作者: 凹槽 時(shí)間: 2025-3-22 00:02
Sound,re the nature of trial-to-trail variability, and seek to verify our hypothesis by developing a decoding algorithm that predicts context from spiking data using a model characterizing changes in the full distribution of firing rate structure across trials. We compare this algorithm to another decodin作者: Cleave 時(shí)間: 2025-3-22 04:14
Martin Kahmann,Roland Kleinknechttes that affect behavior, and (2) identifying neural correlates of stimuli, responses, and states. The framework consists of first constructing state-space models from behavioral data using maximum likelihood methods, and then identifying neural correlates of external stimuli, behavioral responses, 作者: FOIL 時(shí)間: 2025-3-22 07:11
Kommunikationsmittel im Strommarktr infer the brain’s intention during adaptation. This decoder significantly improves the speed and accuracy of model adaptation. Moreover, at steady state, the learned point process filter improves performance over the state-of-the-art Kalman filters due to the fast control and feedback rates and th作者: 增長(zhǎng) 時(shí)間: 2025-3-22 10:31
Marktforschung: Beispiel Prepaymentz?hleraid our understanding of the pathological states related to these signals and has the potential to be used in designing bio-inspired pulsatile controllers; immediate applications include understanding normal and pathological neuroendocrine and affective states.作者: 聽(tīng)寫 時(shí)間: 2025-3-22 13:33 作者: 聽(tīng)寫 時(shí)間: 2025-3-22 19:01
Matthias Heining,Ralf Schünemannonly predict the networks underlying anesthesia-induced oscillations but also guide experiments to further understand the role of these oscillations in the behavioral states that accompany anesthesia.作者: 草本植物 時(shí)間: 2025-3-22 23:30 作者: Indolent 時(shí)間: 2025-3-23 05:22
What Can Trial-to-Trial Variability Tell Us? A Distribution-Based Approach to Spike Train Decoding ire the nature of trial-to-trail variability, and seek to verify our hypothesis by developing a decoding algorithm that predicts context from spiking data using a model characterizing changes in the full distribution of firing rate structure across trials. We compare this algorithm to another decodin作者: 簡(jiǎn)略 時(shí)間: 2025-3-23 07:12
Characterizing Complex Human Behaviors and Neural Responses Using Dynamic Modelstes that affect behavior, and (2) identifying neural correlates of stimuli, responses, and states. The framework consists of first constructing state-space models from behavioral data using maximum likelihood methods, and then identifying neural correlates of external stimuli, behavioral responses, 作者: 跳脫衣舞的人 時(shí)間: 2025-3-23 11:41 作者: 歪曲道理 時(shí)間: 2025-3-23 17:25
From Physiological Signals to Pulsatile Dynamics: A Sparse System Identification Approachaid our understanding of the pathological states related to these signals and has the potential to be used in designing bio-inspired pulsatile controllers; immediate applications include understanding normal and pathological neuroendocrine and affective states.作者: Abrade 時(shí)間: 2025-3-23 20:10 作者: insurgent 時(shí)間: 2025-3-23 22:52
Inferring Neuronal Network Mechanisms Underlying Anesthesia-Induced Oscillations Using Mathematical only predict the networks underlying anesthesia-induced oscillations but also guide experiments to further understand the role of these oscillations in the behavioral states that accompany anesthesia.作者: HARP 時(shí)間: 2025-3-24 03:03 作者: ORE 時(shí)間: 2025-3-24 07:38
Retraction Note to: Artifact Rejection for Concurrent TMS-EEG Data,作者: evanescent 時(shí)間: 2025-3-24 12:02 作者: Landlocked 時(shí)間: 2025-3-24 18:28 作者: ALOFT 時(shí)間: 2025-3-24 22:37
Sound,terizing neuronal population dynamics or analyzing various sorts of neural data. The inference of latent variable models can lead to novel solutions for signal detection, neural decoding, denoising, dimensionality reduction, and data visualization. We review general modeling and inference strategies作者: amenity 時(shí)間: 2025-3-25 01:45 作者: ear-canal 時(shí)間: 2025-3-25 07:02 作者: 勉強(qiáng) 時(shí)間: 2025-3-25 07:49 作者: 冒失 時(shí)間: 2025-3-25 14:51 作者: 充氣球 時(shí)間: 2025-3-25 19:54
Martin Kahmann,Roland Kleinknechtinical neuroscience. This chapter will examine this question through the lens of dynamical systems and control theory. We will survey existing methods and new formalisms to probe and assess the geometry of neural trajectories, i.e., neural activity patterns at multiple spatial scales. In particular,作者: vertebrate 時(shí)間: 2025-3-25 21:50 作者: 強(qiáng)制令 時(shí)間: 2025-3-26 02:16
