Computational Modeling Work Research Articles

Automating literature screening and curation with applications to computational neuroscience.

ModelDB (https://modeldb.science) is a discovery platform for computational neuroscience, containing over 1850 published model codes with standardized metadata. These codes were mainly supplied from unsolicited model author submissions, but this approach is inherently limited. For example, we estimate we have captured only around one-third of NEURON models, the most common type of models in ModelDB. To more completely characterize the state of computational neuroscience modeling work, we aim to identify works containing results derived from computational neuroscience approaches and their standardized associated metadata (eg, cell types, research topics). Known computational neuroscience work from ModelDB and identified neuroscience work queried from PubMed were included in our study. After pre-screening with SPECTER2 (a free document embedding method), GPT-3.5, and GPT-4 were used to identify likely computational neuroscience work and relevant metadata. SPECTER2, GPT-4, and GPT-3.5 demonstrated varied but high abilities in identification of computational neuroscience work. GPT-4 achieved 96.9% accuracy and GPT-3.5 improved from 54.2% to 85.5% through instruction-tuning and Chain of Thought. GPT-4 also showed high potential in identifying relevant metadata annotations. Accuracy in identification and extraction might further be improved by dealing with ambiguity of what are computational elements, including more information from papers (eg, Methods section), improving prompts, etc. Natural language processing and large language model techniques can be added to ModelDB to facilitate further model discovery, and will contribute to a more standardized and comprehensive framework for establishing domain-specific resources.

Changes in Behavior and Neural Dynamics across Adolescent Development.

Adolescence is an important developmental period, during which substantial changes occur in brain function and behavior. Several aspects of executive function, including response inhibition, improve during this period. Correspondingly, structural imaging studies have documented consistent decreases in cortical and subcortical gray matter volume, and postmortem histologic studies have found substantial (∼40%) decreases in excitatory synapses in prefrontal cortex. Recent computational modeling work suggests that the change in synaptic density underlie improvements in task performance. These models also predict changes in neural dynamics related to the depth of attractor basins, where deeper basins can underlie better task performance. In this study, we analyzed task-related neural dynamics in a large cohort of longitudinally followed subjects (male and female) spanning early to late adolescence. We found that age correlated positively with behavioral performance in the Eriksen Flanker task. Older subjects were also characterized by deeper attractor basins around task related evoked EEG potentials during specific cognitive operations. Thus, consistent with computational models examining the effects of excitatory synaptic pruning, older adolescents showed stronger attractor dynamics during task performance.SIGNIFICANCE STATEMENT There are well-documented changes in brain and behavior during adolescent development. However, there are few mechanistic theories that link changes in the brain to changes in behavior. Here, we tested a hypothesis, put forward on the basis of computational modeling, that pruning of excitatory synapses in cortex during adolescence changes neural dynamics. We found, consistent with the hypothesis, that variability around event-related potentials shows faster decay dynamics in older adolescent subjects. The faster decay dynamics are consistent with the hypothesis that synaptic pruning during adolescent development leads to stronger attractor basins in task-related neural activity.

Multiscale Process Intensification of Waste Valorization Reactions.

