The Approach of a Neuron Population Firing Rate to a New Equilibrium: An Exact Theoretical Result

The response of a noninteracting population of identical neurons to a step change in steady synaptic input can be analytically calculated exactly from the dynamical equation that describes the population\u27s evolution in time. Here, for model integrate-and-fire neurons that undergo a fixed finite upward shift in voltage in response to each synaptic event, we compare the theoretical prediction with the result of a direct simulation of 90,000 model neurons. The degree of agreement supports the applicability of the population dynamics equation. The theoretical prediction is in the form of a series. Convergence is rapid, so that the full result is well approximated by a few terms

Pasternak Zeminine Yaslanan Ve Kısmen Akışkan İle Temas Eden Mindlin Plağının Dinamik Davranışı

Konferans Bildirisi -- Teorik ve Uygulamalı Mekanik Türk Milli Komitesi, 2013Conference Paper -- Theoretical and Applied Mechanical Turkish National Committee, 2013Bu çalışmada Pasternak zeminine oturan ve aynı zamanda durağan bir akışkan alanıyla kısmen temas eden Mindlin plaklarının dinamik davranışı incelenmiştir. Gâteaux türevinden yararlanılarak plak-zemin etkileşimi karışık sonlu eleman yöntemi ile ele alınmış, yayılı kütle matrisinde plak dönel eylemsizlikleri de gözetilmiştir. Akışkan-yapı etkileşimi içinse sınır eleman yönteminden yararlanılmıştır. Uygulanan kabuller, plak-zemin sisteminin akışkanla temas sırasında kendi doğal modlarında titreştiği ve her bir elastik moda karşılık plak ıslak yüzeyi üzerinde bir basınç dağılımının oluştuğu şeklindedir. Akışkan serbest yüzey etkileri sonsuz-frekans kabulü altında ihmal edilmiştir. Ortaya çıkan etkileşim kuvvetleri akışkan eylemsizlik etkisini temsil eden genelleştirilmiş ek su kütlesi formundadır. Sunulan çözüm yaklaşımı örneklerle test edilmiş, zemin varlığının ve akışkan etkileşiminin Mindlin plağının dinamik davranışına olan etkileri parametrik olarak ayrıca incelenmiştir.This study is concerned with the dynamic response of Mindlin plates resting on an Pasternak foundation and simultaneously interacting partially with a quiescent fluid field. Plate-foundation interaction is simulated in the framework of a mixed finite element by employing the Gâteaux differential. Consistent mass matrix formulation is used by considering the rotary inertia. Fluid-structure interaction analysis is carried out by the boundary element method. It is assumed that the plate – elastic foundation system vibrates in its in vacuo eigenmodes when it is in contact with fluid, and that each mode gives rise to a corresponding surface pressure distribution on the wetted surface of the structure. The fluid free-surface effects are neglected by imposing the high-frequency limit condition. The fluid-structure interaction forces are calculated in terms of the generalized hydrodynamic added mass coefficients that represent the inertial effect of the fluid. The methodology is verified, and the influence of foundation and fluid interaction on the dynamic behavior of the Mindlin plate is studied through parametric investigations

Screening for Alzheimer's disease using prefrontal resting-state functional near-infrared spectroscopy

IntroductionAlzheimer's disease (AD) is neurodegenerative dementia that causes neurovascular dysfunction and cognitive impairment. Currently, 50 million people live with dementia worldwide, and there are nearly 10 million new cases every year. There is a need for relatively less costly and more objective methods of screening and early diagnosis. MethodsFunctional near-infrared spectroscopy (fNIRS) systems are a promising solution for the early Detection of AD. For a practical clinically relevant system, a smaller number of optimally placed channels are clearly preferable. In this study, we investigated the number and locations of the best-performing fNIRS channels measuring prefrontal cortex activations. Twenty-one subjects diagnosed with AD and eighteen healthy controls were recruited for the study. ResultsWe have shown that resting-state fNIRS recordings from a small number of prefrontal locations provide a promising methodology for detecting AD and monitoring its progression. A high-density continuous-wave fNIRS system was first used to verify the relatively lower hemodynamic activity in the prefrontal cortical areas observed in patients with AD. By using the episode averaged standard deviation of the oxyhemoglobin concentration changes as features that were fed into a Support Vector Machine; we then showed that the accuracy of subsets of optical channels in predicting the presence and severity of AD was significantly above chance. The results suggest that AD can be detected with a 0.76 sensitivity score and a 0.68 specificity score while the severity of AD could be detected with a 0.75 sensitivity score and a 0.72 specificity score with <= 5 channels. DiscussionThese scores suggest that fNIRS is a viable technology for conveniently detecting and monitoring AD as well as investigating underlying mechanisms of disease progression

