Improvements to the Sensitivity of Gravitational Clustering for Multiple Neuron Recordings

Authors:
Stuart N. Baker;George L. Gerstein
Affiliations:
Department of Anatomy, University of Cambridge, Cambridge, CB2 3DY, U.K.;Department of Neuroscience, University of Pennsylvania, Philadelphia, PA 19104, U.S.A.
Venue:
Neural Computation
Year:
2000

Citing 3
Cited 7

Detecting synchronous cell assemblies with limited data and overlapping assemblies

Neural Computation
Spikes: exploring the neural code

Spikes: exploring the neural code
An Accurate Measure of the Instantaneous Discharge Probability, with Application to Unitary Joint-Event Analysis

Neural Computation

Unitary events in multiple single-neuron spiking activity: II. nonstationary data

Neural Computation
The representation of neural data using visualization

Information Visualization
Analyzing Functional Connectivity Using a Network Likelihood Model of Ensemble Neural Spiking Activity

Neural Computation
Determination of Response Latency and Its Application to Normalization of Cross-Correlation Measures

Neural Computation
Validation of task-related excess of spike coincidences based on NeuroXidence

Neurocomputing
First-spike latency in the presence of spontaneous activity

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

Journal of Computational Neuroscience

Quantified Score

Hi-index	0.00

Visualization

Abstract

We outline two improvements to the technique of gravitational clustering for detection of neuronal synchrony, which are capable of improving the method's detection of weak synchrony with limited data. The advantages of the enhancements are illustrated using data with known levels of synchrony and different interspike interval distributions. The novel simulation method described can easily generate such test data. An important dependence of the sensitivity of gravitational clustering to the interspike interval distribution of the analysed spike trains is described.