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.
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.
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Research Highlights
A biologically plausible neural network for multi-channel Canonical Correlation Analysis
Cortical pyramidal neurons receive inputs from multiple distinct neural populations and integrate these inputs in separate dendritic compartments. We explore…
arxiv:2010.00525Interpretable Image Clustering via Diffeomorphism-Aware K-Means
Y. Bahroun, A. Sengupta, B. Aazhang, R. Baraniuk
We design an interpretable clustering algorithm aware of the nonlinear structure of image manifolds. Our approach leverages the interpretability of…
arxiv:2012.09743A biologically plausible neural network for local supervision in cortical microcircuits
The back propagation algorithm is an invaluable tool for training artificial neural networks; however, because of a weight sharing requirement,…
arxiv:2011.15031