Decoding human mental states by whole-head EEG+fNIRS during category fluency task performance

Objective: Concurrent scalp electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), which we refer to as EEG+fNIRS, promises greater accuracy than the individual modalities while remaining nearly as convenient as EEG. We sought to quantify the hybrid system's ability to decode mental states and compare it with unimodal systems. Approach: We recorded from healthy volunteers taking the category fluency test and applied machine learning techniques to the data. Main results: EEG+fNIRS's decoding accuracy was greater than that of its subsystems, partly due to the new type of neurovascular features made available by hybrid data. Significance: Availability of an accurate and practical decoding method has potential implications for medical diagnosis, brain-computer interface design, and neuroergonomics

Improving the ability of ED physicians to identify subclinical/electrographic seizures on EEG after a brief training module

Background: Approximately 5% of emergency department (ED) patients with altered mental status (AMS) have non-convulsive seizures (NCS). Patients with NCS should be diagnosed with EEG as soon as possible to initiate antiepileptic treatment. Since ED physicians encounter such patients first in the ED, they should be familiar with general EEG principles as well as the EEG patterns of NCS/NCSE. We evaluated the utility of a brief training module in enhancing the ED physicians’ ability to identify seizures on EEG. Methods: This was a randomized controlled trial conducted in three academic institutions. A slide presentation was developed describing the basic principles of EEG including EEG recording techniques, followed by characteristics of normal and abnormal patterns, the goal of which was to familiarize the participants with EEG seizure patterns. We enrolled board-certified emergency medicine physicians into the trial. Subjects were randomized to control or intervention groups. Participants allocated to the intervention group received a self-learning training module and were asked to take a quiz of EEG snapshots after reviewing the presentation, while the control group took the quiz without the training. Results: A total of 30 emergency physicians were enrolled (10 per site, with 15 controls and 15 interventions). Participants were 52% male with median years of practice of 9.5 years (3, 14). The percentage of correct answers in the intervention group (65%, 63% and 75%) was significantly different (p = 0.002) from that of control group (50%, 45% and 60%). Conclusions: A brief self-learning training module improved the ability of emergency physicians in identifying EEG seizure patterns

Measuring mental workload with EEG+fNIRS

We studied the capability of a Hybrid functional neuroimaging technique to quantify human mental workload (MWL). We have used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) as imaging modalities with 17 healthy subjects performing the letter n-back task, a standard experimental paradigm related to working memory (WM). The level of MWL was parametrically changed by variation of n from 0 to 3. Nineteen EEG channels were covering the whole-head and 19 fNIRS channels were located on the forehead to cover the most dominant brain region involved in WM. Grand block averaging of recorded signals revealed specific behaviors of oxygenated-hemoglobin level during changes in the level of MWL. A machine learning approach has been utilized for detection of the level of MWL. We extracted different features from EEG, fNIRS, and EEG+fNIRS signals as the biomarkers of MWL and fed them to a linear support vector machine (SVM) as train and test sets. These features were selected based on their sensitivity to the changes in the level of MWL according to the literature. We introduced a new category of features within fNIRS and EEG+fNIRS systems. In addition, the performance level of each feature category was systematically assessed. We also assessed the effect of number of features and window size in classification performance. SVM classifier used in order to discriminate between different combinations of cognitive states from binary- and multi-class states. In addition to the cross-validated performance level of the classifier other metrics such as sensitivity, specificity, and predictive values were calculated for a comprehensive assessment of the classification system. The Hybrid (EEG+fNIRS) system had an accuracy that was significantly higher than that of either EEG or fNIRS. Our results suggest that EEG+fNIRS features combined with a classifier are capable of robustly discriminating among various levels of MWL. Results suggest that EEG+fNIRS should be preferred to only EEG or fNIRS, in developing passive BCIs and other applications which need to monitor users' MWL

Modeling a large population of traders:

We introduce a method of accurately and efficiently modeling a large population of participants in a financial market. Each participant is modeled as having an internal preference state affected by the continual arrival of exogenous information and by the behavior of others. In order to describe a community of traders, we introduce a population equation that is derived rigorously from the underlying single-agent model. The population equation is used to investigate collective behavior with mimetic interactions. We observe and study the sharp transitions in parameter space from a stable time-independent regime to instability where the demand and supply diverge sharply. © 2006 Elsevier B.V. All rights reserved