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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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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Publications
Partial Soft-Matching Distance for Neural Representational Comparison with Partial Unit Correspondence
Chaitanya Kapoor, A. Williams, Meenakshi Khosla
Representational similarity metrics typically force all units to be matched, making them susceptible to noise and outliers common in neural…
International Conference on Learning Representations (ICLR)Estimating Dimensionality of Neural Representations from Finite Samples
C. Chun, A. Canatar, S. Chung , Daniel Lee
The global dimensionality of a neural representation manifold provides rich insight into the computational process underlying both artificial and biological…
International Conference on Learning Representations (ICLR)Quasi Monte Carlo methods enable extremely low-dimensional deep generative models
Miles Martinez, A. Williams
This paper introduces quasi-Monte Carlo latent variable models (QLVMs): a class of deep generative models that are specialized for finding…
International Conference on Learning Representations (ICLR)Leadership
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.