Fano Factor: A Potentially Useful Information

The Fano factor, defined as the variance-to-mean ratio of spike counts in a time window, is often used to measure the variability of neuronal spike trains. However, despite its transparent definition, careless use of the Fano factor can easily lead to distorted or even wrong results. One of the problems is the unclear dependence of the Fano factor on the spiking rate, which is often neglected or handled insufficiently. In this paper we aim to explore this problem in more detail and to study the possible solution, which is to evaluate the Fano factor in the operational time. We use equilibrium renewal and Markov renewal processes as spike train models to describe the method in detail, and we provide an illustration on experimental data.

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Joint Probability-Based Neuronal Spike Train Classification

Computational and Mathematical Methods in Medicine ◽

10.1080/17486700802448615 ◽

2009 ◽

Vol 10 (3) ◽

pp. 229-239 ◽

Cited By ~ 2

Author(s):

Yan Chen ◽

Vitaliy Marchenko ◽

Robert F. Rogers

Keyword(s):

Spike Train ◽

Classification Accuracy ◽

Joint Probability ◽

Spike Trains ◽

Observation Time ◽

Lung Inflation ◽

Neuronal Spike ◽

Constant Rate ◽

Neuronal Spike Trains ◽

Different Response

Neuronal spike trains are used by the nervous system to encode and transmit information. Euclidean distance-based methods (EDBMs) have been applied to quantify the similarity between temporally-discretized spike trains and model responses. In this study, using the same discretization procedure, we developed and applied a joint probability-based method (JPBM) to classify individual spike trains of slowly adapting pulmonary stretch receptors (SARs). The activity of individual SARs was recorded in anaesthetized, paralysed adult male rabbits, which were artificially-ventilated at constant rate and one of three different volumes. Two-thirds of the responses to the 600 stimuli presented at each volume were used to construct three response models (one for each stimulus volume) consisting of a series of time bins, each with spike probabilities. The remaining one-third of the responses where used as test responses to be classified into one of the three model responses. This was done by computing the joint probability of observing the same series of events (spikes or no spikes, dictated by the test response) in a given model and determining which probability of the three was highest. The JPBM generally produced better classification accuracy than the EDBM, and both performed well above chance. Both methods were similarly affected by variations in discretization parameters, response epoch duration, and two different response alignment strategies. Increasing bin widths increased classification accuracy, which also improved with increased observation time, but primarily during periods of increasing lung inflation. Thus, the JPBM is a simple and effective method performing spike train classification.

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Notes on renewal processes and neuronal spike trains

Mathematical Biosciences ◽

10.1016/0025-5564(71)90071-x ◽

1971 ◽

Vol 12 (1-2) ◽

pp. 33-39 ◽

Cited By ~ 2

Author(s):

Shunji Osaki

Keyword(s):

Spike Trains ◽

Renewal Processes ◽

Neuronal Spike ◽

Neuronal Spike Trains

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Evolutionary Modularity Optimization Clustering of Neuronal Spike Trains

Neural Information Processing - Lecture Notes in Computer Science ◽

10.1007/978-3-319-70093-9_55 ◽

2017 ◽

pp. 525-532

Author(s):

Chaojie Yu ◽

Yuquan Zhu ◽

Yuqing Song ◽

Hu Lu

Keyword(s):

Spike Trains ◽

Neuronal Spike ◽

Neuronal Spike Trains

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A Computational Model as Neurodecoder Based on Synchronous Oscillation in the Visual Cortex

Neural Computation ◽

10.1162/089976603322362419 ◽

2003 ◽

Vol 15 (10) ◽

pp. 2399-2418 ◽

Cited By ~ 5

Author(s):

Zhao Songnian ◽

Xiong Xiaoyun ◽

Yao Guozheng ◽

Fu Zhi

Keyword(s):

Visual Cortex ◽

Computational Model ◽

Visual Information ◽

Spike Trains ◽

Spike Timing ◽

Contour Detection ◽

System Level ◽

Neuronal Spike ◽

Neuronal Spike Trains ◽

External Stimuli

Based on synchronized responses of neuronal populations in the visual cortex to external stimuli, we proposed a computational model consisting primarily of a neuronal phase-locked loop (NPLL) and multiscaled operator. The former reveals the function of synchronous oscillations in the visual cortex. Regardless of which of these patterns of the spike trains may be an average firing-rate code, a spike-timing code, or a rate-time code, the NPLL can decode original visual information from neuronal spike trains modulated with patterns of external stimuli, because a voltage-controlled oscillator (VCO), which is included in the NPLL, can precisely track neuronal spike trains and instantaneous variations, that is, VCO can make a copy of an external stimulus pattern. The latter, however, describes multi-scaled properties of visual information processing, but not merely edge and contour detection. In this study, in which we combined NPLL with a multiscaled operator and maximum likelihood estimation, we proved that the model, as a neurodecoder, implements optimum algorithm decoding visual information from neuronal spike trains at the system level. At the same time, the model also obtains increasingly important supports, which come from a series of experimental results of neurobiology on stimulus-specific neuronal oscillations or synchronized responses of the neuronal population in the visual cortex. In addition, the problem of how to describe visual acuity and multiresolution of vision by wavelet transform is also discussed. The results indicate that the model provides a deeper understanding of the role of synchronized responses in decoding visual information.

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