ConspectusThe central theme of this Account is the development of intensified and sustainable chemical processes for the sequestration of CO2 in synergism with the utilization of wastes of industrial, urban, and agricultural origins. A challenge when working with solid waste-fluid reactions is that mass transfer limitations across solid-liquid, solid-gas, and gas-liquid interfaces and unfavorable thermodynamics lead to slow reaction rates, incomplete reaction conversions, high energy expenditure and processing costs, and inadequate product properties. The traditional macroscale approaches to overcoming slurry reaction limitations can be effective; however, they come at a cost to the environment. In the treatment or valorization of low-grade and waste resources, such conventional approaches are often unfeasible on an industrial scale. Sustainable solutions are thus needed.In the last six years, we have been exploring and developing approaches to overcoming reaction rate limitations of slurry reactions of environmental relevance by concurrently applying process intensification strategies and multiscale engineering approaches. The scientific approach has relied on laboratory-scale experiments to test and refine the devised multiscale process intensification strategies, with thermodynamic and computational modeling work supporting the experimental work and with advanced characterization techniques being used to elucidate reaction and transport mechanisms and aid the development of nanoscale reaction models and micro- and macroscale process models. The research streams, associated with the four key references, discussed next are (a) brine carbonation; (b) mineral carbonation and enhanced weathering; (c) process intensification and integration; and (d) characterization techniques.Within the four research streams, a number of mineral carbonation processes have been investigated and can be classified as (i) ambient weathering and carbonation; (ii) gas-(wet) solid accelerated carbonation; (iii) aqueous accelerated carbonation; (iv) supercritical accelerated carbonation; and (v) CO2 mineralization from brine. In some cases, the research was aimed at producing valuable products with reduced environmental risk or a reduced carbon footprint, such as an organomineral fertilizer and zeolites. In other cases, the aim was to assess the reactivity of minerals to match the right feedstock with the right carbonation process, in view of maximizing net carbon sequestration. There were also cases where the carbonation process was reimagined by the use of innovative reaction conditions, reactors, and reagents. The experience with accelerated weathering and carbonation in engineered processes has been translated into the field of enhanced rock weathering (ERW) in agriculture, where the multidisciplinary approach used has served to advance ERW science and technology in ways that have had a resounding effect on recent commercial deployment.The completed research serves to encourage the adoption of process intensification technologies in place of conventional processes, in industry and among the research community, and to catalyze the development of the types of sustainable processes required by the chemical, metallurgical, and minerals industries (which are critical to the green transition) to reduce their environmental impact and carbon emissions. Moreover, the multiscale process intensification approaches developed may also be extended to other industrial, urban, and agricultural processes where the reduction of energy intensity, carbon intensity, and environmental footprint could be achieved.

The Neural Correlates of Updating and Gating in Procedural Working Memory.

Goal-directed behavior relies on maintaining relevant goals in working memory (WM) and updating them when required. Computational modeling, behavioral, and neuroimaging work has previously identified the processes and brain regions involved in selecting, updating, and maintaining declarative information, such as letters and pictures. However, the neural substrates that underlie the analogous processes that operate on procedural information, namely, task goals, are currently unknown. Forty-three participants were therefore scanned with fMRI while performing a procedural version of the reference-back paradigm that allowed for the decomposition of WM updating processes into gate-opening, gate-closing, task switching, and task cue conflict components. Significant behavioral costs were observed for each of these components, with interactions indicating facilitation between gate-opening and task switching, and a modulation of cue conflict by gate state. In neural terms, opening the gate to procedural WM was associated with activity in medial pFC, posterior parietal cortex (PPC), the basal ganglia (BG), thalamus, and midbrain, but only when the task set needed to be updated. Closing the gate to procedural WM was associated with frontoparietal and BG activity specifically in conditions where conflicting task cues had to be ignored. Task switching was associated with activity in the medial pFC/ACC, PPC, and BG, whereas cue conflict was associated with PPC and BG activity during gate closing but was abolished when the gate was already closed. These results are discussed in relation to declarative WM and to gating models of WM.

Cell to network computational model of the epileptic human hippocampus suggests specific roles of network and channel dysfunctions in the ictal and interictal oscillations.

The mechanisms underlying the generation of hippocampal epileptic seizures and interictal events and their interactions with the sleep-wake cycle are not yet fully understood. Indeed, medial temporal lobe epilepsy is associated with hippocampal abnormalities both at the neuronal (channelopathies, impaired potassium and chloride dynamics) and network level (neuronal and axonal loss, mossy fiber sprouting), with more frequent seizures during wakefulness compared with slow-wave sleep. In this article, starting from our previous computational modeling work of the hippocampal formation based on realistic topology and synaptic connectivity, we study the role of micro- and mesoscale pathological conditions of the epileptic hippocampus in the generation and maintenance of seizure-like theta and interictal oscillations. We show, through the simulations of hippocampal activity during slow-wave sleep and wakefulness that: (i) both mossy fiber sprouting and sclerosis account for seizure-like theta activity, (ii) but they have antagonist effects (seizure-like activity occurrence increases with sprouting but decreases with sclerosis), (iii) though impaired potassium and chloride dynamics have little influence on the generation of seizure-like activity, they do play a role on the generation of interictal patterns, and (iv) seizure-like activity and fast ripples are more likely to occur during wakefulness and interictal spikes during sleep.

