  Cell ensembles, originally proposed by Donald Hebb in 1949, are subsets of
synchronously firing neurons and proposed to explain basic firing behavior in
the brain. Despite having been studied for many years no conclusive evidence
has been presented yet for their existence and involvement in information
processing such that their identification is still a topic of modern research,
especially since simultaneous recordings of large neuronal population have
become possible in the past three decades. These large recordings pose a
challenge for methods allowing to identify individual neurons forming cell
ensembles and their time course of activity inside the vast amounts of spikes
recorded. Related work so far focused on the identification of purely simulta-
neously firing neurons using techniques such as Principal Component Analysis.
In this paper we propose a new algorithm based on sparse convolution coding
which is also able to find ensembles with temporal structure. Application of
our algorithm to synthetically generated datasets shows that it outperforms
previous work and is able to accurately identify temporal cell ensembles even
when those contain overlapping neurons or when strong background noise is
present.
