scholarly journals Noise-enhanced coding in phasic neuron spike trains

PLoS ONE ◽  
2017 ◽  
Vol 12 (5) ◽  
pp. e0176963
Author(s):  
Cheng Ly ◽  
Brent Doiron
Keyword(s):  
Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 1011
Author(s):  
Simone Orcioni ◽  
Alessandra Paffi ◽  
Francesca Apollonio ◽  
Micaela Liberti

Power spectra of spike trains reveal important properties of neuronal behavior. They exhibit several peaks, whose shape and position depend on applied stimuli and intrinsic biophysical properties, such as input current density and channel noise. The position of the spectral peaks in the frequency domain is not straightforwardly predictable from statistical averages of the interspike intervals, especially when stochastic behavior prevails. In this work, we provide a model for the neuronal power spectrum, obtained from Discrete Fourier Transform and expressed as a series of expected value of sinusoidal terms. The first term of the series allows us to estimate the frequencies of the spectral peaks to a maximum error of a few Hz, and to interpret why they are not harmonics of the first peak frequency. Thus, the simple expression of the proposed power spectral density (PSD) model makes it a powerful interpretative tool of PSD shape, and also useful for neurophysiological studies aimed at extracting information on neuronal behavior from spike train spectra.


1989 ◽  
Vol 483 (2) ◽  
pp. 373-378 ◽  
Author(s):  
Bruce G. Lindsey ◽  
Roger Shannon ◽  
George L. Gerstein

1997 ◽  
Vol 9 (6) ◽  
pp. 1265-1275 ◽  
Author(s):  
Purvis Bedenbaugh ◽  
George L. Gerstein

As the technology for simultaneously recording from many brain locations becomes more available, more and more laboratories are measuring the cross-correlation between single-neuron spike trains, and between composite spike trains derived from several undiscriminated cells recorded on a single electrode (multiunit clusters). The relationship between single-unit correlations and multiunit cluster correlations has not yet been fully explored. We calculated the normalized cross-correlation (NCC) between single unit spike trains and between small clusters of units recorded in the rat somatosensory cortex. The NCC between small clusters of units was larger than the NCC between single units. To understand this result, we investigated the scaling of the NCC with the number of units in a cluster. Multiunit cross-correlation can be a more sensitive detector of neuronal relationship than single-unit cross-correlation. However, changes in multiunit cross-correlation are difficult to interpret uniquely because they depend on the number of cells recorded on each electrode and because they can arise from changes in the correlation between cells recorded on a single electrode or from changes in the correlation between cells recorded on two electrodes.


2012 ◽  
Vol 2012 ◽  
pp. 1-11
Author(s):  
Shinichi Tamura ◽  
Tomomitsu Miyoshi ◽  
Hajime Sawai ◽  
Yuko Mizuno-Matsumoto

When analyzing neuron spike trains, it is always the problem of how to set the time bin. Bin width affects much to analyzed results of such as periodicity of the spike trains. Many approaches have been proposed to determine the bin setting. However, these bins are fixed through the analysis. In this paper, we propose a randomizing method of bin width and location instead of conventional fixed bin setting. This technique is applied to analyzing periodicity of interspike interval train. Also the sensitivity of the method is presented.


2007 ◽  
Vol 98 (3) ◽  
pp. 1428-1439 ◽  
Author(s):  
Hannah M. Bayer ◽  
Brian Lau ◽  
Paul W. Glimcher

Work in behaving primates indicates that midbrain dopamine neurons encode a prediction error, the difference between an obtained reward and the reward expected. Studies of dopamine action potential timing in the alert and anesthetized rat indicate that dopamine neurons respond in tonic and phasic modes, a distinction that has been less well characterized in the primates. We used spike train models to examine the relationship between the tonic and burst modes of activity in dopamine neurons while monkeys were performing a reinforced visuo-saccadic movement task. We studied spiking activity during four task-related intervals; two of these were intervals during which no task-related events occurred, whereas two were periods marked by task-related phasic activity. We found that dopamine neuron spike trains during the intervals when no events occurred were well described as tonic. Action potentials appeared to be independent, to occur at low frequency, and to be almost equally well described by Gaussian and Poisson-like (gamma) processes. Unlike in the rat, interspike intervals as low as 20 ms were often observed during these presumptively tonic epochs. Having identified these periods of presumptively tonic activity, we were able to quantitatively define phasic modulations (both increases and decreases in activity) during the intervals in which task-related events occurred. This analysis revealed that the phasic modulations of these neurons include both bursting, as has been described previously, and pausing. Together bursts and pauses seemed to provide a continuous, although nonlinear, representation of the theoretically defined reward prediction error of reinforcement learning.


2014 ◽  
Vol 11 (4) ◽  
pp. 046004 ◽  
Author(s):  
Alexandre Iolov ◽  
Susanne Ditlevsen ◽  
André Longtin

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