Deploying and Optimizing Embodied Simulations of Large-Scale Spiking Neural Networks on HPC Infrastructure

Simulating the brain-body-environment trinity in closed loop is an attractive proposal to investigate how perception, motor activity and interactions with the environment shape brain activity, and vice versa. The relevance of this embodied approach, however, hinges entirely on the modeled complexity of the various simulated phenomena. In this article, we introduce a software framework that is capable of simulating large-scale, biologically realistic networks of spiking neurons embodied in a biomechanically accurate musculoskeletal system that interacts with a physically realistic virtual environment. We deploy this framework on the high performance computing resources of the EBRAINS research infrastructure and we investigate the scaling performance by distributing computation across an increasing number of interconnected compute nodes. Our architecture is based on requested compute nodes as well as persistent virtual machines; this provides a high-performance simulation environment that is accessible to multi-domain users without expert knowledge, with a view to enable users to instantiate and control simulations at custom scale via a web-based graphical user interface. Our simulation environment, entirely open source, is based on the Neurorobotics Platform developed in the context of the Human Brain Project, and the NEST simulator. We characterize the capabilities of our parallelized architecture for large-scale embodied brain simulations through two benchmark experiments, by investigating the effects of scaling compute resources on performance defined in terms of experiment runtime, brain instantiation and simulation time. The first benchmark is based on a large-scale balanced network, while the second one is a multi-region embodied brain simulation consisting of more than a million neurons and a billion synapses. Both benchmarks clearly show how scaling compute resources improves the aforementioned performance metrics in a near-linear fashion. The second benchmark in particular is indicative of both the potential and limitations of a highly distributed simulation in terms of a trade-off between computation speed and resource cost. Our simulation architecture is being prepared to be accessible for everyone as an EBRAINS service, thereby offering a community-wide tool with a unique workflow that should provide momentum to the investigation of closed-loop embodiment within the computational neuroscience community.journal articl

QUALITATIVE AND QUANTITATIVE CHARACTERIZATION OF URBAN WASTEWATER IN ORDER TO ASSESS ITS PERFORMANCE (CASE STUDY: ILAM SEWAGE TREATMENT PLANT)

In this study, the efficiency of wastewater treatment plant of Ilam was investigated in a period of time. In order to measure the pollution indicators such as: temperature, Coliforms, turbidity, pH, COD, BOD, Cl-, TSS, TDS and DO, samples of the effluent sampling were done periodically. The results of this study show that measured values of operating parameters such as: pH, BOD, COD, NTU, DO (with average of: 8.34, 66.45, 106.4, 80.15, 7.4 mg/l) are consistent with EPA's standards in the field of re-use for agricultural purposes and irrigating, However, Chloride parameters and TSS with the averages of 60.7 and 455.7 (mg/l) do not comply. In addition COD, BOD, TSS and Chloride parameter analysis discharging effluent into surface water does not match to EPA's standards. An essential proceeding to reduce parameters, BOD, COD and TSS should be done

Sensitivity analysis of temperature and force in robotic bone drilling process using Sobol statistical method

The bone drilling process is indispensable in orthopaedic surgeries and treating bone breakages. It is also very important in dentistry and bone sampling operations. Bone is a very complex material and the process of drilling is very sensitive. Thus, bone drilling is one of the most important, common in the field of biomedical engineering. The bone drilling process can be promoted using automatic drilling machines and surgery-assisting robots. The problematic issue during operation is the high increase in drilling process temperature (higher than 47 °C) which leads to the so-called ‘thermal necrosis’ or cell death, and local burn in bone tissue. Furthermore, imposing higher forces to bone might yield to breaking or cracking, and consequently causes serious damages in bone. In this paper, the tool rotational speed, feed ratio and tool diameter were taken into account as process input parameters, and process temperature and thrust force were taken as output parameters. Design of experiments using response surface methodology was followed. Then, second linear governing equation was assigned to the model and its accuracy was evaluated. Later, Sobol statistical sensitivity analysis was used to ascertain the effect of process input parameters on process temperature and force. The results showed that among all effective input parameters, the tool rotational speed, feed rate and tool diameter have the highest influence respectively on process temperature and force. The behaviour of each output parameter with variation in each input parameter was further investigated

The Effectiveness of Mindfulness-Based Cognitive Therapy Group in Decreasing Depression and Increasing Quality of Life in Women with Addicted Husbands

