Fuzzy characterization of spike synchrony in parallel spike trains

Muiño, David Picado; León, Iván Castro; Borgelt, Christian

doi:10.1007/s00500-013-1034-6

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…We call functions of this form influence maps (and the windows of the form [ t i − w /2, t i + w /2] underlying them are called influence regions ). Such functions constitute the building blocks of the synchrony model that we introduce in our companion paper [ 21 ], which is characterized by a graded notion of synchrony (which differs substantially from the intended notion of synchrony in this paper, which is bivalent): the degree of synchrony among two or more spikes is defined as the integral (i.e., area) of the intersection of their corresponding influence maps. Such degree is thus a value in the interval [0,1] (e.g., 0 if the time distance between any two spikes is greater than or equal to w and 1 if there is exact time synchrony between them).…”

Section: Statisticsmentioning

confidence: 99%

Test Statistics for the Identification of Assembly Neurons in Parallel Spike Trains

Muiño

Borgelt

2015

Computational Intelligence and Neuroscience

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In recent years numerous improvements have been made in multiple-electrode recordings (i.e., parallel spike-train recordings) and spike sorting to the extent that nowadays it is possible to monitor the activity of up to hundreds of neurons simultaneously. Due to these improvements it is now potentially possible to identify assembly activity (roughly understood as significant synchronous spiking of a group of neurons) from these recordings, which—if it can be demonstrated reliably—would significantly improve our understanding of neural activity and neural coding. However, several methodological problems remain when trying to do so and, among them, a principal one is the combinatorial explosion that one faces when considering all potential neuronal assemblies, since in principle every subset of the recorded neurons constitutes a candidate set for an assembly. We present several statistical tests to identify assembly neurons (i.e., neurons that participate in a neuronal assembly) from parallel spike trains with the aim of reducing the set of neurons to a relevant subset of them and this way ease the task of identifying neuronal assemblies in further analyses. These tests are an improvement of those introduced in the work by Berger et al. (2010) based on additional features like spike weight or pairwise overlap and on alternative ways to identify spike coincidences (e.g., by avoiding time binning, which tends to lose information).

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Section: Statisticsmentioning

confidence: 99%

Test Statistics for the Identification of Assembly Neurons in Parallel Spike Trains

Muiño

Borgelt

2015

Computational Intelligence and Neuroscience

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“…This is illustrated in Figure 2, where two of the three coincidences of the eight neurons (shown in color) cannot be detected, because they are split by badly placed time bin boundaries. These problems have been addressed with the influence map approach (see [24,7]), which bears some resemblance to the definition of a distance measure for continuous spike trains suggested in [26]. The core idea is to surround each spike time with an influence region, which specifies how imprecisely another spike may be placed, which is still to be considered as synchronous.…”

Section: Soft Pattern Miningmentioning

confidence: 99%

Soft Pattern Mining in Neuroscience

Borgelt

2013

Synergies of Soft Computing and Statistics for Intelligent Data Analysis

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While the lower-level mechanisms of neural information processing (in biological neural networks) are fairly well understood, the principles of higher-level processing remain a topic of intense debate in the neuroscience community. With many theories competing to explain how stimuli are encoded in nerve signal (spike) patterns, data analysis tools are desired by which proper tests can be carried out on recorded parallel spike trains. This paper surveys how pattern mining methods, especially soft methods that tackle the core problems of temporal imprecision and selective participation, can help to test the temporal coincidence coding hypothesis. Future challenges consist in extending these methods, in particular to the case of spatio-temporal coding.

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