Computational, Systems and Developmental Neuroscience
Novel algorithms for data analysis
To help us understand our data we often develop new computational methods. Some recent examples are as follows. We also recently reviewed computational methods for analyzing behavioral data here.
- Calcium Imaging Latent Variable Analysis (CILVA). In two recent papers here and here we developed a statistical model of the interaction between spontaneous and evoked activity in calcium imaging data, based on the idea that spontaneous activity can often be accurately represented by a small number of latent (unobserved) factors. Using statistical inference procedures we simultaneously derived from the data both the structure of these latent factors and neural receptive fields. This allows us to decouple spontaneous from evoked activity, providing the opportunity to study each in relative isolation.
- Detecting neural assemblies in calcium imaging data. Activity in populations of neurons often takes the form of assemblies, where specific groups of neurons tend to activate at the same time. However, in calcium imaging data, reliably identifying these assemblies is a challenging problem. In a recent paper in BMC Biology we compared several recently proposed assembly-detection algorithms, and demonstrated leading performance for a graph-clustering algorithm that we adapted for this problem.