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
How Do Our Brains Classify Similar Objects? New Theory Works Out the Details
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With a new computational model, Flatiron Institute researchers have made big steps in understanding how our brains classify. This advance could improve efficiency in artificial neural networks.
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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Upcoming Events
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11 Wed -
Workshop 5:00 - 5:00 p.m.
CCN Statistical Analysis of Neural Data Workshop
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Workshop 5:00 - 5:00 p.m.
Research Highlights
A polar prediction model for learning to represent visual transformations
All organisms make temporal predictions, and their evolutionary fitness level depends on the accuracy of these predictions. In the context…
Advances in Neural Information Processing SystemsAdaptive whitening with fast gain modulation and slow synaptic plasticity
Neurons in early sensory areas rapidly adapt to changing sensory statistics, both by normalizing the variance of their individual responses…
Advances in Neural Information Processing SystemsEfficient coding of natural images using maximum manifold capacity representations
Yilun Kuang, E. P. Simoncelli, S. Chung
The efficient coding hypothesis posits that sensory systems are adapted to the statistics of their inputs, maximizing mutual information between…
Advances in Neural Information Processing SystemsLeadership
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.