Both in vivo  and in vitro  experimental data suggest that medium spiny neurons in striatum participate in the formation of sequentially firing cell assemblies, at a timescale relevant for the presumed involvement of basal ganglia in reinforcement learning. Computational models argue that such cell assemblies are a feature of a minimal network architecture of the striatum . This suggests that cell assemblies can be a potential candidate for representation of the 'system states' in the framework of reinforcement learning.
Spike patterns associated with cells assemblies can be identified by clustering the spectrum of zero-lag cross-correlation between all pairs of neurons in a network . Other methods based on the dimensionality reduction of the similarity matrix of the spike trains have also been used [2,4].
Here we investigate how the identification of cell assemblies is dependent on the methodology chosen, and to what extent the statistical properties of the cell assemblies make them suitable for representation of system states in the striatum during reinforcement learning.
Partially funded by the German Federal Ministry of Education and Research (BMBF 01GQ0420 to BCCN Freiburg, BMBF GW0542 Cognition and BMBF 01GW0730 Impulse Control), EU Grant 269921 (BrainScaleS), Helmholtz Alliance on Systems Biology (Germany), Neurex, the Junior Professor Program of Baden-Württemberg and the Erasmus Mundus Joint Doctoral programme EuroSPIN.