scholarly journals Rapid Recalibration of Peri-Personal Space: Psychophysical, Electrophysiological, and Neural Network Modeling Evidence

2020 ◽  
Vol 30 (9) ◽  
pp. 5088-5106 ◽  
Author(s):  
Jean-Paul Noel ◽  
Tommaso Bertoni ◽  
Emily Terrebonne ◽  
Elisa Pellencin ◽  
Bruno Herbelin ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Scale ◽  
Sensory Information ◽  
Personal Space ◽  
The Body ◽  
Neural Network Modeling ◽  
Visual Objects ◽  
Hebbian Plasticity ◽  
Sensory History ◽  
Adaptive Time

Abstract Interactions between individuals and the environment occur within the peri-personal space (PPS). The encoding of this space plastically adapts to bodily constraints and stimuli features. However, these remapping effects have not been demonstrated on an adaptive time-scale, trial-to-trial. Here, we test this idea first via a visuo-tactile reaction time (RT) paradigm in augmented reality where participants are asked to respond as fast as possible to touch, as visual objects approach them. Results demonstrate that RTs to touch are facilitated as a function of visual proximity, and the sigmoidal function describing this facilitation shifts closer to the body if the immediately precedent trial had indexed a smaller visuo-tactile disparity. Next, we derive the electroencephalographic correlates of PPS and demonstrate that this multisensory measure is equally shaped by recent sensory history. Finally, we demonstrate that a validated neural network model of PPS is able to account for the present results via a simple Hebbian plasticity rule. The present findings suggest that PPS encoding remaps on a very rapid time-scale and, more generally, that it is sensitive to sensory history, a key feature for any process contextualizing subsequent incoming sensory information (e.g., a Bayesian prior).

scholarly journals Rapid Recalibration of Peri-Personal Space; Psychophysical, Electrophysiological, and Neural Network Modeling Evidence

2019 ◽  
Author(s):  
Jean-Paul Noel ◽  
Tommaso Bertoni ◽  
Emily Terrebonne ◽  
Elisa Pellencin ◽  
Bruno Herbelin ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Scale ◽  
Reaction Times ◽  
Personal Space ◽  
The Body ◽  
Neural Network Modeling ◽  
Visual Object ◽  
Human Environment ◽  
Hebbian Plasticity ◽  
Sensory History

AbstractInteractions between individuals and the environment are mediated by the body and occur within the peri-personal space (PPS) – the space surrounding the body. The multisensory encoding of this space plastically adapts to different bodily constraints and stimuli features. However, these remapping effects have only been demonstrated on the time-scale of days, hours, or minutes. Yet, if PPS mediates human-environment interactions in an adaptive manner, its representation should be altered by sensory history on trial-to-trial timescale. Here we test this idea first via a visuo-tactile reaction time paradigm in augmented reality where participants are asked to respond as fast as possible to touch, as visual object approach them. Results demonstrate that reaction times to touch are facilitated as a function of visual proximity, and the sigmoidal function describing this facilitation shifts closer to the body if the immediately precedent trial had indexed a smaller visuo-tactile disparity (i.e., positive serial dependency). Next, we derive the electroencephalographic correlates of PPS and demonstrate that this measure is equally shaped by recent sensory history. Finally, we demonstrate that a validated neural network model of PPS is able to account for the present results via a simple Hebbian plasticity rule. The present findings suggest that PPS encoding remaps on a very rapid time-scale and is sensitive to recent sensory history.

DEVELOPMENT OF AN EXPERT SYSTEM FOR DIAGNOSTICS OF LUNG DISEASES BASED ON NEURAL NETWORK MODELING

2021 ◽  
pp. 155-159
Author(s):  
Екатерина Ивановна Новикова ◽  
Екатерина Александровна Андрианова ◽  
Елена Евгеньевна Удодова ◽  
Анастасия Юрьевна Корниенко ◽  
Александр Станиславович Панов
Keyword(s):  
Neural Network ◽  
Lung Diseases ◽  
Network Models ◽  
The Body ◽  
Neural Network Modeling ◽  
Diagnostic Study ◽  
Topological Groups ◽  
Social Significance ◽  
Neural Network Models ◽  
Information Process