Silicon-Containing Complex II Acaricides─Design, Synthesis, and Pharmacological Optimization.

Bioisosteric replacement has been proven to be a powerful strategy in life science research. In this review, general aspects of carbon-silicon bioisosteric substitution and its applications in pharmaceutical and crop protection research are described. Carbon and their silicon analogues possess similar intrinsic properties. Replacing carbon with silicon in pharmaceuticals and pesticides has shown to result in positive effects on efficacy and selectivity, physicochemical properties, and bioavailability and also to eliminate or improve human or environmental safety properties as well as to provide novelty and new intellectual property in many cases. Furthermore, the application of carbon-silicon substitution in the search for new complex II acaricides is highlighted. This research led to the discovery of sila-cyflumetofen 23a and other silicon-containing analogues of cyflumetofen that match or exceed the acaricidal activity of cyflumetofen. The molecular design strategy, synthetic aspects, biological activity, computational modeling work, and structure-activity relationships will be discussed.

Computational Modeling Intervertebral Disc Pathophysiology: A Review

Lower back pain is a medical condition of epidemic proportion, and the degeneration of the intervertebral disc has been identified as a major contributor. The etiology of intervertebral disc (IVD) degeneration is multifactorial, depending on age, cell-mediated molecular degradation processes and genetics, which is accelerated by traumatic or gradual mechanical factors. The complexity of such intertwined biochemical and mechanical processes leading to degeneration makes it difficult to quantitatively identify cause–effect relationships through experiments. Computational modeling of the IVD is a powerful investigative tool since it offers the opportunity to vary, observe and isolate the effects of a wide range of phenomena involved in the degenerative process of discs. This review aims at discussing the main findings of finite element models of IVD pathophysiology with a special focus on the different factors contributing to physical changes typical of degenerative phenomena. Models presented are subdivided into those addressing role of nutritional supply, progressive biochemical alterations stemming from an imbalance between anabolic and catabolic processes, aging and those considering mechanical factors as the primary source that induces morphological change within the disc. Limitations of the current models, as well as opportunities for future computational modeling work are also discussed.

Effects of methylphenidate on reinforcement learning depend on working memory capacity

RationaleBrain catecholamines have long been implicated in reinforcement learning, exemplified by catecholamine drug and genetic effects on probabilistic reversal learning. However, the mechanisms underlying such effects are unclear.Objectives and methodsHere we investigated effects of an acute catecholamine challenge with methylphenidate (20 mg, oral) on a novel probabilistic reversal learning paradigm in a within-subject, double-blind randomised design. The paradigm was designed to disentangle effects on punishment avoidance from effects on reward perseveration. Given the known large individual variability in methylphenidate’s effects, we stratified our effects by working memory capacity and trait impulsivity, putatively modulating the effects of methylphenidate, in a large sample (n = 102) of healthy volunteers.ResultsContrary to our prediction, methylphenidate did not alter performance in the reversal phase of the task. Our key finding is that methylphenidate altered learning of choice-outcome contingencies in a manner that depended on individual variability in working memory span. Specifically, methylphenidate improved performance by adaptively reducing the effective learning rate in participants with higher working memory capacity.ConclusionsThis finding emphasises the important role of working memory in reinforcement learning, as reported in influential recent computational modelling and behavioural work, and highlights the dependence of this interplay on catecholaminergic function.

The N400 ERP component reflects an error-based implicit learning signal during language comprehension.

