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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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
Responses of neurons in macaque V4 to object and texture images
Justin D. Lieber, T. D. Oleskiw , Laura Palmieri, E. P. Simoncelli, J. A. Movshon
Humans and monkeys can rapidly recognize objects in everyday scenes. While it is known that this ability relies on neural…
bioRxiv:2024.02.20.581273Optimized Linear Measurements for Inverse Problems using Diffusion-Based Image Generation
We examine the problem of selecting a small set of linear measurements for reconstructing high-dimensional signals. Well-established methods for optimizing…
arXiv:2405.17456Discriminating image representations with principal distortions
Image representations (artificial or biological) are often compared in terms of their global geometric structure; however, representations with similar global…
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.