В статье рассматриваются вопросы диагностики заболеваний легких, таких как кавернозный, инфильтративный, очаговой, диссеминированный туберкулез, онкология и пневмония. Медико-социальное значение болезней органов дыхания в современных условиях велико и определяется, прежде всего, их крайне высокой частотой среди различных контингентов населения. Учитывая значимость дыхания для организма, необходимо вовремя выявлять различные патологии и применять незамедлительные меры лечения. Одним из средств повышения эффективности диагностики данных патологий является автоматизация обработки диагностических данных с использованием современных технологий, а также создание компьютерной системы поддержки принятия решений, которая принимала бы во внимание большой объем диагностической информации и исключала ошибки субъективного характера. Выделение топологических групп по легочным заболеваниям проводилось с использованием самоорганизующихся карт Кохонена. По результатам классификации было проведено обучения нейронных сетей, используя алгоритм «многослойного персептрона» методом «обратного распространения», и получены математические модели. В медицинской практике постоянно следует учитывать то обстоятельство, что достоверные и адекватные медицинские данные, например, лабораторные анализы, результаты инструментального диагностического исследования, данные опроса больного или физикального исследования, потеряют свою актуальность, если информационный процесс длительно растянут по времени. Разработанные нейросетевые модели, были реализованы в информационно-программном обеспечении, которое позволит повысить эффективность процесса диагностики заболеваний легких The article deals with the diagnosis of lung diseases such as cavernous, infiltrative, focal, disseminated tuberculosis, oncology and pneumonia. The medical and social significance of respiratory diseases in modern conditions is great and is determined, first of all, by their extremely high frequency among various contingents of the population. Given the importance of breathing for the body, it is necessary to timely identify various pathologies and apply immediate treatment measures. One of the means of increasing the efficiency of diagnosing these pathologies is the automation of the processing of diagnostic data using modern technologies, as well as the creation of a computer decision support system that would take into account a large amount of diagnostic information and exclude subjective errors. The selection of topological groups for pulmonary diseases was carried out using self-organizing Kohonen maps. Based on the classification results, neural networks were trained using the "multilayer perceptron" algorithm by the "backpropagation" method and mathematical models were obtained. In medical practice, one should constantly take into account the fact that reliable and adequate medical data, for example, laboratory tests, the results of an instrumental diagnostic study, data from a patient survey or physical examination, will lose their relevance if the information process is prolonged for a long time. The developed neural network models were implemented in information and software that will improve the efficiency of the process of diagnosing lung diseases

scholarly journals Inflexible Updating of the Self-Other Divide During a Social Context in Autism; Psychophysical, Electrophysiological, and Neural Network Modeling Evidence

2020 ◽  
Author(s):  
Jean-Paul Noel ◽  
Renato Paredes ◽  
Emily Terrebonne ◽  
Jacob I. Feldman ◽  
Tiffany Woynaroski ◽  
...  
Keyword(s):  
Neural Network ◽  
Social Context ◽  
Statistical Inference ◽  
Personal Space ◽  
Autism Spectrum ◽  
Sensory Function ◽  
Neural Network Modeling ◽  
Self And Other ◽  
Neural Ensemble ◽  
Neural Gain

AbstractAutism spectrum disorder (ASD) is a heterogenous disorder predominantly characterized by social and communicative differences, but increasingly recognized to also alter (multi)sensory function. To face the heterogeneity and ubiquity of ASD, researchers have proposed models of statistical inference operating at the level of ‘computations’. Here, we attempt to bridge both across domains – from social to sensory – and levels of description – from behavioral computations to neural ensemble activity to a biologically-plausible artificial neural network – in furthering our understanding of autism. We do so by mapping visuo-tactile peri-personal space (PPS), and examining its electroencephalography (EEG) correlates, in individuals with ASD and neurotypical individuals during both a social and non-social context given that (i) the sensory coding of PPS is well understood, (ii) this space is thought to distinguish between self and other, and (iii) PPS is known to remap during social interactions. In contrast to their neurotypical counterparts, psychophysical and EEG evidence suggested that PPS does not remap in ASD during a social context. To account for this observation, we then employed a neural network model of PPS and demonstrate that PPS remapping may be driven by changes in neural gain operating at the level of multisensory neurons. Critically, under the anomalous excitation-inhibition (E/I) regime of ASD, this gain modulation does not result in PPS resizing. Overall, our findings are in line with recent statistical inference accounts suggesting diminished flexibility in ASD, and further these accounts by demonstrating within an example relevant for social cognition that such inflexibility may be due to E/I imbalances.