The functional significance of the N400 evoked-response component is still actively debated. An increasing amount of theoretical and computational modelling work is built on the interpretation of the N400 as a prediction error. In neural network modelling work, it was proposed that the N400 component can be interpreted as the change in a probabilistic representation of meaning that drives the continuous adaptation of an internal model of the statistics of the environment. These results imply that increased N400 amplitudes should correspond to greater adaptation, which can be measured via implicit memory. To investigate this model derived hypothesis, the current study manipulated expectancy in a sentence reading task to influence N400 amplitudes and subsequently presented the previously expected vs. unexpected words in a perceptual identification task to measure implicit memory. As predicted, reaction times in the perceptual identification task were significantly faster for previously unexpected words that induced larger N400 amplitudes in the previous sentence reading task. Additionally, it could be demonstrated that this adaptation seems to specifically depend on the process underlying N400 amplitudes, as participants with larger N400 differences during sentence reading also exhibited a larger implicit memory benefit in the perceptual identification task. These findings support the interpretation of the N400 as an implicit learning signal driving adaptation in language processing.

Evaluation of facial tissue stresses under medical devices post application of a cyanoacrylate liquid skin protectant: An integrated experimental-computational study.

Medical device‐related pressure ulcers (PUs) (injuries) are a subclass of PUs, associated with pressure and/or shear applied by a medical device onto the skin. Clinical application of a cyanoacrylate liquid skin protectant (CLSP) under the contours of skin‐contacting medical devices to shield an intact skin from the sustained mechanical loads that are applied by medical devices is a preventative option, but no computer modelling work has been reported to assess the biomechanical efficacy of such interventions. Here, we investigated the biomechanical protective effect of a polymerised cyanoacrylate coating using three‐dimensional, anatomically realistic finite element models of the ear with oxygen cannula and the mouth with endotracheal attachment device, informed by experimental studies. We have compared tissue stress exposures under the devices at these facial sites between conditions where the cyanoacrylate skin protectant has been applied or where the device was contacting the skin directly, without the shielding of the cyanoacrylate coating. The CLSP considerably reduced the skin stress concentration levels and overall tissue stress exposures under the aforementioned medical devices. This demonstrates strong biomechanical effectiveness of the studied cyanoacrylate‐based skin protectant in prevention of facial medical device‐related injuries at small, curved and thereby difficult to protect facial sites.

Trust Miscalibration Is Sometimes Necessary: An Empirical Study and a Computational Model

The literature on trust seems to have reached a consensus that appropriately calibrated trust in humans or machines is highly desirable; miscalibrated (i.e., over- or under-) trust has been thought to only have negative consequences (i.e., over-reliance or under-utilization). While not invalidating the general idea of trust calibration, a published computational cognitive model of trust in strategic interaction predicts that some local and temporary violations of the trust calibration principle are critical for sustained success in strategic situations characterized by interdependence and uncertainty (e.g., trust game, prisoner’s dilemma, and Hawk-dove). This paper presents empirical and computational modeling work aimed at testing the predictions of under- and over-trust in an extension of the trust game, the multi-arm trust game, that captures some important characteristics of real-world interpersonal and human-machine interactions, such as the ability to choose when and with whom to interact among multiple agents. As predicted by our previous model, we found that, under conditions of increased trust necessity, participants actively reconstructed their trust-investment portfolios by discounting their trust in their previously trusted counterparts and attempting to develop trust with the counterparts that they previously distrusted. We argue that studying these exceptions of the principle of trust calibration might be critical for understanding long-term trust calibration in dynamic environments.

Meeting energy needs of low-income Sub-Saharan households with a Partially Hybridised Solar Technology (PHST): Experimental performance evaluation based on simulated solar radiation and demand profiles

Renewable energy technologies for sustainable development are rapidly attracting attention across many disciplines despite technical, economic, and social barriers that limit application beyond the laboratory. This study proposes novel semi-automated and automated domestic hot water and electricity demand simulation experiments to evaluate the performance of a proof-of-concept prototype under simulated solar conditions. The prototype is a Partially Hybridised Solar Technology (PHST) which integrates photovoltaic (PV) and low-temperature solar thermal technology for low-cost electricity and domestic hot water supply. The domestic hot water and electrical demand profiles, and the solar radiation utilised during the study represent typical conditions of off-grid households in Sub-Saharan Africa. The prototype delivered a thermal energy supply potential of 2,073±75kJ per day at an average solar thermal conversion efficiency of 29.4±1.0%. The average yield of Direct Current (DC) electricity was 273 Wh per day at a corresponding PV module efficiency of 12.1% but depended on the type of charge controller. These results provide essential baselines for future computer modelling work and techno-economic predictions for Sub-Saharan Africa. The study has important future implications to test standards guiding laboratory-based evaluation of Solar Home Systems (SHSs) for electricity and domestic hot water.

