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Global warming skeptics often fall back on the argument that the scientific case for global warming is all model predictions, nothing but simulation; they warn us that we need to wait for real data, \"sound science.\" In A Vast Machine Paul Edwards has news for these doubters: without models, there are no data. Today, no collection of signals or observations--even from satellites, which can \"see\" the whole planet with a single instrument--becomes global in time and space without passing through a series of data models. Everything we know about the world's climate we know through models. Edwards offers an engaging and innovative history of how scientists learned to understand the atmosphere--to measure it, trace its past, and model its future. Edwards argues that all our knowledge about climate change comes from three kinds of computer models: simulation models of weather and climate; reanalysis models, which recreate climate history from historical weather data; and data models, used to combine and adjust measurements from many different sources. Meteorology creates knowledge through an infrastructure (weather stations and other data platforms) that covers the whole world, making global data. This infrastructure generates information so vast in quantity and so diverse in quality and form that it can be understood only by computer analysis--making data global. Edwards describes the science behind the scientific consensus on climate change, arguing that over the years data and models have converged to create a stable, reliable, and trustworthy basis for the reality of global warming.

Successful behaviour depends on the right balance between maximising reward and soliciting information about the world. Here, we show how different types of information-gain emerge when casting behaviour as surprise minimisation. We present two distinct mechanisms for goal-directed exploration that express separable profiles of active sampling to reduce uncertainty. ‘Hidden state’ exploration motivates agents to sample unambiguous observations to accurately infer the (hidden) state of the world. Conversely, ‘model parameter’ exploration, compels agents to sample outcomes associated with high uncertainty, if they are informative for their representation of the task structure. We illustrate the emergence of these types of information-gain, termed active inference and active learning, and show how these forms of exploration induce distinct patterns of ‘Bayes-optimal’ behaviour. Our findings provide a computational framework for understanding how distinct levels of uncertainty systematically affect the exploration-exploitation trade-off in decision-making.

Inferring parameters of computational models that capture experimental data is a central task in cognitive neuroscience. Bayesian statistical inference methods usually require the ability to evaluate the likelihood of the model—however, for many models of interest in cognitive neuroscience, the associated likelihoods cannot be computed efficiently. Simulation-based inference (SBI) offers a solution to this problem by only requiring access to simulations produced by the model. Previously, Fengler et al. introduced likelihood approximation networks (LANs, Fengler et al., 2021) which make it possible to apply SBI to models of decision-making but require billions of simulations for training. Here, we provide a new SBI method that is substantially more simulation efficient. Our approach, mixed neural likelihood estimation (MNLE), trains neural density estimators on model simulations to emulate the simulator and is designed to capture both the continuous (e.g., reaction times) and discrete (choices) data of decision-making models. The likelihoods of the emulator can then be used to perform Bayesian parameter inference on experimental data using standard approximate inference methods like Markov Chain Monte Carlo sampling. We demonstrate MNLE on two variants of the drift-diffusion model and show that it is substantially more efficient than LANs: MNLE achieves similar likelihood accuracy with six orders of magnitude fewer training simulations and is significantly more accurate than LANs when both are trained with the same budget. Our approach enables researchers to perform SBI on custom-tailored models of decision-making, leading to fast iteration of model design for scientific discovery.

We have developed a simple approach to high-performance, stretchable, and foldable integrated circuits. The systems integrate inorganic electronic materials, including aligned arrays of nanoribbons of single crystalline silicon, with ultrathin plastic and elastomeric substrates. The designs combine multilayer neutral mechanical plane layouts and \"wavy\" structural configurations in silicon complementary logic gates, ring oscillators, and differential amplifiers. We performed three-dimensional analytical and computational modeling of the mechanics and the electronic behaviors of these integrated circuits. Collectively, the results represent routes to devices, such as personal health monitors and other biomedical devices, that require extreme mechanical deformations during installation/use and electronic properties approaching those of conventional systems built on brittle semiconductor wafers.