Neural Network Modeling of Maximum Insertion Force of Bevel-Tip Surgical Needle

2018 ◽  
Author(s):  
Sai Teja Reddy Gidde ◽  
Tololupe Verissimo ◽  
Nuo Chen ◽  
Parsaoran Hutapea ◽  
Byoung-gook Loh
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Modeling ◽  
The Body ◽  
Neural Network Modeling ◽  
Tissue Deformation ◽  
Input Output ◽  
Insertion Force ◽  
Tissue Phantom

Recently there has been a growing interest to develop innovative surgical needles for percutaneous interventional procedures. Needles are commonly used to reach target locations inside of the body for various medical interventions. The effectiveness of these procedures depends on the accuracy with which the needle tips reach the targets, such as a biopsy procedure to assess cancerous cells and tumors. One of the major issues in needle steering is the force during insertion, also known as the insertion (penetration) force. The insertion force causes tissue damage as well as tissue deformation. It has been well studied that tissue deformation causes the needle to deviate from its target thus causing an ineffective procedure. Simulation of surgical procedures provides an effective method for a robot-assisted surgery for pre- and intra-operative planning. Accurate modeling of the mechanical behavior on the interface of surgical needles and organs, specifically the insertion force, has been well recognized as a major challenge. Overcoming such obstacle by development of robust numerical models will enable realistic force feedback to the user during surgical simulation. This study investigates feasibility of predicting the insertion force of bevel-tip needles based on experimental data using neural network modeling. Simulation of the proposed neural network model is performed using Kera’s Python Deep Learning Library with TensorFlow as a backend. The insertion forces of needles with different bevel-tip angles in gel tissue phantom are measured using a specially designed automated needle insertion test setup. Input-output datasets are generated where the inputs are defined as bevel-tip angles and gel tissue phantom stiffness, and the output is defined as the insertion force. A properly trained neural network then maps the input data to the output data and the input-output dataset is supplied to train a neural network. Its performance is then evaluated using different and unseen input-output dataset. This paper shows that the proposed neural network model accurately predicts the insertion force.

scholarly journals Neural adaptation accounts for the dynamic resizing of peripersonal space: evidence from a psychophysical-computational approach

2018 ◽  
Vol 119 (6) ◽  
pp. 2307-2333 ◽  
Author(s):  
Jean-Paul Noel ◽  
Olaf Blanke ◽  
Elisa Magosso ◽  
Andrea Serino
Keyword(s):  
Neural Network ◽  
Dynamic Properties ◽  
Peripersonal Space ◽  
The Body ◽  
Neural Adaptation ◽  
Neural Network Modeling ◽  
Adaptation Mechanism ◽  
Stimulus Velocity ◽  
Trunk Space ◽  
Psychophysical Approach

Interactions between the body and the environment occur within the peripersonal space (PPS), the space immediately surrounding the body. The PPS is encoded by multisensory (audio-tactile, visual-tactile) neurons that possess receptive fields (RFs) anchored on the body and restricted in depth. The extension in depth of PPS neurons’ RFs has been documented to change dynamically as a function of the velocity of incoming stimuli, but the underlying neural mechanisms are still unknown. Here, by integrating a psychophysical approach with neural network modeling, we propose a mechanistic explanation behind this inherent dynamic property of PPS. We psychophysically mapped the size of participant’s peri-face and peri-trunk space as a function of the velocity of task-irrelevant approaching auditory stimuli. Findings indicated that the peri-trunk space was larger than the peri-face space, and, importantly, as for the neurophysiological delineation of RFs, both of these representations enlarged as the velocity of incoming sound increased. We propose a neural network model to mechanistically interpret these findings: the network includes reciprocal connections between unisensory areas and higher order multisensory neurons, and it implements neural adaptation to persistent stimulation as a mechanism sensitive to stimulus velocity. The network was capable of replicating the behavioral observations of PPS size remapping and relates behavioral proxies of PPS size to neurophysiological measures of multisensory neurons’ RF size. We propose that a biologically plausible neural adaptation mechanism embedded within the network encoding for PPS can be responsible for the dynamic alterations in PPS size as a function of the velocity of incoming stimuli. NEW & NOTEWORTHY Interactions between body and environment occur within the peripersonal space (PPS). PPS neurons are highly dynamic, adapting online as a function of body-object interactions. The mechanistic underpinning PPS dynamic properties are unexplained. We demonstrate with a psychophysical approach that PPS enlarges as incoming stimulus velocity increases, efficiently preventing contacts with faster approaching objects. We present a neurocomputational model of multisensory PPS implementing neural adaptation to persistent stimulation to propose a neurophysiological mechanism underlying this effect.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

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