The Best Laid Plans: Computational Principles of Anterior Cingulate Cortex.

Despite continual debate for the past 30 years about the function of anterior cingulate cortex (ACC), its key contribution to neurocognition remains unknown. However, recent computational modeling work has provided insight into this question. Here we review computational models that illustrate three core principles of ACC function, related to hierarchy, world models, and cost. We also discuss four constraints on the neural implementation of these principles, related to modularity, binding, encoding, and learning and regulation. These observations suggest a role for ACC in hierarchical model-based hierarchical reinforcement learning (HMB-HRL), which instantiates a mechanism motivating the execution of high-level plans.

Fgf10/Fgfr2b Signaling Orchestrates the Symphony of Molecular, Cellular, and Physical Processes Required for Harmonious Airway Branching Morphogenesis.

Airway branching morphogenesis depends on the intricate orchestration of numerous biological and physical factors connected across different spatial scales. One of the key regulatory pathways controlling airway branching is fibroblast growth factor 10 (Fgf10) signaling via its epithelial fibroblast growth factor receptor 2b (Fgfr2b). Fine reviews have been published on the molecular mechanisms, in general, involved in branching morphogenesis, including those mechanisms, in particular, connected to Fgf10/Fgfr2b signaling. However, a comprehensive review looking at all the major biological and physical factors involved in branching, at the different scales at which branching operates, and the known role of Fgf10/Fgfr2b therein, is missing. In the current review, we attempt to summarize the existing literature on airway branching morphogenesis by taking a broad approach. We focus on the biophysical and mechanical forces directly shaping epithelial bud initiation, branch elongation, and branch tip bifurcation. We then shift focus to more passive means by which branching proceeds, via extracellular matrix remodeling and the influence of the other pulmonary arborized networks: the vasculature and nerves. We end the review by briefly discussing work in computational modeling of airway branching. Throughout, we emphasize the known or speculative effects of Fgfr2b signaling at each point of discussion. It is our aim to promote an understanding of branching morphogenesis that captures the multi-scalar biological and physical nature of the phenomenon, and the interdisciplinary approach to its study.

Prevention of skin damage caused by the protective equipment used to mitigate COVID-19: monthly update.

Prevention of skin damage caused by the protective equipment used to mitigate COVID-19: monthly update.

Towards Replication in Computational Cognitive Modeling: a Machine Learning Perspective

The suggestions proposed by Lee et al. to improve cognitive modeling practices have significant parallels to the current best practices for improving reproducibility in the field of Machine Learning. In the current commentary on “robust modeling in cognitive science”, we highlight the practices that overlap and discuss how similar proposals have produced novel ongoing challenges, including cultural change towards open science, the scalability and interpretability of required practices, and the downstream effects of having robust practices that are fully transparent. Through this, we hope to inform future practices in computational modeling work with a broader scope.

Bacterial coping mechanisms for aging: using an individual-based model to study aging in biofilms

Aging can be defined as an accumulation of damage, or a loss of function, with increasing age. For bacteria, it has generally been assumed that the mechanism used to cope with aging is the asymmetric segregation of damage at division, so that all of the damage is inherited by one cell and the other is therefore rejuvenated. Another, often neglected, mechanism is to repair the damage; our previous computational modelling work has found that an optimized, fixed rate of repair is fitter than damage segregation in well-mixed environments such as chemostats. The predominant mode of growth for bacteria is in biofilms, however, and here we investigate aging in biofilms using the individual-based model iDynoMiCS. In addition to the previously used damage segregation and fixed repair strategies, we introduced adaptive repair: sensing the current damage levels within the cell and responding to this by investing in damage repair machinery. We found that the optimal method for dealing with cellular damage varies with the environment being investigated. The investment of additional resources into adaptive repair is only beneficial when competition is sufficiently strong, in the chemostat and in biofilms, and the speed at which the fittest strategy becomes apparent depends upon the initial density of cells. When the bacterial cells are dense initially, and thus the competition between strategies is stronger, the adaptive repair strategy emerges as the winner much more rapidly.