Marktforschung: Beispiel Prepaymentz?hlerion to achieve enhanced perceptual capacity. To articulate this hypothesis, we describe stimulus-evoked activity of a neural population based on the maximum entropy principle with constraints on two types of overlapping activities, one that is controlled by stimulus conditions and the other, termed 作者: 大喘氣 時(shí)間: 2025-3-26 07:54
Matthias Heining,Ralf Schünemannn. Anesthetics induce widespread and stereotypic changes in oscillatory patterns in EEG suggesting they operate on the scale of neuronal circuits to produce the anesthetic state. Details of anesthetic-induced alterations to neuronal circuits have only begun to be investigated and one of the main too作者: 殺菌劑 時(shí)間: 2025-3-26 11:32 作者: 聯(lián)想記憶 時(shí)間: 2025-3-26 13:51
https://doi.org/10.1007/978-3-319-71976-4Neural signal processing; Neuronal coding theories; Neural engineering; Neural activity; State-space par作者: Lipoprotein(A) 時(shí)間: 2025-3-26 20:02 作者: jaundiced 時(shí)間: 2025-3-27 00:14 作者: Canopy 時(shí)間: 2025-3-27 04:36
Zhe Chen,Sridevi V. SarmaPresents innovative methodological and algorithmic development in statistics, modeling, control, and signal processing for neural data analysis;.Includes a coherent framework for a broad class of neur作者: 萬(wàn)靈丹 時(shí)間: 2025-3-27 06:42 作者: 愉快么 時(shí)間: 2025-3-27 09:51
Kommunikationsmittel im StrommarktThe chapter “Artifact Rejection for Concurrent TMS-EEG Data” by “Wei Wu, Corey Keller, and Amit Etkin” published in the book “Dynamic Neuroscience”, pages 141–173, ., has been retracted at the request of the authors, as this work will be appearing in another publication.作者: 闡釋 時(shí)間: 2025-3-27 13:47
RETRACTED CHAPTER: Artifact Rejection for Concurrent TMS-EEG DataThe chapter “Artifact Rejection for Concurrent TMS-EEG Data” by “Wei Wu, Corey Keller, and Amit Etkin” published in the book “Dynamic Neuroscience”, pages 141–173, ., has been retracted at the request of the authors, as this work will be appearing in another publication.作者: atrophy 時(shí)間: 2025-3-27 21:29 作者: 售穴 時(shí)間: 2025-3-27 23:03
Characterizing Complex, Multi-Scale Neural Phenomena Using State-Space Modelss, and across multiple spatial and temporal scales. As a result, scientific investigation is limited in many cases not by the availability of data but by the availability of statistical and analysis tools. In particular, making use of such complex datasets to understand the mechanisms and effects of作者: 敏捷 時(shí)間: 2025-3-28 03:13 作者: 杠桿支點(diǎn) 時(shí)間: 2025-3-28 06:53 作者: 新字 時(shí)間: 2025-3-28 13:21 作者: 繁榮中國(guó) 時(shí)間: 2025-3-28 16:58 作者: 協(xié)定 時(shí)間: 2025-3-28 19:59
Brain–Machine Interfacesities, use a decoding algorithm to infer the subject’s intended movement and control a prosthetic device, and provide visual feedback to the subject. Thus BMIs can be viewed as closed-loop control systems. In this chapter, we review the computational components of a BMI and the common decoders used 作者: 厚顏 時(shí)間: 2025-3-29 00:05
Control-Theoretic Approaches for Modeling, Analyzing, and Manipulating Neuronal (In)activityinical neuroscience. This chapter will examine this question through the lens of dynamical systems and control theory. We will survey existing methods and new formalisms to probe and assess the geometry of neural trajectories, i.e., neural activity patterns at multiple spatial scales. In particular,作者: 蜈蚣 時(shí)間: 2025-3-29 04:32 作者: Favorable 時(shí)間: 2025-3-29 11:16 作者: 發(fā)牢騷 時(shí)間: 2025-3-29 12:18 作者: Humble 時(shí)間: 2025-3-29 19:02 作者: 小卒 時(shí)間: 2025-3-29 21:49 作者: emulsify 時(shí)間: 2025-3-30 02:01 作者: 細(xì)微差別 時(shí)間: 2025-3-30 08:05 作者: maudtin 時(shí)間: 2025-3-30 10:43
Sound,or signal detection, neural decoding, denoising, dimensionality reduction, and data visualization. We review general modeling and inference strategies for latent variable models. Finally, we illustrate our methods with several neuroscience applications using population spike trains recorded from the animal’s hippocampus and neocortices.作者: maladorit 時(shí)間: 2025-3-30 14:14
Latent Variable Modeling of Neural Population Dynamicsor signal detection, neural decoding, denoising, dimensionality reduction, and data visualization. We review general modeling and inference strategies for latent variable models. Finally, we illustrate our methods with several neuroscience applications using population spike trains recorded from the animal’s hippocampus and neocortices.作者: ANNUL 時(shí)間: 2025-3-30 17:43 作者: ingenue 時(shí)間: 2025-3-30 22:45
Introduction,oncepts and representative applications of statistics, signal processing, and control in neuroscience. Finally, we provide roadmaps for this edited book as well as pointers to the literature and other resources.
