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作者： Sarmashghi, Mehrad Boston University

学位级别：Ph.D., Doctor of Philosophy

neural coding is essential for understanding how the activity of individual neurons or ensembles of neurons relates to cognitive processing of the world. Neurons can code for multiple variables simultaneously and neuroscientists are interested in classifying neurons based on the variables they *** a model identification paradigm to identify neurons in terms of their coding properties is essential to understanding how the brain processes information. Statistical paradigms are capable of methodologically determining the factors influencing neural observations and assessing the quality of the resulting models to characterize and classify individual neurons. However, as neural recording technologies develop to produce data from massive populations, classical statistical methods often lack the computational efficiency required to handle such data. Machine learning (ML) approaches are known for enabling efficient large scale data analysis; however, they require huge training data sets, and model assessment and interpretation are more challenging than for classical statistical *** address these challenges, we develop an integrated framework, combining statistical modeling and machine learning approaches to identify the coding properties of neurons from large populations. In order to evaluate our approaches, we apply them to data from a population of neurons in rat hippocampus and prefrontal cortex (PFC), to characterize how spatial learning and memory processes are represented in these areas. The data consist of local field potentials (LFP) and spiking data simultaneously recorded from the CA1 region of hippocampus and the PFC of a male Long Evans rat performing a spatial alternation task on a W-shaped track. We have examined this data in three separate but related *** one project, we build an improved class of statistical models for neural activity by expanding a common set of basis functions to increase the statistical power of the resulting

关键词： Computational neuroscience Hippocampus Machine learning neural data analysis Prefrontal cortex Statistical analysis

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data processing techniques impact quantification of cortico-cortical evoked potentials

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JOURNAL OF NEUROSCIENCE METHODS 2024年 408卷 110130页

作者： Levinson, L. H. Sun, S. Paschall, C. J. Perks, K. M. Weaver, K. E. Perlmutter, S. I. Ko, A. L. Ojemann, J. G. Herron, J. A. Univ Washington Grad Program Neurosci 1959 NE Pacific StT 47 Seattle WA 98195 USA Univ Washington Dept Bioengn Box 355061 Seattle WA 98195 USA Univ Washington Dept Radiol 1959 NE Pacific St Seattle WA 98195 USA Univ Washington Dept Physiol & Biophys 1705 NE Pacific StHSB Room G424Box 357290 Seattle WA 98195 USA Univ Washington Dept Neurol Surg Box 3564701959 NE Pacific St Seattle WA 98195 USA

Background: Cortico-cortical evoked potentials (CCEPs) are a common tool for probing effective connectivity in intracranial human electrophysiology. As with all human electrophysiology data, CCEP data are highly susceptible to noise. To address noise, filters and re-referencing are often applied to CCEP data, but different processing strategies are used from study to study. New method: We systematically compare how common average re-referencing and filtering CCEP data impacts quantification. Results: We show that common average re-referencing and filters, particularly filters that cut out more frequencies, can significantly impact the quantification of CCEP magnitude and morphology. We identify that high cutoff high pass filters (> 0.5 Hz), low cutoff low pass filters (< 200 Hz), and common average re-referencing impact quantification across subjects. However, we also demonstrate that the presence of noise may impact CCEP quantification, and preprocessing is necessary to mitigate this. We show that filtering is more effective than re-referencing or averaging across trials for reducing most common types of noise. Comparison with existing methods: These results suggest that existing CCEP processing methods must be applied with care to maximize noise reduction and minimize changes to the data. We do not test every available processing strategy;rather we demonstrate that processing can influence the results of CCEP studies. We emphasize the importance of reporting all processing methods, particularly re-referencing methods. Conclusions: We propose a general framework for choosing an appropriate processing pipeline for CCEP data, taking into consideration the noise levels of a specific dataset. We suggest that minimal gentle filtering is preferable.

