Functional Reconstruction of Dyadic and Triadic Subgraphs in Spiking Neural Networks

Akin, Myles; Onderdonk, Alex; Dzakpasu, Rhonda; Guo, Yixin

doi:10.1007/978-3-319-50901-3_55

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…It is witnessed that Artificial Neural Network models have the lowest mean-square-error value compared to the statistical models (Moghaddam et al, 2011). Bayata et al (2011), Akin et al (2017), Moghaddam et al (2011), andChiou (2006) used Artificial Neural Networks to model crashes and identified crash-related factors. Chang (2005) employed an Artificial Neural Network model and a negative-binomial model to analyze crash data.…”

Section: Low Sample Mean and Small Sample Sizementioning

confidence: 99%

Crash Prediction Models and Methodological Issues

Mekonnen

Sipos

2022

Period. Polytech. Transp. Eng.

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The conducted literature review aimed to provide an overall perspective on the significant findings of past research works related to vehicle crashes and prediction models. The literature review also provided information concerning past road safety research methodology and viable statistical analysis and computing tools. Though the selection of a specific model hinges on the objective of the research and nature of the response, when compared to statistical modeling techniques, Artificial Neural Networks (ANNs), which can model complex nonlinear relationships among dependent and independent parameters, have been witnessed to be very powerful.

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Section: Low Sample Mean and Small Sample Sizementioning

confidence: 99%

Crash Prediction Models and Methodological Issues

Mekonnen

Sipos

2022

Period. Polytech. Transp. Eng.

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Emergence of Stable Functional Cliques in Developing Neural Networks

Akin

Guo

2022

Studies in Computational Intelligence

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Effects of local network topology on the functional reconstruction of spiking neural network models

2017

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The representation of information flow through structural networks, as depicted by functional networks, does not coincide exactly with the anatomical configuration of the networks. Model free correlation methods including transfer entropy (TE) and a Gaussian convolution-based correlation method (CC) detect functional networks, i.e. temporal correlations in spiking activity among neurons, and depict information flow as a graph. The influence of synaptic topology on these functional correlations is not well-understood, though nonrandom features of the resulting functional structure (e.g. small-worldedness, motifs) are believed to play a crucial role in information-processing. We apply TE and CC to simulated networks with prescribed small-world and recurrence properties to obtain functional reconstructions which we compare with the underlying synaptic structure using multiplex networks. In particular, we examine the effects of the surrounding local synaptic circuitry on functional correlations by comparing dyadic and triadic subgraphs within the structural and functional graphs in order to explain recurring patterns of information flow on the level of individual neurons. Statistical significance is demonstrated by employing randomized null models and Z-scores, and results are obtained for functional networks reconstructed across a range of correlation-threshold values. From these results, we observe that certain triadic structural subgraphs have strong influence over functional topology.

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Functional Reconstruction of Dyadic and Triadic Subgraphs in Spiking Neural Networks

Cited by 3 publications

References 14 publications

Crash Prediction Models and Methodological Issues

Crash Prediction Models and Methodological Issues

Emergence of Stable Functional Cliques in Developing Neural Networks

Effects of local network topology on the functional reconstruction of spiking neural network models

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