Relevant bibliographies by topics / Spike-inference analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Spike-inference analysis'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Spike-inference analysis"

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Chen, Zhe. "An Overview of Bayesian Methods for Neural Spike Train Analysis." Computational Intelligence and Neuroscience 2013 (2013): 1–17. http://dx.doi.org/10.1155/2013/251905.

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Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian infere
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Harrison, Matthew T., Asohan Amarasingham, and Wilson Truccolo. "Spatiotemporal Conditional Inference and Hypothesis Tests for Neural Ensemble Spiking Precision." Neural Computation 27, no. 1 (2015): 104–50. http://dx.doi.org/10.1162/neco_a_00681.

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The collective dynamics of neural ensembles create complex spike patterns with many spatial and temporal scales. Understanding the statistical structure of these patterns can help resolve fundamental questions about neural computation and neural dynamics. Spatiotemporal conditional inference (STCI) is introduced here as a semiparametric statistical framework for investigating the nature of precise spiking patterns from collections of neurons that is robust to arbitrarily complex and nonstationary coarse spiking dynamics. The main idea is to focus statistical modeling and inference not on the f
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Zhang, Jingyue. "Signal processing and data analysis for GCaMP filtering and OASIS algorithm." Theoretical and Natural Science 73, no. 1 (2025): 172–76. https://doi.org/10.54254/2753-8818/2024.19391.

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This paper presents a comprehensive exploration of Calcium Imaging, a robust technique in neuroscience for monitoring neuronal activity. Utilizing GCaMP fluorescence indicators, the study focuses on the mouse primary visual cortex, aiming to decipher various cellular processes. The research highlights the significance of calcium ions in cellular processes and introduces the GCaMP indicator and elucidates the data preprocessing technique involving high-pass filtering and Fourier transformation, as well as the employment of the Online Active Set method to Infer Spike (OASIS) for spike inference.
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Ramírez-Mendoza, Abigail María Elena, Wen Yu, and Xiaoou Li. "A New Spike Membership Function for the Recognition and Processing of Spatiotemporal Spike Patterns: Syllable-Based Speech Recognition Application." Mathematics 11, no. 11 (2023): 2525. http://dx.doi.org/10.3390/math11112525.

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This paper introduces a new spike activation function (SPKAF) or spike membership function for fuzzy adaptive neurons (FAN), developed for decoding spatiotemporal information with spikes, optimizing digital signal processing. A solution with the adaptive network-based fuzzy inference system (ANFIS) method is proposed and compared with that of the FAN-SPKAF model, obtaining very precise simulation results. Stability analysis of systems models is presented. An application to voice recognition using solfeggio syllables in Spanish is performed experimentally, comparing the methods of FAN-step acti
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Du, Sizhen, Guojie Song, Lei Han, and Haikun Hong. "Temporal Causal Inference with Time Lag." Neural Computation 30, no. 1 (2018): 271–91. http://dx.doi.org/10.1162/neco_a_01028.

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Accurate causal inference among time series helps to better understand the interactive scheme behind the temporal variables. For time series analysis, an unavoidable issue is the existence of time lag among different temporal variables. That is, past evidence would take some time to cause a future effect instead of an immediate response. To model this process, existing approaches commonly adopt a prefixed time window to define the lag. However, in many real-world applications, this parameter may vary among different time series, and it is hard to be predefined with a fixed value. In this lette
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Qiu, Zhaomei, Fei Wang, Tingting Li, et al. "LGWheatNet: A Lightweight Wheat Spike Detection Model Based on Multi-Scale Information Fusion." Plants 14, no. 7 (2025): 1098. https://doi.org/10.3390/plants14071098.

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Wheat spike detection holds significant importance for agricultural production as it enhances the efficiency of crop management and the precision of operations. This study aims to improve the accuracy and efficiency of wheat spike detection, enabling efficient crop monitoring under resource-constrained conditions. To this end, a wheat spike dataset encompassing multiple growth stages was constructed, leveraging the advantages of MobileNet and ShuffleNet to design a novel network module, SeCUIB. Building on this foundation, a new wheat spike detection network, LGWheatNet, was proposed by integr
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Perez, Jean Claude, Valère Lounnas, and Montagnier Montagnier. "THE OMICRON VARIANT BREAKS THE EVOLUTIONARY LINEAGE OF SARS-COV2 VARIANTS." International Journal of Research -GRANTHAALAYAH 9, no. 12 (2021): 108–32. http://dx.doi.org/10.29121/granthaalayah.v9.i12.2021.4418.

