Efficient Identification of Assembly Neurons within Massively Parallel Spike Trains
نویسندگان
چکیده
منابع مشابه
Efficient Identification of Assembly Neurons within Massively Parallel Spike Trains
The chance of detecting assembly activity is expected to increase if the spiking activities of large numbers of neurons are recorded simultaneously. Although such massively parallel recordings are now becoming available, methods able to analyze such data for spike correlation are still rare, as a combinatorial explosion often makes it infeasible to extend methods developed for smaller data sets...
متن کاملTest Statistics for the Identification of Assembly Neurons in Parallel Spike Trains
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...
متن کاملASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains
With the ability to observe the activity from large numbers of neurons simultaneously using modern recording technologies, the chance to identify sub-networks involved in coordinated processing increases. Sequences of synchronous spike events (SSEs) constitute one type of such coordinated spiking that propagates activity in a temporally precise manner. The synfire chain was proposed as one pote...
متن کاملIdentification of bursts in spike trains.
A computer algorithm to identify 'bursts' in trains of spikes is described. The algorithm works by constructing a histogram of interspike intervals, then analyzing the histogram to detect the critical interval value in the distribution that represents the break between short intervals within a burst and the longer intervals between bursts. When such a value is found, it is used as the 'threshol...
متن کاملEfficient methods for sampling spike trains in networks of coupled neurons
Monte Carlo approaches have recently been proposed to quantify connectivity in neuronal networks. The key problem is to sample from the conditional distribution of a single neuronal spike train, given the activity of the other neurons in the network. Dependencies between neurons are usually relatively weak; however, temporal dependencies within the spike train of a single neuron are typically s...
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ژورنال
عنوان ژورنال: Computational Intelligence and Neuroscience
سال: 2010
ISSN: 1687-5265,1687-5273
DOI: 10.1155/2010/439648