Conscious multisensory integration: Introducing a universal contextual field in biological and deep artificial neural networks

© 2020 The Authors. Published by Frontiers Media. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.3389/fncom.2020.00015Conscious awareness plays a major role in human cognition and adaptive behaviour, though its function in multisensory integration is not yet fully understood, hence, questions remain: How does the brain integrate the incoming multisensory signals with respect to different external environments? How are the roles of these multisensory signals defined to adhere to the anticipated behavioural-constraint of the environment? This work seeks to articulate a novel theory on conscious multisensory integration that addresses the aforementioned research challenges. Specifically, the well-established contextual field (CF) in pyramidal cells and coherent infomax theory [1][2] is split into two functionally distinctive integrated input fields: local contextual field (LCF) and universal contextual field (UCF). LCF defines the modulatory sensory signal coming from some other parts of the brain (in principle from anywhere in space-time) and UCF defines the outside environment and anticipated behaviour (based on past learning and reasoning). Both LCF and UCF are integrated with the receptive field (RF) to develop a new class of contextually-adaptive neuron (CAN), which adapts to changing environments. The proposed theory is evaluated using human contextual audio-visual (AV) speech modelling. Simulation results provide new insights into contextual modulation and selective multisensory information amplification/suppression. The central hypothesis reviewed here suggests that the pyramidal cell, in addition to the classical excitatory and inhibitory signals, receives LCF and UCF inputs. The UCF (as a steering force or tuner) plays a decisive role in precisely selecting whether to amplify/suppress the transmission of relevant/irrelevant feedforward signals, without changing the content e.g., which information is worth paying more attention to? This, as opposed to, unconditional excitatory and inhibitory activity in existing deep neural networks (DNNs), is called conditional amplification/suppression

Correlation of respiratory symptoms and spirometric lung patterns in a rural community setting, Sindh, Pakistan: A cross sectional survey

Background: Symptom-based questionnaires can be a cost effective tool enabling identification and diagnosis of patients with respiratory illnesses in resource limited setting. This study aimed to determine the correlation of respiratory symptoms and spirometric lung patterns and validity of ATS respiratory questionnaire in a rural community setting.Methods: This cross sectional survey was conducted between January - March 2009 on a sample of 200 adults selected from two villages of district Khairpur, Sindh, Pakistan. A modified version of the American thoracic society division of lung disease questionnaire was used to record the presence of respiratory symptoms. Predicted lung volumes i.e. forced vital capacity (FVC), forced expiratory volume in one second (FEV1) and their ratio (FEV1/FVC) were recorded using portable spirometer.Results: In the study sample there were 91 (45.5%) males and 109 (54.5%) females with overall mean age of 34 years (±11.69). Predominant respiratory symptom was phlegm (19%) followed by cough (17.5%), wheeze (14%) and dyspnea (10.5%). Prevalence of physician diagnosed and self-reported asthma was 5.5% and 9.5% respectively. Frequency of obstructive pattern on spirometry was 28.72% and that of restrictive pattern was 19.68%. After adjustment for age, gender, socioeconomic status, spoken dialect, education, smoking status, height, weight and arsenic in drinking water, FVC was significantly reduced for phlegm (OR 3.01; 95% CI: 1.14 - 7.94), wheeze (OR 7.22; 95% CI: 2.52 - 20.67) and shortness of breath (OR 4.91; 95% CI: 1.57 - 15.36); and FEV1 was significantly reduced for cough (OR 2.69; 95% CI: 1.12 - 6.43), phlegm (OR 3.01; 95% CI: 1.26 - 7.16) and wheeze (OR 10.77; 95% CI: 3.45 - 33.6). Presence of respiratory symptoms was significantly associated with restrictive and/or obstructive patterns after controlling for confounders. Similar findings were observed through linear regression where respiratory symptoms were found to be significantly associated with decrements in lung volumes. Specificity and positive predictive values were found to be higher for all the symptoms compared to sensitivity and negative predictive values.Conclusion: Symptoms based respiratory questionnaires are a valuable tool for screening of respiratory symptoms in resource poor, rural community setting

Research fatigue among injecting drug users in Karachi, Pakistan

Background Karachi is the largest metropolis of Pakistan and its economic hub attracting domestic migrants for economic opportunities. It is also the epicenter of HIV epidemic in the country. Since 2004, one pilot study and four behavioral and biological surveillance rounds have been conducted in Karachi. In addition many student research projects have also focused on key risk groups including injection drug users (IDUs). As a result of this extra ordinary exposure of same kind of questions, IDUs know how to respond to high value questions related to sharing of needles or unsafe sexual practices. The purpose of the study was to explore the element of research fatigue among IDUs in Karachi, Pakistan. Methods The study was conducted on 32 spots in Karachi, selected on the basis of estimate of IDUs at each spot. A trained field worker (recovered IDU) visited each spot; observed sharing behavior of IDUs and asked questions related to practices in January 2009. Verbal consent was obtained from each respondent before asking questions. Results On average 14 IDUs were present at each spot and out of 32 selected spots, 81% were active while more than two groups were present at 69% spots. In each group three to four IDUs were present and everyone in the group was sharing. One dose of injecting narcotics was observed. Sharing of syringes, needles and distilled water was observed at 63% spots while professional injector/street doctor was present at 60% spots. Conclusion There is a need to check internal consistency in surveillance research. It is highly likely that IDUs and other risk groups know how to respond to key questions but their responses do not match with the practices

Silicon-based carbonaceous electrocatalysts for oxygen reduction and evolution properties in alkaline conditions

In this contribution, new electrocatalyst materials, namely silicon-multiwalled carbon nanotubes (Si/MWCNTs), nitrogen-doped multiwalled carbon nano-tubes (Si/NCNTs), and silicon–carbon black (Si/CB), were developed and characterized in an effort to investigate less expensive and more efficient alternatives to Pt-based catalysis for energy storage cells applications. The role of structural behavior of obtained specimens and corresponding electrochemical performances were characterized through X-ray diffraction and scanning electron microscopy, while cyclic voltammetry and electrochemical impedance spectroscopy were analyzed for electrochemical measurements and evaluation of oxygen evolution reaction (OER) along with oxygen reduction reaction (ORR). The electrochemical studies have shown that these materials exhibit reasonable performance for both the ORR and the OER. The findings concluded that the Si/CB base catalyst has shown both OER and ORR activities in comparison to Si/MWCNTs and Si/NCNTs where only ORR performance was monitored. However, Si/NCNTs have shown much higher ORR activity compared to the others. This work highlights the comparison of three possible alternative materials as a potential catalyst to develop optimum alternatives of Pt-free catalysts for fuel cell and lithium-based battery systems

Deep Cognitive Neural Network (DCNN)

Embodiments of the present systems and methods may provide a more efficient and low-powered cognitive computational platform utilizing a deep cognitive neural network (DCNN), incorporating an architecture that integrates convolutional feedforward and recurrent networks , and replaces multi - layer perceptron (MLP) based sigmoidal neural structures with a queuing theory-driven design. For example, in an embodiment, a circuit may comprise a plurality of layers of neural network circuitry, each layer comprising a plurality of neuron circuits, each neuron comprising a plurality of computational circuits, and each neuron connected to a plurality of other neurons in the same layer by synapse circuitry, wherein the plurality of layers of neural network circuitry are adapted to process symbolic and conceptual information.United State