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 was developed previously 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 we show an improved algorithm for the gravitational clustering method. Where 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 gives not only an identification of synchronous goroups present but also can be used for graphical display of changing activity patterns and changing synchronies among a larger population of neurons.