Signatures of hierarchical temporal processing in the mouse visual system.

A core challenge for the brain is to process information across various timescales. This could be achieved by a hierarchical organization of temporal processing through intrinsic mechanisms (e.g., recurrent coupling or adaptation), but recent evidence from spike recordings of the rodent visual system seems to conflict with this hypothesis. Here, we used an optimized information-theoretic and classical autocorrelation analysis to show that information- and correlation timescales of spiking activity increase along the anatomical hierarchy of the mouse visual system under visual stimulation, while information-theoretic predictability decreases. Moreover, intrinsic timescales for spontaneous activity displayed a similar hierarchy, whereas the hierarchy of predictability was stimulus-dependent. We could reproduce these observations in a basic recurrent network model with correlated sensory input. Our findings suggest that the rodent visual system employs intrinsic mechanisms to achieve longer integration for higher cortical areas, while simultaneously reducing predictability for an efficient neural code.

Everything is Infinite: Children's Beliefs About Endless Space, Time, and Number.

How do children form beliefs about the infinity of space, time, and number? We asked whether children held similar beliefs about infinity across domains, and whether beliefs in infinity for domains like space and time might be scaffolded upon numerical knowledge (e.g., knowledge successors within the count list). To test these questions, 112 U.S. children (aged 4;0-7;11) completed an interview regarding their beliefs about infinite space, time, and number. We also measured their knowledge of counting, and other factors that might impact performance on linguistic assessments of infinity belief (e.g., working memory, ability to respond to hypothetical questions). We found that beliefs about infinity were very high across all three domains, suggesting that infinity beliefs may arise early in development for space, time, and number. Second, we found that-across all three domains-children were more likely to believe that it is always possible to add a unit than to believe that the domain is endless. Finally, we found that understanding the rules underlying counting predicted children's belief that it is always possible to add 1 to any number, but did not predict any of the other elements of infinity belief.

Investigating the Face Inversion Effect in Autism Across Behavioral and Neural Measures of Face Processing: A Systematic Review and Bayesian Meta-Analysis.

Importance: Face processing is foundational to human social cognition, is central to the hallmark features of autism spectrum disorder (ASD), and shapes neural systems and social behavior. Highly efficient and specialized, the face processing system is sensitive to inversion, demonstrated by reduced accuracy in recognition and altered neural response to inverted faces. Understanding at which mechanistic level the autistic face processing system may be particularly different, as measured by the face inversion effect, will improve overall understanding of brain functioning in autism. Objective: To synthesize data from the extant literature to determine differences of the face processing system in ASD, as measured by the face inversion effect, across multiple mechanistic levels. Data Sources: Systematic searches were conducted in the MEDLINE, Embase, Web of Science, and PubMed databases from inception to August 11, 2022. Study Selection: Original research that reported performance-based measures of face recognition to upright and inverted faces in ASD and neurotypical samples were included for quantitative synthesis. All studies were screened by at least 2 reviewers. Data Extraction and Synthesis: This systematic review and meta-analysis was conducted according to the 2020 Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. Multiple effect sizes were extracted from studies to maximize information gain and statistical precision and used a random-effects, multilevel modeling framework to account for statistical dependencies within study samples. Main Outcomes and Measures: Effect sizes were calculated as a standardized mean change score between ASD and neurotypical samples (ie, Hedges g). The primary outcome measure was performance difference between upright and inverted faces during face recognition tasks. Measurement modality, psychological construct, recognition demand, sample age, sample sex distribution, and study quality assessment scores were assessed as moderators. Results: Of 1768 screened articles, 122 effect sizes from 38 empirical articles representing data from 1764 individual participants (899 ASD individuals and 865 neurotypical individuals) were included in the meta-analysis. Overall, face recognition performance differences between upright and inverted faces were reduced in autistic individuals compared with neurotypical individuals (g = -0.41; SE = 0.11; 95% credible interval [CrI], -0.63 to -0.18). However, there was considerable heterogeneity among effect sizes, which were explored with moderator analysis. The attenuated face inversion effect in autistic individuals was more prominent in emotion compared with identity recognition (b = 0.46; SE = 0.26; 95% CrI, -0.08 to 0.95) and in behavioral compared with electrophysiological measures (b = 0.23; SE = 0.24; 95% CrI, -0.25 to 0.70). Conclusions and Relevance: This study found that on average, face recognition in autism is less impacted by inversion. These findings suggest less specialization or expertise of the face processing system in autism, particularly in recognizing emotion from faces as measured in behavioral paradigms.

A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware.

