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.
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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.
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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.
Projects