The attachment control system and computational modeling: Origins and prospects.

From his first attempts to explain attachment phenomena in the 1940s through his Attachment and Loss trilogy (Bowlby, 1969/1982, 1973, 1980), John Bowlby reformulated the theoretical underpinnings of attachment theory several times. He initially attempted to explain attachment phenomena in psychoanalytic terms. Then he invoked ethological theory in the explanation of how and why people behave as they do in close personal relationships. The mature theoretical framework that he presented between 1969 and 1982 in the attachment and loss trilogy retained strengths and insights, ultimately situating them within an overarching control systems framework. This article describes key stages in Bowlby's theoretical development, with particular emphasis placed on the emergence of control systems theory as a cornerstone of the mature theory. It also compares Bowlby's control systems approach to contemporary cognitive science approaches. It concludes by suggesting how Bowlby's control systems formulation could evolve along the path opened up by contemporary work in computational modeling and how it could benefit by doing so. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Chapter 7 - Dopamine and the motivation of cognitive control

The major ascending neuromodulator dopamine has long been implicated in cognitive control. Effects of dopamine-related disorders and the treatment of the cognitive control deficits associated with these disorders are commonly attributed to modulation of the prefrontal cortex. However, many disorders that are accompanied by cognitive control deficits also implicate abnormal dopamine transmission in the striatum, which has been associated more readily with value-based learning, choice, and motivation. We put forward the hypothesis that effects of dopamine on cognitive control reflect, in part, indirect modulation of value-based learning and choice computations that alter the motivation to exert control. This hypothesis is grounded in accumulating evidence from work with experimental animals as well as neurochemical PET, pharmacologic fMRI, and computational modeling work with healthy volunteers and patients with addictive disorders, ADHD, and Parkinson's disease. Consistent with an "inverted-U"-shaped relationship between dopamine and value-based learning, this evidence suggests that dopaminergic drugs might paradoxically increase our drive away from cognitive control in individuals with high baseline levels of dopamine, perhaps by "overdosing" dopamine levels, and thus reducing the value of cognitive control. The hypothesis has implications for the many dopamine-related disorders, which are often accompanied by either apathy or problems with impulse control, and their pharmacotreatment with dopaminergic drugs. For example, the cognitive deficits that are commonly associated with prefrontal cortex dysfunctioning might instead reflect modulation of striatal dopamine and its role in the willingness rather than the ability to exert control.

Adaptive sensory coding of gaze direction in schizophrenia.

Schizophrenia has been associated with differences in how the visual system processes sensory input. A fundamental mechanism that regulates sensory processing in the brain is gain control, whereby the responses of sensory neurons to a given stimulus are modulated in accordance with the spatial and temporal context. Some studies indicate an impairment of certain cortical gain control mechanisms in schizophrenia in low-level vision, reflected, for instance, in how the visual appearance of a stimulus is affected by the presence of other stimuli around it. In the present study, we investigated higher-level, social vision in schizophrenia, namely the perception of other people's direction of gaze (i.e. a type of face processing). Recent computational modelling work indicates that perceptual aftereffects—changes in perception that occur following repeated exposure to faces that display a specific direction of gaze—are indicative of two distinct forms of gain control involved in the coding of gaze direction across sensory neurons. We find that individuals with schizophrenia display strong perceptual aftereffects following repeated exposure to faces with averted gaze, and a modelling analysis indicates similarly robust gain control in the form of (i) short-term adjustment of channel sensitivities in response to the recent sensory history and (ii) divisive normalization of the encoded gaze direction. Together, this speaks to the typical coding of other people's direction of gaze in the visual system in schizophrenia, including flexible gain control, despite the social–cognitive impairments that can occur in this condition.