Background & Objective: Addiction as a disease is a disadvantage that affects the quality of life of families and couples and may cause depression among other members of the family. The purpose of this study was to investigate the effectiveness of Mindfulness-Based Cognitive Therapy Group in decreasing depression and increasing the quality of life in women with addicted husbands. Material & Methods: The semi-experimental design was pre-test-posttest with control group. The statistical population included all women with addicted husbands referring to the comprehensive health center of Fasa into the experimental and control groups (15 women in each group). The experimental group received 8 sessions of mindfulness-based cognitive-group therapy, and no intervention was provided to the control group members. The research instrument questionnaire was Beck and Quality of Life. Results: The results showed that mindfulness-based cognitive-group therapy was effective in depression and quality of life of women with addicted husbands (P<0.001) and treatment reduced depression and increased the quality of life of women in post-test of the experimental group. Conclusion: The results show that Mindfulness-Based Cognitive Therapy Group is effective in decreasing depression and, consequently, increasing the quality of life of addicted spouse

The Psychometric Properties of the Persian Migraine-Specific Quality of Life Questionnaire Version 2.1 in Episodic and Chronic Migraines

Background. Migraine-specific quality of life (MSQ) is a valid and reliable questionnaire. Linguistic validation of Persian MSQ questionnaire, analysis of psychometric properties between chronic and episodic migraine patients, and capability of MSQ to differentiate between chronic and episodic migraines were the aims of this study. Method. Participants were selected from four different neurology clinics that were diagnosed as chronic or episodic migraine patients. Baseline data included information from MSQ v. 2.1, MIGSEV, SF-36, and symptoms questionnaire. At the third week from the baseline, participants filled out MSQ and MIGSEV. Internal consistency (Cronbach alpha) and test-retest reproducibility (intraclass correlation coefficients) were used to assess reliability. Convergent and discriminant validities were also assessed. Results. A total of 106 participants were enrolled. Internal consistencies of MSQ among all patients, chronic and episodic migraines, were 0.92, 0.91, and 0.92, respectively. Test-retest correlation of MSQ dimensions between visits 1 and 2 varied from 0.41 to 0.50. Convergent, item discriminant, and discriminant validities were approved. In all visits MSQ scores were lower in chronic migraine than episodic migraine; however, the difference was not statistically significant. Conclusion. Persian translation of MSQ is consistent with original version of MSQ in terms of psychometric properties in both chronic and episodic migraine patients

Deploying and Optimizing Embodied Simulations of Large-Scale Spiking Neural Networks on HPC Infrastructure

Simulating the brain-body-environment trinity in closed loop is an attractive proposal to investigate how perception, motor activity and interactions with the environment shape brain activity, and vice versa. The relevance of this embodied approach, however, hinges entirely on the modeled complexity of the various simulated phenomena. In this article, we introduce a software framework that is capable of simulating large-scale, biologically realistic networks of spiking neurons embodied in a biomechanically accurate musculoskeletal system that interacts with a physically realistic virtual environment. We deploy this framework on the high performance computing resources of the EBRAINS research infrastructure and we investigate the scaling performance by distributing computation across an increasing number of interconnected compute nodes. Our architecture is based on requested compute nodes as well as persistent virtual machines; this provides a high-performance simulation environment that is accessible to multi-domain users without expert knowledge, with a view to enable users to instantiate and control simulations at custom scale via a web-based graphical user interface. Our simulation environment, entirely open source, is based on the Neurorobotics Platform developed in the context of the Human Brain Project, and the NEST simulator. We characterize the capabilities of our parallelized architecture for large-scale embodied brain simulations through two benchmark experiments, by investigating the effects of scaling compute resources on performance defined in terms of experiment runtime, brain instantiation and simulation time. The first benchmark is based on a large-scale balanced network, while the second one is a multi-region embodied brain simulation consisting of more than a million neurons and a billion synapses. Both benchmarks clearly show how scaling compute resources improves the aforementioned performance metrics in a near-linear fashion. The second benchmark in particular is indicative of both the potential and limitations of a highly distributed simulation in terms of a trade-off between computation speed and resource cost. Our simulation architecture is being prepared to be accessible for everyone as an EBRAINS service, thereby offering a community-wide tool with a unique workflow that should provide momentum to the investigation of closed-loop embodiment within the computational neuroscience community