Selectivity and Stability via Dendritic Nonlinearity

2007 ◽  
Vol 19 (7) ◽  
pp. 1798-1853 ◽  
Author(s):  
Kenji Morita ◽  
Masato Okada ◽  
Kazuyuki Aihara
Keyword(s):  
Network Architecture ◽  
Transfer Functions ◽  
Signal To Noise Ratio ◽  
Similar Degree ◽  
Inhibitory Cell ◽  
Highly Active ◽  
Wide Range ◽  
Input Layer ◽  
Activity State ◽  
Dendritic Branches

Inspired by recent studies regarding dendritic computation, we constructed a recurrent neural network model incorporating dendritic lateral inhibition. Our model consists of an input layer and a neuron layer that includes excitatory cells and an inhibitory cell; this inhibitory cell is activated by the pooled activities of all the excitatory cells, and it in turn inhibits each dendritic branch of the excitatory cells that receive excitations from the input layer. Dendritic nonlinear operation consisting of branch-specifically rectified inhibition and saturation is described by imposing nonlinear transfer functions before summation over the branches. In this model with sufficiently strong recurrent excitation, on transiently presenting a stimulus that has a high correlation with feed- forward connections of one of the excitatory cells, the corresponding cell becomes highly active, and the activity is sustained after the stimulus is turned off, whereas all the other excitatory cells continue to have low activities. But on transiently presenting a stimulus that does not have high correlations with feedforward connections of any of the excitatory cells, all the excitatory cells continue to have low activities. Interestingly, such stimulus-selective sustained response is preserved for a wide range of stimulus intensity. We derive an analytical formulation of the model in the limit where individual excitatory cells have an infinite number of dendritic branches and prove the existence of an equilibrium point corresponding to such a balanced low-level activity state as observed in the simulations, whose stability depends solely on the signal-to-noise ratio of the stimulus. We propose this model as a model of stimulus selectivity equipped with self-sustainability and intensity-invariance simultaneously, which was difficult in the conventional competitive neural networks with a similar degree of complexity in their network architecture. We discuss the biological relevance of the model in a general framework of computational neuroscience.

scholarly journals Detectivity optimization to detect of ultraweak light fluxes with an EM-CCD as binary photon counter array

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ibtissame Khaoua ◽  
Guillaume Graciani ◽  
Andrey Kim ◽  
François Amblard
Keyword(s):  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Photon Counting ◽  
Detection Methods ◽  
Strong Nonlinearity ◽  
Wide Range ◽  
Depth Analysis ◽  
Spatially Extended ◽  
Extended Sources ◽  
Photon Counting Mode

AbstractFor a wide range of purposes, one faces the challenge to detect light from extremely faint and spatially extended sources. In such cases, detector noises dominate over the photon noise of the source, and quantum detectors in photon counting mode are generally the best option. Here, we combine a statistical model with an in-depth analysis of detector noises and calibration experiments, and we show that visible light can be detected with an electron-multiplying charge-coupled devices (EM-CCD) with a signal-to-noise ratio (SNR) of 3 for fluxes less than $$30\,{\text{photon}}\,{\text{s}}^{ - 1} \,{\text{cm}}^{ - 2}$$ 30 photon s - 1 cm - 2 . For green photons, this corresponds to 12 aW $${\text{cm}}^{ - 2}$$ cm - 2 ≈ $$9{ } \times 10^{ - 11}$$ 9 × 10 - 11 lux, i.e. 15 orders of magnitude less than typical daylight. The strong nonlinearity of the SNR with the sampling time leads to a dynamic range of detection of 4 orders of magnitude. To detect possibly varying light fluxes, we operate in conditions of maximal detectivity $${\mathcal{D}}$$ D rather than maximal SNR. Given the quantum efficiency $$QE\left( \lambda \right)$$ Q E λ of the detector, we find $${ \mathcal{D}} = 0.015\,{\text{photon}}^{ - 1} \,{\text{s}}^{1/2} \,{\text{cm}}$$ D = 0.015 photon - 1 s 1 / 2 cm , and a non-negligible sensitivity to blackbody radiation for T > 50 °C. This work should help design highly sensitive luminescence detection methods and develop experiments to explore dynamic phenomena involving ultra-weak luminescence in biology, chemistry, and material sciences.

