Rough annealing by two-step clustering, with application to neuronal signals.

To accomplish analyses on the properties of neuronal populations it is mandatory that each unit activity is identified within the overall noise background and the other unit signals merged in the same trace. The problem, addressed as a clustering one, is particularly difficult as no assumption can be made on the prior data distribution. We propose an algorithm that achieves this goal by a two-phase agglomerative hierarchical clustering. First, an inflated estimation (overly) of the number of clusters is cast down and, by a maximum entropy principle (MEP) approach, is made to collapse towards an arrangement near natural ones. In the second step consecutive partitions are created by merging, two at time previously aggregated partitions, according to similarity criteria, in order to reveal a cluster solution. The procedure makes no assumptions about data distributions and guarantees high robustness with respect to noise. An application on real data out of multiple unit recordings from spinal cord neurons of mixed gas-anaesthetized rats is presented.