关键词： Cortico-cortical evoked potential Evoked potential Effective connectivity Preprocessing neural data analysis Electrocorticography

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A probabilistic hammer for nailing complex neural data analyses

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NEURON 2021年第7期109卷 1077-1079页

作者： Fiser, Jozsef Koblinger, Adam Cent European Univ Dept Cognit Sci Vienna Austria Cent European Univ Ctr Cognit Computat Budapest Hungary

In this issue of Neuron, Mlynarski et al. (2021) provide a maxent-based normative method for flexible neural data analysis by combining data-driven and theory-driven approaches. The next challenge is identifying the r... 详细信息

关键词： neural data analysis Bayesian optimization loss calibration

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Estimating time-varying directed neural networks

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STATISTICS AND COMPUTING 2020年第5期30卷 1209-1220页

作者： Wang, Haixu Cao, Jiguo Simon Fraser Univ Dept Stat & Actuarial Sci Burnaby BC V5A 1S6 Canada

Reconstructing the functional network of a neuron cluster is a fundamental step to reveal the complex interactions among neural systems of the brain. Current approaches to reconstruct a network of neurons or neural systems focus on establishing a static network by assuming the neural network structure does not change over time. To the best of our knowledge, this is the first attempt to build a time-varying directed network of neurons by using an ordinary differential equation model, which allows us to describe the underlying dynamical mechanism of network connections. The proposed method is demonstrated by estimating a network of wide dynamic range neurons located in the dorsal horn of the rats' spinal cord in response to pain stimuli applied to the Zusanli acupoint on the right leg. The finite sample performance of the proposed method is also investigated with a simulation study.

关键词： Differential equation neural data analysis Network reconstruction Poisson process Spike sequence

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neural anatomy and optical microscopy (NAOMi) simulation for evaluating calcium imaging methods

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JOURNAL OF NEUROSCIENCE METHODS 2021年第0期358卷 109173-109173页

作者： Song, Alexander Gauthier, Jeff L. Pillow, Jonathan W. Tank, David W. Charles, Adam S. Princeton Univ Princeton Neurosci Inst Princeton NJ 08540 USA Princeton Univ Dept Phys Princeton NJ 08540 USA Princeton Univ Dept Psychol Princeton NJ 08540 USA Princeton Univ Bezos Ctr Neural Circuit Dynam Princeton NJ 08540 USA Princeton Univ Dept Mol Biol Princeton NJ 08540 USA Johns Hopkins Univ Dept Biomed Engn Baltimore MD 21218 USA Johns Hopkins Univ Math Inst Data Sci Baltimore MD 21218 USA Johns Hopkins Univ Ctr Imaging Sci Baltimore MD 21218 USA Johns Hopkins Univ Kavli Neurosci Discovery Inst Baltimore MD 21218 USA

Background: The past decade has seen a multitude of new in vivo functional imaging methodologies. However, the lack of ground-truth comparisons or evaluation metrics makes the large-scale, systematic validation vital to the continued development and use of optical microscopy impossible. New-method: We provide a new framework for evaluating two-photon microscopy methods via in silico neural Anatomy and Optical Microscopy (NAOMi) simulation. Our computationally efficient model generates large anatomical volumes of mouse cortex, simulates neural activity, and incorporates optical propagation and scanning to create realistic calcium imaging datasets. Results: We verify NAOMi simulations against in vivo two-photon recordings from mouse cortex. We leverage this in silico ground truth to directly compare different segmentation algorithms and optical designs. We find modern segmentation algorithms extract strong neural time-courses comparable to estimation using oracle spatial information, but with an increase in the false positive rate. Comparison between optical setups demonstrate improved resilience to motion artifacts in sparsely labeled samples using Bessel beams, increased signal-to-noise ratio and cell-count using low numerical aperture Gaussian beams and nuclear GCaMP, and more uniform spatial sampling with temporal focusing versus multi-plane imaging. Comparison with existing methods: NAOMi is a first-of-its kind framework for assessing optical imaging modalities. Existing methods are either anatomical simulations or do not address functional imaging. Thus there is no competing method for simulating realistic functional optical microscopy data. Conclusions: By leveraging the rich accumulated knowledge of neural anatomy and optical physics, we provide a powerful new tool to assess and develop important methods in neural imaging.