Modal frequencies of bridges from GNSS (GPS) monitoring data: Experimental, statistical evidence

2021 ◽  
Vol 17 (1-2) ◽  
pp. 3-14
Author(s):  
Stathis C. Stiros ◽  
F. Moschas ◽  
P. Triantafyllidis
Keyword(s):  
Signal To Noise Ratio ◽  
Vertical Axis ◽  
Bridge Monitoring ◽  
Low Snr ◽  
True Value ◽  
Gps Satellites ◽  
Wide Range ◽  
Spectral Peaks ◽  
Monitoring Survey ◽  
True Frequency

GNSS technology (known especially for GPS satellites) for measurement of deflections has proved very efficient and useful in bridge structural monitoring, even for short stiff bridges, especially after the advent of 100 Hz GNSS sensors. Mode computation from dynamic deflections has been proposed as one of the applications of this technology. Apart from formal modal analyses with GNSS input, and from spectral analysis of controlled free attenuating oscillations, it has been argued that simple spectra of deflections can define more than one modal frequencies. To test this scenario, we analyzed 21 controlled excitation events from a certain bridge monitoring survey, focusing on lateral and vertical deflections, recorded both by GNSS and an accelerometer. These events contain a transient and a following oscillation, and they are preceded and followed by intervals of quiescence and ambient vibrations. Spectra for each event, for the lateral and the vertical axis of the bridge, and for and each instrument (GNSS, accelerometer) were computed, normalized to their maximum value, and printed one over the other, in order to produce a single composite spectrum for each of the four sets. In these four sets, there was also marked the true value of modal frequency, derived from free attenuating oscillations. It was found that for high SNR (signal-to-noise ratio) deflections, spectral peaks in both acceleration and displacement spectra differ by up to 0.3 Hz from the true value. For low SNR, defections spectra do not match the true frequency, but acceleration spectra provide a low-precision estimate of the true frequency. This is because various excitation effects (traffic, wind etc.) contribute with numerous peaks in a wide range of frequencies. Reliable estimates of modal frequencies can hence be derived from deflections spectra only if excitation frequencies (mostly traffic and wind) can be filtered along with most measurement noise, on the basis of additional data.

scholarly journals Tuning the electronic structure of Ag-Pd alloys to enhance performance for alkaline oxygen reduction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
José A. Zamora Zeledón ◽  
Michaela Burke Stevens ◽  
G. T. Kasun Kalhara Gunasooriya ◽  
Alessandro Gallo ◽  
Alan T. Landers ◽  
...  
Keyword(s):  
Metal Binding ◽  
Precious Metal ◽  
Metal Content ◽  
Density Functional ◽  
Binding Energies ◽  
Intrinsic Activity ◽  
Energy Technologies ◽  
Highly Active ◽  
Wide Range ◽  
Precious Metal Content

AbstractAlloying is a powerful tool that can improve the electrocatalytic performance and viability of diverse electrochemical renewable energy technologies. Herein, we enhance the activity of Pd-based electrocatalysts via Ag-Pd alloying while simultaneously lowering precious metal content in a broad-range compositional study focusing on highly comparable Ag-Pd thin films synthesized systematically via electron-beam physical vapor co-deposition. Cyclic voltammetry in 0.1 M KOH shows enhancements across a wide range of alloys; even slight alloying with Ag (e.g. Ag0.1Pd0.9) leads to intrinsic activity enhancements up to 5-fold at 0.9 V vs. RHE compared to pure Pd. Based on density functional theory and x-ray absorption, we hypothesize that these enhancements arise mainly from ligand effects that optimize adsorbate–metal binding energies with enhanced Ag-Pd hybridization. This work shows the versatility of coupled experimental-theoretical methods in designing materials with specific and tunable properties and aids the development of highly active electrocatalysts with decreased precious-metal content.

scholarly journals AB0374 LUPUS LOW DISEASE ACTIVITY STATE AND MAINTAINING DRUG THERAPY: A RETROSPECTIVE INVESTIGATION

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1487.2-1487
Author(s):  
E. Gotelli ◽  
A. Sulli ◽  
G. Ferrari ◽  
G. Pacini ◽  
C. Schenone ◽  
...  
Keyword(s):  
Disease Activity ◽  
Hemolytic Anemia ◽  
Clinical Remission ◽  
Disease Onset ◽  
Treat To Target ◽  
Research Support ◽  
Low Disease Activity ◽  
Wide Range ◽  
Target Strategy ◽  
Activity State

