4,952 research outputs found
A guide to time-resolved and parameter-free measures of spike train synchrony
Measures of spike train synchrony have proven a valuable tool in both
experimental and computational neuroscience. Particularly useful are
time-resolved methods such as the ISI- and the SPIKE-distance, which have
already been applied in various bivariate and multivariate contexts. Recently,
SPIKE-Synchronization was proposed as another time-resolved synchronization
measure. It is based on Event-Synchronization and has a very intuitive
interpretation. Here, we present a detailed analysis of the mathematical
properties of these three synchronization measures. For example, we were able
to obtain analytic expressions for the expectation values of the ISI-distance
and SPIKE-Synchronization for Poisson spike trains. For the SPIKE-distance we
present an empirical formula deduced from numerical evaluations. These
expectation values are crucial for interpreting the synchronization of spike
trains measured in experiments or numerical simulations, as they represent the
point of reference for fully randomized spike trains.Comment: 8 pages, 4 figure
Neural activity classification with machine learning models trained on interspike interval series data
The flow of information through the brain is reflected by the activity
patterns of neural cells. Indeed, these firing patterns are widely used as
input data to predictive models that relate stimuli and animal behavior to the
activity of a population of neurons. However, relatively little attention was
paid to single neuron spike trains as predictors of cell or network properties
in the brain. In this work, we introduce an approach to neuronal spike train
data mining which enables effective classification and clustering of neuron
types and network activity states based on single-cell spiking patterns. This
approach is centered around applying state-of-the-art time series
classification/clustering methods to sequences of interspike intervals recorded
from single neurons. We demonstrate good performance of these methods in tasks
involving classification of neuron type (e.g. excitatory vs. inhibitory cells)
and/or neural circuit activity state (e.g. awake vs. REM sleep vs. nonREM sleep
states) on an open-access cortical spiking activity dataset
Blindfold learning of an accurate neural metric
The brain has no direct access to physical stimuli, but only to the spiking
activity evoked in sensory organs. It is unclear how the brain can structure
its representation of the world based on differences between those noisy,
correlated responses alone. Here we show how to build a distance map of
responses from the structure of the population activity of retinal ganglion
cells, allowing for the accurate discrimination of distinct visual stimuli from
the retinal response. We introduce the Temporal Restricted Boltzmann Machine to
learn the spatiotemporal structure of the population activity, and use this
model to define a distance between spike trains. We show that this metric
outperforms existing neural distances at discriminating pairs of stimuli that
are barely distinguishable. The proposed method provides a generic and
biologically plausible way to learn to associate similar stimuli based on their
spiking responses, without any other knowledge of these stimuli
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Coordinated neuronal ensembles in primary auditory cortical columns.
The synchronous activity of groups of neurons is increasingly thought to be important in cortical information processing and transmission. However, most studies of processing in the primary auditory cortex (AI) have viewed neurons as independent filters; little is known about how coordinated AI neuronal activity is expressed throughout cortical columns and how it might enhance the processing of auditory information. To address this, we recorded from populations of neurons in AI cortical columns of anesthetized rats and, using dimensionality reduction techniques, identified multiple coordinated neuronal ensembles (cNEs), which are groups of neurons with reliable synchronous activity. We show that cNEs reflect local network configurations with enhanced information encoding properties that cannot be accounted for by stimulus-driven synchronization alone. Furthermore, similar cNEs were identified in both spontaneous and evoked activity, indicating that columnar cNEs are stable functional constructs that may represent principal units of information processing in AI
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