关键词： neural data analysis Two-photon microscopy Large-scale recordings neural simulation

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Perceptual Decision-Making: A Field in the Midst of a Transformation

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NEURON 2018年第2期100卷 453-462页

作者： Najafi, Farzaneh Churchland, Anne K. Cold Spring Harbor Lab POB 100 Cold Spring Harbor NY 11724 USA

Major changes are underway in the field of perceptual decision-making. Single-neuron studies have given way to population recordings with identified cell types, traditional analyses have been extended to accommodate these large and diverse collections of neurons, and novel methods of neural disruption have provided insights about causal circuits. Further, the field has expanded to include multiple new species: rodents and invertebrates, for example, have been instrumental in demonstrating the importance of internal state on neural responses. Finally, a renewed interest in ethological stimuli prompted development of new behaviors, frequently analyzed by new, automated movement tracking methods. Taken together, these advances constitute a seismic shift in both our approach and understanding of how incoming sensory signals are used to guide decisions.

关键词： decision-making cognition mice imaging neural data analysis computational models

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Unsupervised Discovery of Demixed, Low-Dimensional neural Dynamics across Multiple Timescales through Tensor Component analysis

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NEURON 2018年第6期98卷 1099-+页

作者： Williams, Alex H. Kim, Tony Hyun Wang, Forea Vyas, Saurabh Ryu, Stephen I. Shenoy, Krishna V. Schnitzer, Mark Kolda, Tamara G. Ganguli, Surya Stanford Univ Neurosci Grad Program Stanford CA 94305 USA Stanford Univ Dept Elect Engn Stanford CA 94305 USA Stanford Univ Bioengn Dept Stanford CA 94305 USA Stanford Univ Dept Appl Phys Stanford CA 94305 USA Stanford Univ Dept Biol Stanford CA 94305 USA Stanford Univ Neurobiol Dept Stanford CA 94305 USA Stanford Univ Bio X Program Stanford CA 94305 USA Stanford Univ Stanford Neurosci Inst Stanford CA 94305 USA Stanford Univ Howard Hughes Med Inst Stanford CA 94305 USA Stanford Univ CNC Program Stanford CA 94305 USA Palo Alto Med Fdn Dept Neurosurg Palo Alto CA 94301 USA Sandia Natl Labs Livermore CA 94551 USA

Perceptions, thoughts, and actions unfold over millisecond timescales, while learned behaviors can require many days to mature. While recent experimental advances enable large-scale and long-term neural recordings with high temporal fidelity, it remains a formidable challenge to extract unbiased and interpretable descriptions of how rapid single-trial circuit dynamics change slowly over many trials to mediate learning. We demonstrate a simple tensor component analysis (TCA) can meet this challenge by extracting three interconnected, low-dimensional descriptions of neural data: neuron factors, reflecting cell assemblies;temporal factors, reflecting rapid circuit dynamics mediating perceptions, thoughts, and actions within each trial;and trial factors, describing both long-term learning and trial-to-trial changes in cognitive state. We demonstrate the broad applicability of TCA by revealing insights into diverse datasets derived from artificial neural networks, large-scale calcium imaging of rodent prefrontal cortex during maze navigation, and multi-electrode recordings of macaque motor cortex during brain machine interface learning.

关键词： neural data analysis single-trial analysis dimensionality reduction motor control navigation learning brain machine interfaces recurrent neural networks large-scale recordings gain modulation

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Emotion Annotation Using Hierarchical Aligned Cluster analysis 24th

Emotion Annotation Using Hierarchical Aligned Cluster Analys...

