Synchrony detection in neural assemblies.

The identification of synchronously active neural assemblies in simultaneous recordings of neuron activities is an important research issue and a difficult algorithmic problem. A gravitational analysis method has been developed to detect and identify groups of neurons that tend to generate action potentials in near-synchrony from among a larger population of simultaneously recorded units. In this paper, an improved algorithm is used for the gravitational clustering method and its performance is characterized. Whereas the original algorithm ran in n3 time (n = the number of neurons), the new algorithm runs in n2 time. Neurons are represented as particles in n-space that 'gravitate' towards one another whenever near-synchronous electrical activity occurs. Ensembles of neurons that tend to fire together then become clustered together. The gravitational technique not only identifies the synchronous groups present but also shows graphically the changing activity patterns and changing synchronies.