Clustered Gaussian Graphical Model via Symmetric Convex Clustering
This work addresses the need for data-driven functional clustering of neurons in neuroimaging studies, which is incremental as it builds on existing convex optimization methods.
The authors tackled the problem of clustering neurons with similar connectivity profiles from neural activity data by proposing a clustered Gaussian graphical model with a symmetric convex clustering penalty, and demonstrated its effectiveness on synthetic and real-world neuroscientific data.
Knowledge of functional groupings of neurons can shed light on structures of neural circuits and is valuable in many types of neuroimaging studies. However, accurately determining which neurons carry out similar neurological tasks via controlled experiments is both labor-intensive and prohibitively expensive on a large scale. Thus, it is of great interest to cluster neurons that have similar connectivity profiles into functionally coherent groups in a data-driven manner. In this work, we propose the clustered Gaussian graphical model (GGM) and a novel symmetric convex clustering penalty in an unified convex optimization framework for inferring functional clusters among neurons from neural activity data. A parallelizable multi-block Alternating Direction Method of Multipliers (ADMM) algorithm is used to solve the corresponding convex optimization problem. In addition, we establish convergence guarantees for the proposed ADMM algorithm. Experimental results on both synthetic data and real-world neuroscientific data demonstrate the effectiveness of our approach.