Background:Systemic lupus erythematosus (SLE) is a chronic autoimmune multisystemic disease, that can begin with a wide range of clinical manifestations, and requires immunosuppressive therapies (1). A treat-to-target strategy leads to a high rate of clinical remission among patients (2). Several “remission” definitions have been provided in the last years and Lupus Low Disease Activity State (LLDAS) seems one of the best tools to evaluate it in clinical practice (3).Objectives:To evaluate the prevalence of SLE signs and symptoms at onset and the drugs used to induce and maintain the clinical remission, evaluated by LLDAS, in a real-life cohort of SLE patients.Methods:Thirty female SLE patients (mean age 52±15 years; mean age at disease onset 34±16 years, mean disease duration 18±13 years) in clinical remission have been enrolled (EULAR/ACR 2019 criteria) (4). Remission was defined by LLDAS (SLEDAI-2K < 4 and no activity in major organ systems, no hemolytic anemia; no new features of activity compared with previous assessment, physician global assessment (PGA) ≤ 1, prednisone dose ≤7.5 mg/day, well tolerated and stable therapy with maintenance doses of immunosuppressive drugs). Clinical and serological manifestations, SLEDAI-2K and pharmacological treatments were recorded at baseline and during follow-up.Results:Mucocutaneous involvement (57%), arthritis (30%), serositis (30%), nephritis (27%), leukopenia (23%), thrombocytopenia (20%), hemolytic anemia (13%), antiphospholipid syndrome manifestations (16%), neuro-psychiatric lupus symptoms (6%) were present in various combinations at disease onset. Baseline mean SLEDAI-2K was 10.5±2.5. Patients were treated with different dosages of glucocorticoids (100%), hydroxychloroquine (HCQ, 73%), cyclofosfamide (20%), mycophenolate mofetile (MMF, 13%), azathioprine (AZA, 13%), methotrexate (MTX, 13%), cyclosporine A (CSA, 6%), rituximab (3%), abatacept (ABA, 3%). Glucocorticoids were prescribed together with a single DMARD in 50% of cases and with two DMARDs in the remaining 50% of patients. Patients reached LLDAS remission after a mean time of 14±12 years, with a mean remission duration of 4.2±3.2 years (mean SLEDAI-2K at last visit 1±1; Mean PGA 0.4±0.1). Maintenance therapies during remission were prednisone ≤ 5 mg/day and/or HCQ ≤ 400 mg/day and/or CSA ≤ 200 mg/day and/or MTX ≤ 10 mg/weekly and/or MMF ≤ 2 g/day and/or AZA ≤ 100 mg/day. In particular, only prednisone 7%, only HCQ 3%, prednisone + HCQ 53%, prednisone + single DMARD (different from HCQ) 7%, prednisone + HCQ + DMARDs 30%.Conclusion:After reaching the clinical remission by a treat to target strategy, the administration of low dose of prednisone and HCQ in the majority of SLE patients (63%) seems useful to prevent new SLE flares. The retrospective design and the absence of a control group of patients with active disease limit this study.References:[1]Lisnevskaia L et al. 2014.Lancet384(9957):1878-1888.[2]Van Vollenhoven RF et al. 2014.Ann Rheum Dis73(6): 958-967.[3]Franklyn K et al. 2016.Ann Rheum Dis. 75(9): 1615-21.[4]Aringer M et al. 2019.Arthritis Rheumatol.71(9): 1400-1412.Disclosure of Interests:Emanuele Gotelli: None declared, Alberto Sulli Grant/research support from: Laboratori Baldacci, Giorgia Ferrari: None declared, Greta Pacini: None declared, Carlotta Schenone: None declared, Massimo Patanè: None declared, Pietro Francesco Bica: None declared, Carmen Pizzorni: None declared, Maurizio Cutolo Grant/research support from: Bristol-Myers Squibb, Actelion, Celgene, Consultant of: Bristol-Myers Squibb, Speakers bureau: Sigma-Alpha, Sabrina Paolino: None declared

scholarly journals The essential role of physiotherapists in providing rehabilitation services to people living with HIV in South Africa

2013 ◽  
Vol 69 (1) ◽  
Author(s):  
S. Cobbing ◽  
V. Chetty ◽  
J. Hanass-Hancock ◽  
J. Jelsma ◽  
H. Myezwa ◽  
...  
Keyword(s):  
South Africa ◽  
South African ◽  
Policy Formulation ◽  
People Living With Hiv ◽  
Highly Active ◽  
Hiv Epidemic ◽  
Wide Range ◽  
Living With Hiv ◽  
Education Courses

