Place:Room Ew-305, E Block,IIS, The University of Tokyo 
Speaker:Dr. Gordon Pipa (Group leader at the Max-Planck for Brain Research
Junior fellow at the Frankfurt Institute for Advanced Studies, Research
fellow at MIT and MGHj
title:Detecting Synchronized spiking activity: Theoretical and practical

 It is commonly held that neurons encode information by modulations of
 their discharge rate. A complementary hypothesis is, that information
 is also encoded in the precise relation between the discharges of
 spatially distributed neurons. These complementary views are addressed
 in the literature as the rate coding and the temporal coding
 hypothesis. Multiple methods have been developed to detect temporal
 relations between spiking events and to investigate whether these
 relations that are forming a spike pattern are correlated with
 stimulus configurations, behavior, or particular states of neuronal
 systems. The methods differ in the definitions of the spike patterns,
 the techniques to detect these patterns, and the approaches to analyze
 the resulting data (descriptive, statistical hypothesis testing,
 maximum likelihood, and Bayesian approaches). Even though the temporal
 coding hypothesis formulates precisely what constitutes a spike
 pattern, it turns out to be a non-trivial problem to design a method
 that detects the existence of such pattern, and investigates their
 information content, without being confounded by other properties of
 the data.

 Here I am going to present new a non-parametric and
 computationally-efficient method named NeuroXidence (see that detects coordinated firing within a group
 of two or more neurons and tests whether the observed level of
 coordinated firing is significantly different from that expected by
 chance. NeuroXidence (1) considers the full auto-structure of the
 data, including the changes in the rate responses and the history
 dependencies in the spiking activity. We demonstrate that NeuroXidence
 can identify epochs with significant spike synchronisation even if
 these coincide with strong and fast rate modulations. We also show,
 that the method accounts for trial-by-trial variability in the rate
 responses and their latencies, and that it can be applied to short
 data windows lasting only tens of milliseconds. Based on simulated
 data we compare the performance of NeuroXidence with the UE-method and
 the cross-correlation analysis.

 In this talk, I will cover theoretical background, practical
 guidelines, and hands on demonstrations of the tool NeuroXidence. The
 Matlab Toolbox NeuroXidence (see will be