Computational Neuroscience is an emerging field that provides unique opportunities to study complex brain structures through realistic neural simulations. However, as biological details are added to models, the execution time for the simulation becomes longer. Graphics Processing Units (GPUs) are now being utilized to accelerate simulations due to their ability to perform computations in parallel. As such, they have shown significant improvement in execution time compared to Central Processing Units (CPUs). Most neural simulators utilize either multiple CPUs or a single GPU for better performance, but still show limitations in execution time when biological details are not sacrificed. Therefore, we present a novel CPU/GPU simulation environment for large-scale biological networks, the NeoCortical Simulator version 6 (NCS6). NCS6 is a free, open-source, parallelizable, and scalable simulator, designed to run on clusters of multiple machines, potentially with high performance computing devices in each of them. It has built-in leaky-integrate-and-fire (LIF) and Izhikevich (IZH) neuron models, but users also have the capability to design their own plug-in interface for different neuron types as desired. NCS6 is currently able to simulate one million cells and 100 million synapses in quasi real time by distributing data across eight machines with each having two video cards.
Advances in Virtual Reality (VR) technologies allow the investigation of simulated moral actions in visually immersive environments. Using a robotic manipulandum and an interactive sculpture, we now also incorporate realistic haptic feedback into virtual moral simulations. In two experiments, we found that participants responded with greater utilitarian actions in virtual and haptic environments when compared to traditional questionnaire assessments of moral judgments. In experiment one, when incorporating a robotic manipulandum, we found that the physical power of simulated utilitarian responses (calculated as the product of force and speed) was predicted by individual levels of psychopathy. In experiment two, which integrated an interactive and life-like sculpture of a human into a VR simulation, greater utilitarian actions continued to be observed. Together, these results support a disparity between simulated moral action and moral judgment. Overall this research combines state-of-the-art virtual reality, robotic movement simulations, and realistic human sculptures, to enhance moral paradigms that are often contextually impoverished. As such, this combination provides a better assessment of simulated moral action, and illustrates the embodied nature of morally-relevant actions.
- Proceedings of the National Academy of Sciences of the United States of America
- Published almost 3 years ago
When we grasp and manipulate an object, populations of tactile nerve fibers become activated and convey information about the shape, size, and texture of the object and its motion across the skin. The response properties of tactile fibers have been extensively characterized in single-unit recordings, yielding important insights into how individual fibers encode tactile information. A recurring finding in this extensive body of work is that stimulus information is distributed over many fibers. However, our understanding of population-level representations remains primitive. To fill this gap, we have developed a model to simulate the responses of all tactile fibers innervating the glabrous skin of the hand to any spatiotemporal stimulus applied to the skin. The model first reconstructs the stresses experienced by mechanoreceptors when the skin is deformed and then simulates the spiking response that would be produced in the nerve fiber innervating that receptor. By simulating skin deformations across the palmar surface of the hand and tiling it with receptors at their known densities, we reconstruct the responses of entire populations of nerve fibers. We show that the simulated responses closely match their measured counterparts, down to the precise timing of the evoked spikes, across a wide variety of experimental conditions sampled from the literature. We then conduct three virtual experiments to illustrate how the simulation can provide powerful insights into population coding in touch. Finally, we discuss how the model provides a means to establish naturalistic artificial touch in bionic hands.
This paper presents a real-time capable GPU-based ultrasound simulator suitable for medical education. The main focus of the simulator is to synthesize realistic looking ultrasound images in real-time including artifacts, which are essential for the interpretation of this data. The simulation is based on a convolution-enhanced ray-tracing approach and uses a deformable mesh model. Deformations of the mesh model are calculated using the PhysX engine. Our method advances the state of the art for real-time capable ultrasound simulators by following the path of the ultrasound pulse, which enables better simulation of ultrasound-specific artifacts. An evaluation of our proposed method in comparison with recent generative slicing-based strategies as well as real ultrasound images is performed. Hereby, a gelatin ultrasound phantom containing syringes filled with different media is scanned with a real transducer. The obtained images are then compared to images which are simulated using a slicing-based technique and our proposed method. The particular benefit of our method is the accurate simulation of ultrasound-specific artifacts, like range distortion, refraction and acoustic shadowing. Several test scenarios are evaluated regarding simulation time, to show the performance and the bottleneck of our method. While being computationally more intensive than slicing techniques, our simulator is able to produce high-quality images in real-time, tracing over 5000 rays through mesh models with more than 2 000 000 triangles of which up to 200 000 may be deformed each frame. Index Termsultrasound image simulation, medical image simulation, ray-tracing, sonography training, GPU.