Comprehensively covers the basic principles and practice of Operational Modal Analysis (OMA). * Covers all important aspects that are needed to understand why OMA is a practical tool for modal testing * Covers advanced topics, including closely spaced modes, mode shape scaling, mode shape expansion and estimation of stress and strain in operational responses * Discusses practical applications of Operational Modal Analysis * Includes examples supported by MATLAB® applications * Accompanied by a website hosting a MATLAB® toolbox for Operational Modal Analysis

Non-invasive brain stimulation to target specific network activity patterns, e.g. transcranial alternating current stimulation (tACS), has become an essential tool to understand the causal role of neuronal oscillations in cognition and behavior. However, conventional sinusoidal tACS limits the ability to record neuronal activity during stimulation and lacks spatial focality. One particularly promising new tACS stimulation paradigm uses amplitude-modulated (AM) high-frequency waveforms (AM-tACS) with a slow signal envelope that may overcome the limitations. Moreover. AM-tACS using high-frequency carrier signals is more tolerable than conventional tACS, e.g. in terms of skin irritation and occurrence of phosphenes, when applied at the same current intensities (e.g. 1–2 mA). Yet, the fundamental mechanism of neuronal target-engagement by AM-tACS waveforms has remained unknown. We used a computational model of cortex to investigate how AM-tACS modulates endogenous oscillations and compared the target engagement mechanism to the case of conventional (unmodulated) low-frequency tACS. Analysis of stimulation amplitude and frequency indicated that cortical oscillations were phase-locked to the envelope of the AM stimulation signal, which thus exhibits the same target engagement mechanism as conventional (unmodulated) low frequency tACS. However, in the computational model substantially higher current intensities were needed for AM-tACS than for low-frequency (unmodulated) tACS waveforms to achieve pronounced phase synchronization. Our analysis of the carrier frequency suggests that there might be a trade-off between the use of high-frequency carriers and the stimulation amplitude required for successful entrainment. Together, our computational simulations support the use of slow-envelope high frequency carrier AM waveforms as a tool for noninvasive modulation of brain oscillations. More empirical data will be needed to identify the optimal stimulation parameters and to evaluate tolerability and safety of both, AM- and conventional tACS.

The age-related declines observed in scores on paired-associate-learning (PAL) tests are widely taken as support for the idea that human cognitive capacities decline across the life span. In a computational simulation, we showed that the patterns of change in PAL scores are actually predicted by the models that formalize the associative learning process in other areas of behavioral and neuroscientific research. These models also predict that manipulating language exposure can reproduce the experience-related performance differences erroneously attributed to age-related decline in agematched adults. Consistent with this, results showed that older bilinguals outperformed native speakers in a German PAL test, an advantage that increased with age. These analyses and results show that age-related PAL performance changes reflect the predictable effects of learning on the associability of test items, and indicate that failing to control for these effects is distorting the understanding of cognitive and brain development in adulthood.

The increasing interest in Virtual Reality (VR) as a tool for neuroscientific research contrasts with the current lack of established toolboxes and standards. In several recent studies, game engines like Unity or Unreal Engine were used. It remains to be tested whether these software packages provide sufficiently precise and accurate stimulus timing and time measurements that allow inferring ongoing mental and neural processes. We here investigated the precision and accuracy of the timing mechanisms of Unreal Engine 4 and SteamVR in combination with the HTC Vive VR system. In a first experiment, objective external measures revealed that stimulus durations were highly accurate. In contrast, in a second experiment, the assessment of the precision of built-in timing procedures revealed highly variable reaction time measurements and inaccurate determination of stimulus onsets. Hence, we developed a new software-based method that allows precise and accurate reaction time measurements with Unreal Engine and SteamVR. Instead of using the standard timing procedures implemented within Unreal Engine, time acquisition was outsourced to a background application. Timing benchmarks revealed that the newly developed method allows reaction time measurements with a precision and accuracy in the millisecond range. Overall, the present results indicate that the HTC Vive together with Unreal Engine and SteamVR can achieve high levels of precision and accuracy both concerning stimulus duration and critical time measurements. The latter can be achieved using a newly developed routine that allows not only accurate reaction time measures but also provides precise timing parameters that can be used in combination with time-sensitive functional measures such as electroencephalography (EEG) or transcranial magnetic stimulation (TMS).

Mechanics of Discontinua is the first book to comprehensively tackle both the theory ofthis rapidly developing topic and the applications that span a broad field of scientific and engineering disciplines, from traditional engineering to physics of particulates, nano-technology and micro-flows.