Fast Gravity: An n-Squared Algorithm for Identification of Synchronous Neural Assemblies

dc.contributor.authorDayhoff, Judith E.en_US
dc.description.abstractThe 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.en_US
dc.format.extent327187 bytes
dc.relation.ispartofseriesISR; TR 1992-99en_US
dc.subjectneural networksen_US
dc.subjectdata compressionen_US
dc.subjectdistributed information processingen_US
dc.subjectinformation theoryen_US
dc.subjectneural systemsen_US
dc.subjectrobust information processingen_US
dc.subjectsignal processingen_US
dc.subjectknowledge representationen_US
dc.subjectIntelligent Servomechanismsen_US
dc.titleFast Gravity: An n-Squared Algorithm for Identification of Synchronous Neural Assembliesen_US
dc.typeTechnical Reporten_US


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