Understanding the human brain is one of the greatest and most challenging scientific frontiers of our time. CCN’s mission is to develop models, principles and conceptual frameworks that deepen our knowledge of brain function — both in health and in disease.
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Center for Computational Neuroscience
Watch: Building the Ultimate Brain Map
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In efforts to construct a map of neural connections in the brain, researchers at the Flatiron Institute’s Center for Computational Neuroscience are turning to an unlikely source: the wasp.
CCN takes a “systems" neuroscience approach, building models that are motivated by fundamental principles, that are constrained by properties of neural circuits and responses, and that provide insights into perception, cognition and behavior. This cross-disciplinary approach not only leads to the design of new model-driven scientific experiments, but also encapsulates current functional descriptions of the brain that can spur the development of new engineered computational systems, especially in the realm of machine learning. CCN currently has research groups in Computational Vision and Neural Circuits and Algorithms, and will launch research groups in NeuroAI and Geometry and Statistical Analysis of Neural Data in January 2022.
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Upcoming Events
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27 Mon -
Event 1:15 - 2:30 p.m.
CCN Seminar: E.J. Chichilinsky
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Event 1:15 - 2:30 p.m.
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29 Wed -
Event 1:00 - 3:00 p.m.
LCV Lab Meeting: Eric Wu, Stanford University
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Event 1:00 - 3:00 p.m.
Research Highlights
Computational mechanisms of distributed value representations and mixed learning strategies
S. Farashahi, A. Soltani
Learning appropriate representations of the reward environment is challenging in the real world where there are many options, each with…
Nature CommunicationsNeural optimal feedback control with local learning rules
A major problem in motor control is understanding how the brain plans and executes proper movements in the face of…
NeurIPSBridging the Gap: Point Clouds for Merging Neurons in Connectomics
J. Berman, D. Chklovskii, J. Wu
In the field of Connectomics, a primary problem is that of 3D neuron segmentation. Although deep learning-based methods have achieved…
arXiv:2112.02039