Despite increased access to highly active anti-retroviral therapy (HAART) in South Africa, there remains a high risk of people living with HIV (PLHIV) developing a wide range of disabilities. Physiotherapists are trained to rehabilitate individuals with the disabilities related to HIV. Not only can South African physiotherapists play a significant role in improving the lives of PLHIV, but by responding proactively to the HIV epidemic they can reinforce the relevance and value of the profession in this country at a time when many newly qualified therapists are unable to secure employment. This paper offers recommendations that may help to fuel this response. These ideas include enhancing HIV curricula at a tertiary level, designing and attending continuing education courses on HIV and researching Southern African rehabilitation interventions for HIV at all levels of practice. furthermore, it is vital that physiotherapists are at the forefront of directing multi-disciplinary responses to the rehabilitation of PLHIV in order to influence stakeholders who are responsible for health policy formulation. it is hoped that this paper stimulates discussion and further ideas amongst physiotherapists and other health professionals in order to improve the quality and access to care available to PLHIV in South Africa.

scholarly journals Methods of Power Line Interference Elimination in EMG Signal

2019 ◽  
Vol 40 ◽  
pp. 64-70
Author(s):  
Martina Ladrova ◽  
Radek Martinek ◽  
Jan Nedoma ◽  
Marcel Fajkus
Keyword(s):  
Signal To Noise Ratio ◽  
Notch Filter ◽  
Correlation Coefficients ◽  
Power Line ◽  
Shape Distortion ◽  
Emg Signal ◽  
Wide Range ◽  
Interference Elimination ◽  
Power Line Interference ◽  
Line Interference

Electromyogram (EMG) recordings are often corrupted by the wide range of artifacts, which one of them is power line interference (PLI). The study focuses on some of the well-known signal processing approaches used to eliminate or attenuate PLI from EMG signal. The results are compared using signal-to-noise ratio (SNR), correlation coefficients and Bland-Altman analysis for each tested method: notch filter, adaptive noise canceller (ANC) and wavelet transform (WT). Thus, the power of the remaining noise and shape of the output signal are analysed. The results show that the ANC method gives the best output SNR and lowest shape distortion compared to the other methods.

scholarly journals Improvement of SAXS Lab Equipment by Using Scatterless Apertures

2014 ◽  
Vol 70 (a1) ◽  
pp. C1330-C1330
Author(s):  
Joerg Wiesmann ◽  
Andreas Kleine ◽  
Christopher Umland ◽  
André Beerlink ◽  
Juergen Graf ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Experimental Setup ◽  
High Flux ◽  
Signal To Noise ◽  
Minimum Diameter ◽  
X Ray ◽  
X Ray Scattering ◽  
Wide Range ◽  
Beam Stop ◽  
Synchrotron Beamlines

Parasitic scattering caused by apertures is a well-known problem in X-ray analytics, which forces users and manufacturers to adapt their experimental setup to this unwanted phenomenon. Increased measurement times due to lower photon fluxes, a lower resolution caused by an enlarged beam stop, a larger beam defining pinhole-to-sample distance due to the integration of an antiscatter guard and generally a lower signal-to-noise ratio leads to a loss in data quality. In this presentation we will explain how the lately developed scatterless pinholes called SCATEX overcome the aforementioned problems. SCATEX pinholes are either made of Germanium or of Tantalum and momentarily have a minimum diameter of 30µm. Thus, these novel apertures are applicable to a wide range of different applications and X-ray energies. We will show measurements which were performed either at home-lab small angle X-ray scattering (SAXS) systems such as the NANOSTAR of Bruker AXS or at synchrotron beamlines. At the PTB four-crystal monochromator beamline at BESSY II data was collected for a comparison of conventional pinholes, scatterless Germanium slit systems and SCATEX pinholes. At the Nanofocus Endstation P03 beamline at PETRA III we compared the performance of our SCATEX apertures with conventional Tungsten slit systems under high flux density conditions.

scholarly journals TSAL: Two steps Adversarial learning based domain adaptation

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Haidi Hasan Badr ◽  
Nayer Mahmoud Wanas ◽  
Magda Fayek
Keyword(s):  
Network Architecture ◽  
Domain Adaptation ◽  
Data Availability ◽  
Fine Tuning ◽  
Target Domain ◽  
Adversarial Learning ◽  
Prior Art ◽  
Real World Problem ◽  
Wide Range ◽  
Benchmark Datasets

