroscience at Columbia University, supported by the 6 faculty at the Center, faculty visitors to the Center, and 26 other faculty from the Departments of Neuroscience, Psychology, Biology, Biochemistry, Biomedical Engineering, and Statistics at Columbia. The Theory Center provides an exceptional environment in which pre-doctoral, post-doctoral and faculty researchers interact and collaborate extensively both within the Center and with researchers in experimental laboratories. Most trainees are members of the Columbia graduate program in Neurobiology and Behavior (with a small number drawn from other graduate programs) and take the courses that satisfy the requirements of that program. The required courses are augmented by a large selection of electives, including courses in theoretical neuroscience, statistics and machine learning. The goal of this training program is twofold: 1) To produce theoreticians who combine outstanding skills in analysis and model-building with a deep understanding and sense of biological neuroscience; and 2) To train experimentalist who are skilled at applying theoretical and computational methods in their research. This will be accomplished through extensive collaborations with outstanding experimental laboratories both at Columbia and elsewhere combined with training in state-of-the art theoretical methods. Whenever possible, students will be co-advised by both a member of the Theory Center faculty and a researcher from our associated experimental faculty, and they will be given desk space both in the Theory Center and in the laboratory of their experimental co-advisor. The opportunity to be involved in the operations of an outstanding laboratory and in the activities and intellectual atmosphere of a world-class theory center provides an exceptional training experience.
The complexity of neuroscience data and the challenges of understanding and treating diseases and dysfunctions of the brain demand an unprecedented level of sophistication in data analysis and modeling. The proposed training program provides two solutions to this challenge: training theorists who combine technical expertise with a deep understanding of experimental neuroscience and of how to work with experimentalists; and training experimentalists who know how to apply theoretical methods and work with theorists. People with these combined skills will be essential for progress in neuroscience in the coming years.
| Russo, Abigail A; Bittner, Sean R; Perkins, Sean M et al. (2018) Motor Cortex Embeds Muscle-like Commands in an Untangled Population Response. Neuron 97:953-966.e8 |
| DePasquale, Brian; Cueva, Christopher J; Rajan, Kanaka et al. (2018) full-FORCE: A target-based method for training recurrent networks. PLoS One 13:e0191527 |
| Schaffer, Evan S; Stettler, Dan D; Kato, Daniel et al. (2018) Odor Perception on the Two Sides of the Brain: Consistency Despite Randomness. Neuron 98:736-742.e3 |
| Zhang, Wujie; Falkner, Annegret L; Krishna, B Suresh et al. (2017) Coupling between One-Dimensional Networks Reconciles Conflicting Dynamics in LIP and Reveals Its Recurrent Circuitry. Neuron 93:221-234 |
| Hattori, Daisuke; Aso, Yoshinori; Swartz, Kurtis J et al. (2017) Representations of Novelty and Familiarity in a Mushroom Body Compartment. Cell 169:956-969.e17 |
| International Brain Laboratory. Electronic address: churchland@cshl.edu; International Brain Laboratory (2017) An International Laboratory for Systems and Computational Neuroscience. Neuron 96:1213-1218 |
| Eichler, Katharina; Li, Feng; Litwin-Kumar, Ashok et al. (2017) The complete connectome of a learning and memory centre in an insect brain. Nature 548:175-182 |
| Caron, Sophie; Abbott, Larry F (2017) Neuroscience: Intelligence in the Honeybee Mushroom Body. Curr Biol 27:R220-R223 |
| Lindsay, Grace W; Rigotti, Mattia; Warden, Melissa R et al. (2017) Hebbian Learning in a Random Network Captures Selectivity Properties of the Prefrontal Cortex. J Neurosci 37:11021-11036 |
| Kuchibhotla, Kishore V; Gill, Jonathan V; Lindsay, Grace W et al. (2017) Parallel processing by cortical inhibition enables context-dependent behavior. Nat Neurosci 20:62-71 |
Showing the most recent 10 out of 22 publications