We previously investigated novel therapies for pediatric ependymoma and found 5-fluorouracil (5-FU) i.v. bolus increased survival in a representative mouse model. However, without a quantitative framework to derive clinical dosing recommendations, we devised a translational pharmacokinetic-pharmacodynamic (PK-PD) modeling and simulation approach. Results from our preclinical PK-PD model suggested tumor concentrations exceeded the 1-hour target exposure (in vitro IC90), leading to tumor growth delay and increased survival. Using an adult population PK model, we scaled our preclinical PK-PD model to children. To select a 5-FU dosage for our clinical trial in children with ependymoma, we simulated various 5-FU dosages for tumor exposures and tumor growth inhibition, as well as considering tolerability to bolus 5-FU administration. We developed a pediatric population PK model of bolus 5-FU and simulated tumor exposures for our patients. Simulations for tumor concentrations indicated that all patients would be above the 1-hour target exposure for antitumor effect.
The purpose of this study is to conduct modeling and simulation to understand the effect of shock-induced mechanical loading, in the form of cavitation bubble collapse, on damage to the brain’s perineuronal nets (PNNs). It is known that high-energy implosion due to cavitation collapse is responsible for corrosion or surface damage in many mechanical devices. In this case, cavitation refers to the bubble created by pressure drop. The presence of a similar damage mechanism in biophysical systems has long being suspected but not well-explored. In this paper, we use reactive molecular dynamics (MD) to simulate the scenario of a shock wave induced cavitation collapse within the perineuronal net (PNN), which is the near-neuron domain of a brain’s extracellular matrix (ECM). Our model is focused on the damage in hyaluronan (HA), which is the main structural component of PNN. We have investigated the roles of cavitation bubble location, shockwave intensity and the size of a cavitation bubble on the structural evolution of PNN. Simulation results show that the localized supersonic water hammer created by an asymmetrical bubble collapse may break the hyaluronan. As such, the current study advances current knowledge and understanding of the connection between PNN damage and neurodegenerative disorders.
Stochastic simulations are one of the cornerstones of the analysis of dynamical processes on complex networks, and are often the only accessible way to explore their behavior. The development of fast algorithms is paramount to allow large-scale simulations. The Gillespie algorithm can be used for fast simulation of stochastic processes, and variants of it have been applied to simulate dynamical processes on static networks. However, its adaptation to temporal networks remains non-trivial. We here present a temporal Gillespie algorithm that solves this problem. Our method is applicable to general Poisson (constant-rate) processes on temporal networks, stochastically exact, and up to multiple orders of magnitude faster than traditional simulation schemes based on rejection sampling. We also show how it can be extended to simulate non-Markovian processes. The algorithm is easily applicable in practice, and as an illustration we detail how to simulate both Poissonian and non-Markovian models of epidemic spreading. Namely, we provide pseudocode and its implementation in C++ for simulating the paradigmatic Susceptible-Infected-Susceptible and Susceptible-Infected-Recovered models and a Susceptible-Infected-Recovered model with non-constant recovery rates. For empirical networks, the temporal Gillespie algorithm is here typically from 10 to 100 times faster than rejection sampling.
People frequently engage in future thinking in everyday life, but it is unknown how simulating an event in advance changes how that event is remembered once it takes place. To initiate study of this important topic, we conducted two experiments in which participants simulated emotional events before learning the hypothetical outcome of each event via narratives. Memory was assessed for emotional details contained in those narratives. Positive simulation resulted in a liberal response bias for positive information and a conservative bias for negative information. Events preceded by positive simulation were considered more favorably in retrospect. In contrast, negative simulation had no impact on subsequent memory. Results were similar across an immediate and delayed memory test and for past and future simulation. These results provide novel insights into the cognitive consequences of episodic future simulation and build on the optimism-bias literature by showing that adopting a favorable outlook results in a rosy memory.
Preferences and behavior are heavily influenced by one’s current visceral experience, yet people often fail to anticipate such effects. Although research suggests that this gap is difficult to overcome-to act as if in another visceral state-research on mental simulation has demonstrated that simulations can substitute for experiences, albeit to a weaker extent. We examine whether mentally simulating visceral states can impact preferences and behavior. We show that simulating a specific visceral state (e.g., being cold or hungry) shifts people’s preferences for relevant activities (Studies 1a-2) and choices of food portion sizes (Study 3). Like actual visceral experiences, mental simulation only affects people’s current preferences but not their general preferences (Study 4). Finally, people project simulated states onto similar others, as is the case for actual visceral experiences (Study 5). Thus, mental simulation may help people anticipate their own and others' future preferences, thereby improving their decision making.
Molecular simulations have become an essential tool for biochemical research. When they work properly, they are able to provide invaluable interpretations of experimental results and ultimately provide novel, experimentally testable predictions. Unfortunately, not all simulation models are created equal, and with inaccurate models it becomes unclear what is a bona fide prediction versus a simulation artifact. RNA models are still in their infancy compared to the many robust protein models that are widely in use, and for that reason the number of RNA force field revisions in recent years has been rapidly increasing. As there is no universally accepted ‘best’ RNA force field at the current time, RNA simulators must decide which one is most suited to their purposes, cognizant of its essential assumptions and their inherent strengths and weaknesses. Hopefully, armed with a better understanding of what goes inside the simulation ‘black box,’ RNA biochemists can devise novel experiments and provide crucial thermodynamic and structural data that will guide the development and testing of improved RNA models. For further resources related to this article, please visit the WIREs website.