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.
Featured News
Center for Computational Neuroscience
Insect Brain Connectomes, Courtesy of AI
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Mitya Chklovskii, Jingpeng Wu and Kazunori Shinomiya from the Center for Computational Neuroscience and Pat Gunn from the Scientific Computing Core are hard at work mapping the brain of the mini-wasp — and leveraging AI to automate the process.
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.
Research
Upcoming Events
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11 Thu -
Event 4:00 - 6:00 p.m.
VJC: Robbe Goris
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Event 4:00 - 6:00 p.m.
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30 Tue -
Lecture 10:30 a.m. - 12:00 p.m.
CCN Seminar with Leo Kozachkov
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Lecture 10:30 a.m. - 12:00 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:2112.02039Leadership
Software
CaImAn Python
Computational toolbox for large scale Calcium Imaging Analysis, including movie handling, motion correction, source extraction, spike deconvolution and result visualization.
CaImAn-MATLAB
Complete Matlab pipeline for large scale calcium imaging data analysis.
Chunkflow
Modern imaging methods, such as Light, Electron, and synchrotron X-ray, have enabled 3D imaging for large samples with high resolution.
RealNeuralNetworks.jl
Due to the string-like nature of neurons and blood vessels, they could be abstracted as curved tubes with center lines and radii.