Synchrony detection in neural assemblies

Abstract

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.

Functional clustering algorithm for the analysis of dynamic network data

“…We then compared the performance of the FCA to that of the gravitational method [23,24,25,26]. This method performs clustering based on the spike times of neuronal firings by mapping the neurons as particles in N-dimensional space, and allowing their positions to aggregate in time as a function of their firing patterns.…”

“…In order to successfully capture the (physical or functional) community structure of a network, a clustering algorithm should have two important properties: the ability to detect relationships between nodes in order to form clusters, and the ability to determine the specific set of clusters which optimally characterize the network structure. While some clustering methods have been designed to extract the structure directly from the dynamics of the neurons [12,23,24,25,26], most methods rely on using a similarity measure to compute distances in similarity space between neurons, and then use structural clustering methods to determine the functional groupings [27,28,29,30,31]. However, a major problem becomes identifying statistically significant community structures from spurious ones.…”

Maximum decoding abilities of temporal patterns and synchronized firings: application to auditory neurons responding to click trains and amplitude modulated white noise

“…The neurophysiological substrate from such a strong hypothesis ensues from the fact that "neurons are natural recognizers of synchrony" (Dayhoff 1994), favoring coincident presynaptic events over asynchronous ones to generate an action potential (Abeles 1982a,b;Softky and Koch 1993). As a consequence of this behavior, it also appears that synchronized firings from several neurons feeding the same postsynaptic neuron have a higher probability to activate it, leading subsequently to the propagation of synchronous spiking (Diesmann et al 1999;Kimpo et al 2003).…”

“…The gravitational clustering algorithm presents another category of analysis methods for neural assemblies (Gerstein and Aertsen 1985;Dayhoff 1994;Baker and Gerstein 2000;Lindsey and Gerstein 2006): n neurons are represented by moving particles in a n space, and those producing more synchronized firing than expected by chance see their time trajectories aggregating in this n space. The gravitational clustering algorithm has been used to investigate properties of respiratory neural assemblies (Lindsey et al 1992;Lindsey et al 2000) and detection of recurring transient configurations associated with sensory processing, motor control, and the expression of memories (Lindsey et al 1997;Arata et al 2000;Morris et al 2001;Morris et al 2003).…”