Inferring information flow in spike-train data sets using a trial-shuffle method

AbstractNeurons use sequences of action potentials (spikes) to convey information across neuronal networks. In neurophysiology experiments, information about external stimuli or behavioral tasks has been frequently characterized in term of neuronal firing rate. The firing rate is conventionally estimated by averaging spiking responses across multiple similar experiments (or trials). However, there exist a number of applications in neuroscience research that require firing rate to be estimated on a single trial basis. Estimating firing rate from a single trial is a challenging problem and current state-of-the-art methods do not perform well. To address this issue, we develop a new method for estimating firing rate based on kernel smoothing technique that considers the bandwidth as a random variable with prior distribution that is adaptively updated under a Bayesian framework. By carefully selecting the prior distribution together with Gaussian kernel function, an analytical expression can be achieved for the kernel bandwidth. We refer to the proposed method as Bayesian Adaptive Kernel Smoother (BAKS). We evaluate the performance of BAKS using synthetic spike train data generated by biologically plausible models: inhomogeneous Gamma (IG) and inhomogeneous inverse Gaussian (IIG). We also apply BAKS to real spike train data from non-human primate (NHP) motor and visual cortex. We benchmark the proposed method against the established and previously reported methods. These include: optimized kernel smoother (OKS), variable kernel smoother (VKS), local polynomial fit (Locfit), and Bayesian adaptive regression splines (BARS). Results using both synthetic and real data demonstrate that the proposed method achieves better performance compared to competing methods. This suggests that the proposed method could be useful for understanding the encoding mechanism of neurons in cognitive-related tasks. The proposed method could also potentially improve the performance of brain-machine interface (BMI) decoder that relies on estimated firing rate as the input.

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Influence and Information Flow in Online Social Networks

Research Anthology on Strategies for Using Social Media as a Service and Tool in Business ◽

10.4018/978-1-7998-9020-1.ch026 ◽

2021 ◽

pp. 502-520

Author(s):

Afrand Agah ◽

Mehran Asadi

Keyword(s):

Social Networks ◽

Information Flow ◽

Online Social Networks ◽

Large Data ◽

Marketing Strategies ◽

Divide And Conquer ◽

Data Sets ◽

Divide And Conquer Algorithm ◽

Flow Of Information

This article introduces a new method to discover the role of influential people in online social networks and presents an algorithm that recognizes influential users to reach a target in the network, in order to provide a strategic advantage for organizations to direct the scope of their digital marketing strategies. Social links among friends play an important role in dictating their behavior in online social networks, these social links determine the flow of information in form of wall posts via shares, likes, re-tweets, mentions, etc., which determines the influence of a node. This article initially identities the correlated nodes in large data sets using customized divide-and-conquer algorithm and then measures the influence of each of these nodes using a linear function. Furthermore, the empirical results show that users who have the highest influence are those whose total number of friends are closer to the total number of friends of each node divided by the total number of nodes in the network.

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Analysis of spike train data: Classification and Bayesian alignment

Electronic Journal of Statistics ◽

10.1214/14-ejs865c ◽

2014 ◽

Vol 8 (2) ◽

pp. 1786-1792 ◽

Cited By ~ 1

Author(s):

Wen Cheng ◽

Ian L. Dryden ◽

David B. Hitchcock ◽

Huiling Le

Keyword(s):

Spike Train ◽

Data Classification ◽

Spike Train Data

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Sensitivity of different stimulus-timing strategies for the detection of small excitations in noisy spike train data

Biological Cybernetics ◽

10.1007/bf00200815 ◽

1993 ◽

Vol 68 (6) ◽

pp. 553-558 ◽

Cited By ~ 6

Author(s):

Friedemann Awiszus

Keyword(s):

Spike Train ◽

Stimulus Timing ◽

Spike Train Data ◽

Timing Strategies

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Analysis of spike train data: A multivariate mixed effects model for phase and amplitude

Electronic Journal of Statistics ◽

10.1214/14-ejs865e ◽

2014 ◽

Vol 8 (2) ◽

pp. 1797-1807 ◽

Cited By ~ 2

Author(s):

Pantelis Z. Hadjipantelis ◽

John A. D. Aston ◽

Hans-Georg Müller ◽

John Moriarty

Keyword(s):

Spike Train ◽

Mixed Effects ◽

Mixed Effects Model ◽

Spike Train Data

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Dynamic reconfiguration of brain stem neural assemblies: respiratory phase-dependent synchrony versus modulation of firing rates

Journal of Neurophysiology ◽

10.1152/jn.1992.67.4.923 ◽

1992 ◽

Vol 67 (4) ◽

pp. 923-930 ◽

Cited By ~ 43

Author(s):

B. G. Lindsey ◽

Y. M. Hernandez ◽

K. F. Morris ◽

R. Shannon ◽

G. L. Gerstein

Keyword(s):

Brain Stem ◽

Temporal Variations ◽

Data Sets ◽

Firing Rates ◽

Neural Assemblies ◽

Cross Correlation Analysis ◽

Respiratory Phase ◽

Sequential Samples ◽

Spike Train Data ◽

Respiratory Phases

1. The objective of this work was to determine whether configurations of midline brain stem neural assemblies change during the respiratory cycle. 2. Spike trains of several single neurons were recorded simultaneously in anesthetized, paralyzed, bilaterally vagotomized, artificially ventilated cats. Data were analyzed with cross-correlational and gravity methods. 3. Sequential samples from each of eight groups of neurons known to contain synchronously discharging neurons exhibited temporal variations in that synchrony. 4. Gravity analysis of short (less than 200-s) samples of spike train data revealed 20 pairs of clustered particles that were not predicted from cross-correlation analysis of the parent data sets (greater than 20 min). 5. Twenty-nine groups of three to eight simultaneously monitored neurons, each with at least two synchronously discharging neurons, were analyzed for evidence of respiratory phase-dependent modulation of that coordinated activity. Spikes from successive interleaved inspiratory and expiratory intervals were analyzed separately. 6. Neurons pairs in 11 groups were more synchronous during the inspiratory interval; six groups had pairs that were more synchronous during the expiratory period. In two groups, different pairs were synchronous in different respiratory phases. In 11 of the 26 pairs that exhibited phase-dependent differences in synchrony, neither neuron had a respiratory-modulated firing rate as judged by either the cycle-triggered histogram or an analysis of variance of their firing rates. 7. Configurations of respiratory-related brain stem neural networks changed with time and the phases of breathing. Neurons with no apparent respiratory modulation of their individual firing rates collectively exhibited respiratory phase-dependent modulation of their impulse synchrony.(ABSTRACT TRUNCATED AT 250 WORDS)

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