How spurious correlations affect a correlation-based measure of spike timing reliability

Freund, Jan A.; Cerquera, Alexander

doi:10.1016/j.neucom.2011.10.014

Cited by 4 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In order to evaluate the learning performance, a correlation-based measure C is set to measure the distance between the desired and actual output spike trains [49], [50]. Like Victor-Purpura distance [51], C is popular in many papers [31]- [34], [38]- [40].…”

Section: Simulation Resultsmentioning

confidence: 99%

A Supervised Learning Algorithm for Spiking Neurons Using Spike Train Kernel Based on a Unit of Pair-Spike

Chen

Wang

2020

IEEE Access

View full text Add to dashboard Cite

In recent years, neuroscientists have discovered that the neural information is encoded by spike trains with precise times. Supervised learning algorithm based on the precise times for spiking neurons becomes an important research field. Although many existing algorithms have the excellent learning ability, most of their mechanisms still have some complex computations and certain limitations. Moreover, the discontinuity of spiking process also makes it very difficult to build an efficient algorithm. This paper proposes a supervised learning algorithm for spiking neurons using the kernel function of spike trains based on a unit of pair-spike. Firstly, we comprehensively divide the intervals of spike trains. Then, we construct an optimal selection and computation method of spikes based on the unit of pair-spike. This method avoids some wrong computations and reduces the computational cost by using each effective input spike only once in every epoch. Finally, we use the kernel function defined by an inner product operator to solve the computing problem of discontinue spike process and multiple output spikes. The proposed algorithm is successfully applied to many learning tasks of spike trains, where the effect of our optimal selection and computation method is verified and the influence of learning factors such as learning kernel, learning rate, and learning epoch is analyzed. Moreover, compared with other algorithms, all experimental results show that our proposed algorithm has the higher learning accuracy and good learning efficiency.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

A Supervised Learning Algorithm for Spiking Neurons Using Spike Train Kernel Based on a Unit of Pair-Spike

Chen

Wang

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Our results, for the first time, show that neurons respond preferentially to the 1/f noise in large frequency ranges, with respect to the firing rate and spike timing reliability. Because the rate and temporal encoding are two major encoding methods for neurons (Nowak et al, 1997; Reinagel and Reid, 2000; Fellous et al, 2001; Brette and Guigon, 2003; Avissar et al, 2007; Freund and Cerquera, 2012) and neuronal activities at all levels show 1/f characteristics in vivo (Bédard et al, 2006; El Boustani et al, 2009; Freeman and Zhai, 2009; Milstein et al, 2009; Dehghani et al, 2010; He et al, 2010; Ciuciu et al, 2012; Pettersen et al, 2014; Voytek et al, 2015), our results may indicate the positive effects of the 1/f property and large frequency range on the neuronal responses for normal physiological functioning. In addition, since the 1/f type of signal substantially drives neuronal response, our findings suggest new operating rules for synaptic transmission, neuronal plasticity and other activities relying on neuronal firing.…”

Section: Discussionmentioning

confidence: 99%

“…In particular, although the signal frequency range has been confirmed to significantly affect neuronal excitability (Nozaki et al, 1999b; Higgs and Spain, 2009), the exact role of the frequency range in neuronal responsiveness to the 1/f β statistic remains unclear. As neurons generally encode information according to the rate and/or the precise timing of spikes (Nowak et al, 1997; Reinagel and Reid, 2000; Fellous et al, 2001; Brette and Guigon, 2003; Avissar et al, 2007; Freund and Cerquera, 2012), we sought to examine the neuronal firing rate and spike-timing reliability to input statistics. In this study, we focus on the effects of the signal frequency range to identify the neuronal responsiveness to 1/f β (β = 0, 1, and 2) noises with respect to the firing rate and spike-timing reliability.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Frequency Scale on the Response Sensitivity and Reliability of Cortical Neurons to 1/fβ Input Signals

Fan

2019

Front. Cell. Neurosci.

View full text Add to dashboard Cite

What type of principle features intrinsic inside of the fluctuated input signals could drive neurons with the maximal excitations is one of the crucial neural coding issues. In this article, we examined both experimentally and theoretically the cortical neuronal responsivity (including firing rate and spike timing reliability) to input signals with different intrinsic correlational statistics (e.g., white-type noise, showed 1/f 0 power spectrum, pink noise 1/f, and brown noises 1/f 2 ) and different frequency ranges. Our results revealed that the response sensitivity and reliability of cortical neurons is much higher in response to 1/f noise stimuli with long-term correlations than 1/f 0 with short-term correlations for a broad frequency range, and also higher than 1/f 2 for all frequency ranges. In addition, we found that neuronal sensitivity diverges to opposite directions for 1/f noise comparing with 1/f 0 white noise as a function of cutoff frequency of input signal. As the cutoff frequency is progressively increased from 50 to 1,000 Hz, the neuronal responsiveness increased gradually for 1/f noise, while decreased exponentially for white noise. Computational simulations of a general cortical model revealed that, neuronal sensitivity and reliability to input signal statistics was majorly dominated by fast sodium inactivation, potassium activation, and membrane time constants.

show abstract

“…Con este laboratorio, se espera fortalecer las investigaciones en el área de neurociencia computacional y análisis de código retinal, cuyas perspectivas podrían apoyar en un futuro el desarrollo de mejores sistemas basados en neuroingeniería. De igual forma, se dará continuidad a los estudios relacionados con este tema que han sido previamente abordados en trabajos de investigación anteriores [12][13].…”

Section: Discusionesunclassified

Decoding of visual stimuli from extracellular responses of a retinal ganglion cells population

Gómez

Muñoz

et al. 2013

2013 Pan American Health Care Exchanges (PAHCE)

View full text Add to dashboard Cite

The retina is a tissue placed on the posterior surface of the eye that encodes the visual information from the environment. This code is represented by action potentials or spikes transmitted along the optic nerve to the brain, whose characteristics depend on the visual stimulus that come to the retina. Currently, it is not still completely clear which coding strategies are employed by the retina to encode the visual information, although some of them have been assumed in the recent years. The present work evaluates some coding strategies to reconstruct visual stimuli corresponding to sequences of four light intensities randomly presented. This task was performed with combinations of three coding strategies: latency, first inter-spike interval and firing rate. The classification was based on linear discriminate analysis, with the aim to determine which combinations of coding strategies detect the information of the light intensities. The performance of the method depended on the coding strategies combinations and the specific light intensity to reconstruct.

show abstract

How spurious correlations affect a correlation-based measure of spike timing reliability

Cited by 4 publications

References 14 publications

A Supervised Learning Algorithm for Spiking Neurons Using Spike Train Kernel Based on a Unit of Pair-Spike

A Supervised Learning Algorithm for Spiking Neurons Using Spike Train Kernel Based on a Unit of Pair-Spike

The Impact of Frequency Scale on the Response Sensitivity and Reliability of Cortical Neurons to 1/fβ Input Signals

Decoding of visual stimuli from extracellular responses of a retinal ganglion cells population

Contact Info

Product

Resources

About