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We analyze here 7 very first strains of OMICRON the SARS-CoV2 new variant from South Africa, the USA (California and Minesota), Canada and Belgium. We applied, at the scale of the whole genome and the spike gene, the biomathematics method of Fibonacci meta-structure fractal analysis applied to the UA / CG proportions. We have evidenced the RUPTURE of OMICRON with respect to ALL the previous variants: D614G, ALPHA, BETA, GAMMA, DELTA. Remarkably, it is observed that the density of OMICRON mutations in the SPIKE PRION region is more than 8 times that of the rest of the Spike protein.
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Osborn, C. E., and M. D. Binder. "Correlation analysis of muscle receptor discharge during active contractions of the cat medial gastrocnemius muscle." Journal of Neurophysiology 57, no. 2 (1987): 343–56. http://dx.doi.org/10.1152/jn.1987.57.2.343.

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The spike trains of afferent fibers innervating muscle spindles and Golgi tendon organs in the medial gastrocnemius muscle were recorded during spontaneous contractions in either decerebrate cats or decapitate cats treated with L-dopa. For each afferent fiber, the approximate location of its receptor within the muscle was determined. Cross-correlation histograms were compiled from the simultaneously recorded spike trains of pairs of afferent fibers (Ia, Ib, spindle II) to determine if the degree of temporal correlation in their discharge was related to the mutual proximity of the receptors the
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Zhang, Qi, Yihui Zhang, and Yemao Xia. "Bayesian Feature Extraction for Two-Part Latent Variable Model with Polytomous Manifestations." Mathematics 12, no. 5 (2024): 783. http://dx.doi.org/10.3390/math12050783.

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Semi-continuous data are very common in social sciences and economics. In this paper, a Bayesian variable selection procedure is developed to assess the influence of observed and/or unobserved exogenous factors on semi-continuous data. Our formulation is based on a two-part latent variable model with polytomous responses. We consider two schemes for the penalties of regression coefficients and factor loadings: a Bayesian spike and slab bimodal prior and a Bayesian lasso prior. Within the Bayesian framework, we implement a Markov chain Monte Carlo sampling method to conduct posterior inference.
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Czanner, Gabriela, Uri T. Eden, Sylvia Wirth, Marianna Yanike, Wendy A. Suzuki, and Emery N. Brown. "Analysis of Between-Trial and Within-Trial Neural Spiking Dynamics." Journal of Neurophysiology 99, no. 5 (2008): 2672–93. http://dx.doi.org/10.1152/jn.00343.2007.

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Recording single-neuron activity from a specific brain region across multiple trials in response to the same stimulus or execution of the same behavioral task is a common neurophysiology protocol. The raster plots of the spike trains often show strong between-trial and within-trial dynamics, yet the standard analysis of these data with the peristimulus time histogram (PSTH) and ANOVA do not consider between-trial dynamics. By itself, the PSTH does not provide a framework for statistical inference. We present a state-space generalized linear model (SS-GLM) to formulate a point process represent
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Dissertations / Theses on the topic "Spike-inference analysis"

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Echtermeyer, Christoph. "Causal pattern inference from neural spike train data." Thesis, St Andrews, 2009. http://hdl.handle.net/10023/843.

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Sharp, Kevin John. "Effective Bayesian inference for sparse factor analysis models." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/effective-bayesian-inference-for-sparse-factor-analysis-models(4facfde0-0aae-4f09-aeaa-960111e854ff).html.

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We study how to perform effective Bayesian inference in high-dimensional sparse Factor Analysis models with a zero-norm, sparsity-inducing prior on the model parameters. Such priors represent a methodological ideal, but Bayesian inference in such models is usually regarded as impractical. We test this view. After empirically characterising the properties of existing algorithmic approaches, we use techniques from statistical mechanics to derive a theory of optimal learning in the restricted setting of sparse PCA with a single factor. Finally, we describe a novel `Dense Message Passing' algorith
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Book chapters on the topic "Spike-inference analysis"

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Shahbaba, Babak, Sam Behseta, and Alexander Vandenberg-Rodes. "Neuronal Spike Train Analysis Using Gaussian Process Models." In Nonparametric Bayesian Inference in Biostatistics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19518-6_13.

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Conference papers on the topic "Spike-inference analysis"

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Chen, Zhe, Fabian Kloosterman, Matthew A. Wilson, and Emery N. Brown. "Variational Bayesian inference for point process generalized linear models in neural spike trains analysis." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495095.

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Zhu, Zulun, Jiaying Peng, Jintang Li, Liang Chen, Qi Yu, and Siqiang Luo. "Spiking Graph Convolutional Networks." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/338.

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Graph Convolutional Networks (GCNs) achieve an impressive performance due to the remarkable representation ability in learning the graph information. However, GCNs, when implemented on a deep network, require expensive computation power, making them difficult to be deployed on battery-powered devices. In contrast, Spiking Neural Networks (SNNs), which perform a bio-fidelity inference process, offer an energy-efficient neural architecture. In this work, we propose SpikingGCN, an end-to-end framework that aims to integrate the embedding of GCNs with the biofidelity characteristics of SNNs. The o
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Journal articles
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