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
New Computational Model of Real Neurons Could Lead to Better AI
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The new model developed by researchers at the Flatiron Institute proposes that biological neurons have more control over their surroundings than previously thought, something that could be replicated in the artificial neural networks used in machine learning.
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
Collaborative Work
Machine Learning at the Flatiron Institute
Machine learning has become an indispensable tool for computational science, and it is an active and growing area of research throughout the Flatiron Institute.
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- CCN
- CCA
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- CCQ
Publications
Learning normalized image densities via dual score matching
Learning probability models from data is at the heart of many machine learning endeavors, but is notoriously difficult due to…
arXiv:2506.05310Elucidating the representation of images within an unconditional diffusion model denoiser
Generative diffusion models learn probability densities over diverse image datasets by estimating the score with a neural network trained to…
arXiv:2506.01912Detecting Moving Objects During Self-motion
H. Lutwak
s we move through the world, the pattern of light projected on our eyes is complex and dynamic. Even in…
New York University ProQuest Dissertations & ThesesLeadership
Software
CaImAn Python
Computational toolbox for large scale Calcium Imaging Analysis, including movie handling, motion correction, source extraction, spike deconvolution and result visualization.
NeMoS
A statistical modeling framework for systems neuroscience. NeMos specializes in GPU-accelerated optimizations.
plenoptic
`plenoptic` is a python library for model-based stimulus synthesis.
PYthon Neural Analysis Package (Pynapple)
Pynapple is a light-weight python library for neurophysiological data analysis.
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.