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24th International Conference on neural Information Processing (ICONIP)

作者： Zhao, Wei-Ye Fang, Sheng Ji, Ting Ji, Qian Zheng, Wei-Long Lu, Bao-Liang Ctr Brain Comp & Machine Intelligence Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Educ Commiss Intelligent Interact & Cogn Key Lab Shanghai Peoples R China Shanghai Jiao Tong Univ Brain Sci & Technol Res Ctr Shanghai Peoples R China

ISBN: (纸本)9783319700939;9783319700922

The correctness of annotation is quite important in supervised learning, especially in electroencephalography (EEG)-based emotion recognition. The conventional EEG annotations for emotion recognition are based on the feedback like questionnaires about emotion elicitation from subjects. However, these methods are subjective and divorced from experiment data, which lead to inaccurate annotations. In this paper, we pose the problem of annotation optimization as temporal clustering one. We mainly explore two types of clustering algorithms: aligned clustering analysis (ACA) and hierarchical aligned clustering analysis (HACA). We compare the performance of questionnaire-based, ACA-based, HACA-based annotation on a public EEG dataset called SEED. The experimental results demonstrate that our proposed ACA-based and HACA-based annotation achieve an accuracy improvement of 2.59% and 4.53% in average, respectively, which shows their effectiveness for emotion recognition.

关键词： neural data analysis Time series analysis EEG annotations Emotion recognition

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Estimating Conditional Intensity Function of a neural Spike Train by Particle Markov Chain Monte Carlo and Smoothing

Estimating Conditional Intensity Function of a Neural Spike ...

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作者： Haixu (Alex) Wang Simon Fraser University

学位级别：硕士

Understanding neural activities is fundamental and challenging in decoding how the brain processes information. An essential part of the problem is to define a meaningful and quantitative characterization of neural activities when they are represented by a sequence of action potentials or a neural spike train. The thesis approaches to use a point process to represent a neural spike train, and such representation provides a conditional intensity function (CIF) to describe neural activities. The estimation procedure for CIF, including particle Markov Chain Monte Carlo (PMCMC) and smoothing, is introduced and applied to a real data set. From the CIF and its derivative of a neural spike train, we can successfully observe adaption behavior. Simulation study verifies that the estimation procedure provides reliable estimate of CIF. This framework provides a definite quantification of neural activities and facilitates further investigation of understanding the brain from neurological perspective.

关键词： neural data analysis neural spike train point process state-space model particle Markov Chain Monte Carol smoothing

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Joint analysis of spikes and local field potentials using copula

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NEUROIMAGE 2016年 133卷 457-467页

作者： Hu, Meng Li, Mingyao Li, Wu Liang, Hualou Drexel Univ Sch Biomed Engn Philadelphia PA 19104 USA Univ Penn Sch Med Dept Biostat & Epidemiol Philadelphia PA 19104 USA Beijing Normal Univ State Key Lab Cognit Neurosci & Learning Beijing 100875 Peoples R China Beijing Normal Univ IDG McGovern Inst Brain Res Beijing 100875 Peoples R China

Recent technological advances, which allow for simultaneous recording of spikes and local field potentials (LFPs) at multiple sites in a given cortical area or across different areas, have greatly increased our understanding of signal processing in brain circuits. Joint analysis of simultaneously collected spike and LFP signals is an important step to explicate how the brain orchestrates information processing. In this contribution, we present a novel statistical framework based on Gaussian copula to jointly model spikes and LFP. In our approach, we use copula to link separate, marginal regression models to construct a joint regression model, in which the binary-valued spike train data are modeled using generalized linear model (GLM) and the continuous-valued LFP data are modeled using linear regression. Model parameters can be efficiently estimated via maximum-likelihood. In particular, we show that our model offers a means to statistically detect directional influence between spikes and LFP, akin to Granger causality measure, and that we are able to assess its statistical significance by conducting a Wald test. Through extensive simulations, we also show that our method is able to reliably recover the true model used to generate the data. To demonstrate the effectiveness of our approach in real setting, we further apply the method to a mixed neural dataset, consisting of spikes and LFP simultaneously recorded from the visual cortex of a monkey performing a contour detection task. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Copula neural data analysis Generalized linear model Spike trains Local field potentials

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