Since labeled data availability differs greatly across domains, Domain Adaptation focuses on learning in new and unfamiliar domains by reducing distribution divergence. Recent research suggests that the adversarial learning approach could be a promising way to achieve the domain adaptation objective. Adversarial learning is a strategy for learning domain-transferable features in robust deep networks. This paper introduces the TSAL paradigm, a two-step adversarial learning framework. It addresses the real-world problem of text classification, where source domain(s) has labeled data but target domain (s) has only unlabeled data. TSAL utilizes joint adversarial learning with class information and domain alignment deep network architecture to learn both domain-invariant and domain-specific features extractors. It consists of two training steps that are similar to the paradigm, in which pre-trained model weights are used as initialization for training with new data. TSAL’s two training phases, however, are based on the same data, not different data, as is the case with fine-tuning. Furthermore, TSAL only uses the learned domain-invariant feature extractor from the first training as an initialization for its peer in subsequent training. By doubling the training, TSAL can emphasize the leverage of the small unlabeled target domain and learn effectively what to share between various domains. A detailed analysis of many benchmark datasets reveals that our model consistently outperforms the prior art across a wide range of dataset distributions.

scholarly journals BUILDING CHANGE DETECTION FROM BITEMPORAL AERIAL IMAGES USING DEEP LEARNING

2020 ◽  
Vol V-2-2020 ◽  
pp. 565-571
Author(s):  
S. Su ◽  
T. Nawata ◽  
T. Fuse
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Urban Areas ◽  
Network Architecture ◽  
Aerial Images ◽  
Detection Accuracy ◽  
Detection Model ◽  
Wide Range ◽  
Surface Models ◽  
Data Source

Abstract. Automatic building change detection has become a topical issue owing to its wide range of applications, such as updating building maps. However, accurate building change detection remains challenging, particularly in urban areas. Thus far, there has been limited research on the use of the outdated building map (the building map before the update, referred to herein as the old-map) to increase the accuracy of building change detection. This paper presents a novel deep-learning-based method for building change detection using bitemporal aerial images containing RGB bands, bitemporal digital surface models (DSMs), and an old-map. The aerial images have two types of spatial resolutions, 12.5 cm or 16 cm, and the cell size of the DSMs is 50 cm × 50 cm. The bitemporal aerial images, the height variations calculated using the differences between the bitemporal DSMs, and the old-map were fed into a network architecture to build an automatic building change detection model. The performance of the model was quantitatively and qualitatively evaluated for an urban area that covered approximately 10 km2 and contained over 21,000 buildings. The results indicate that it can detect the building changes with optimum accuracy as compared to other methods that use inputs such as i) bitemporal aerial images only, ii) bitemporal aerial images and bitemporal DSMs, and iii) bitemporal aerial images and an old-map. The proposed method achieved recall rates of 89.3%, 88.8%, and 99.5% for new, demolished, and other buildings, respectively. The results also demonstrate that the old-map is an effective data source for increasing building change detection accuracy.

scholarly journals Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline

2021 ◽  
Vol 7 ◽  
pp. e638
Author(s):  
Md Nahidul Islam ◽  
Norizam Sulaiman ◽  
Fahmid Al Farid ◽  
Jia Uddin ◽  
Salem A. Alyami ◽  
...  
Keyword(s):  
Hearing Loss ◽  
Network Architecture ◽  
Auditory Evoked Potential ◽  
Human Communication ◽  
Time Frequency ◽  
The Neural Network ◽  
Cortex Area ◽  
Wide Range ◽  
Functional Reliability ◽  
Fully Connected

Hearing deficiency is the world’s most common sensation of impairment and impedes human communication and learning. Early and precise hearing diagnosis using electroencephalogram (EEG) is referred to as the optimum strategy to deal with this issue. Among a wide range of EEG control signals, the most relevant modality for hearing loss diagnosis is auditory evoked potential (AEP) which is produced in the brain’s cortex area through an auditory stimulus. This study aims to develop a robust intelligent auditory sensation system utilizing a pre-train deep learning framework by analyzing and evaluating the functional reliability of the hearing based on the AEP response. First, the raw AEP data is transformed into time-frequency images through the wavelet transformation. Then, lower-level functionality is eliminated using a pre-trained network. Here, an improved-VGG16 architecture has been designed based on removing some convolutional layers and adding new layers in the fully connected block. Subsequently, the higher levels of the neural network architecture are fine-tuned using the labelled time-frequency images. Finally, the proposed method’s performance has been validated by a reputed publicly available AEP dataset, recorded from sixteen subjects when they have heard specific auditory stimuli in the left or right ear. The proposed method outperforms the state-of-art studies by improving the classification accuracy to 96.87% (from 57.375%), which indicates that the proposed improved-VGG16 architecture can significantly deal with AEP response in early hearing loss diagnosis.

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