Neuromorphic systems open up opportunities to enlarge the explorative space for computational research. However, it is often challenging to unite efficiency and usability. This work presents the software aspects of this endeavor for the BrainScaleS-2 system, a hybrid accelerated neuromorphic hardware architecture based on physical modeling. We introduce key aspects of the BrainScaleS-2 Operating System: experiment workflow, API layering, software design, and platform operation. We present use cases to discuss and derive requirements for the software and showcase the implementation. The focus lies on novel system and software features such as multi-compartmental neurons, fast re-configuration for hardware-in-the-loop training, applications for the embedded processors, the non-spiking operation mode, interactive platform access, and sustainable hardware/software co-development. Finally, we discuss further developments in terms of hardware scale-up, system usability, and efficiency.

The BrainScaleS-2 Accelerated Neuromorphic System With Hybrid Plasticity.

Since the beginning of information processing by electronic components, the nervous system has served as a metaphor for the organization of computational primitives. Brain-inspired computing today encompasses a class of approaches ranging from using novel nano-devices for computation to research into large-scale neuromorphic architectures, such as TrueNorth, SpiNNaker, BrainScaleS, Tianjic, and Loihi. While implementation details differ, spiking neural networks-sometimes referred to as the third generation of neural networks-are the common abstraction used to model computation with such systems. Here we describe the second generation of the BrainScaleS neuromorphic architecture, emphasizing applications enabled by this architecture. It combines a custom analog accelerator core supporting the accelerated physical emulation of bio-inspired spiking neural network primitives with a tightly coupled digital processor and a digital event-routing network.

Surrogate gradients for analog neuromorphic computing.

To rapidly process temporal information at a low metabolic cost, biological neurons integrate inputs as an analog sum, but communicate with spikes, binary events in time. Analog neuromorphic hardware uses the same principles to emulate spiking neural networks with exceptional energy efficiency. However, instantiating high-performing spiking networks on such hardware remains a significant challenge due to device mismatch and the lack of efficient training algorithms. Surrogate gradient learning has emerged as a promising training strategy for spiking networks, but its applicability for analog neuromorphic systems has not been demonstrated. Here, we demonstrate surrogate gradient learning on the BrainScaleS-2 analog neuromorphic system using an in-the-loop approach. We show that learning self-corrects for device mismatch, resulting in competitive spiking network performance on both vision and speech benchmarks. Our networks display sparse spiking activity with, on average, less than one spike per hidden neuron and input, perform inference at rates of up to 85,000 frames per second, and consume less than 200 mW. In summary, our work sets several benchmarks for low-energy spiking network processing on analog neuromorphic hardware and paves the way for future on-chip learning algorithms.

The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks.

Spiking neural networks are the basis of versatile and power-efficient information processing in the brain. Although we currently lack a detailed understanding of how these networks compute, recently developed optimization techniques allow us to instantiate increasingly complex functional spiking neural networks in-silico. These methods hold the promise to build more efficient non-von-Neumann computing hardware and will offer new vistas in the quest of unraveling brain circuit function. To accelerate the development of such methods, objective ways to compare their performance are indispensable. Presently, however, there are no widely accepted means for comparing the computational performance of spiking neural networks. To address this issue, we introduce two spike-based classification data sets, broadly applicable to benchmark both software and neuromorphic hardware implementations of spiking neural networks. To accomplish this, we developed a general audio-to-spiking conversion procedure inspired by neurophysiology. Furthermore, we applied this conversion to an existing and a novel speech data set. The latter is the free, high-fidelity, and word-level aligned Heidelberg digit data set that we created specifically for this study. By training a range of conventional and spiking classifiers, we show that leveraging spike timing information within these data sets is essential for good classification accuracy. These results serve as the first reference for future performance comparisons of spiking neural networks.

Structural plasticity on an accelerated analog neuromorphic hardware system.

In computational neuroscience, as well as in machine learning, neuromorphic devices promise an accelerated and scalable alternative to neural network simulations. Their neural connectivity and synaptic capacity depend on their specific design choices, but is always intrinsically limited. Here, we present a strategy to achieve structural plasticity that optimizes resource allocation under these constraints by constantly rewiring the pre- and postsynaptic partners while keeping the neuronal fan-in constant and the connectome sparse. In particular, we implemented this algorithm on the analog neuromorphic system BrainScaleS-2. It was executed on a custom embedded digital processor located on chip, accompanying the mixed-signal substrate of spiking neurons and synapse circuits. We evaluated our implementation in a simple supervised learning scenario, showing its ability to optimize the network topology with respect to the nature of its training data, as well as its overall computational efficiency.

Control of criticality and computation in spiking neuromorphic networks with plasticity.

The critical state is assumed to be optimal for any computation in recurrent neural networks, because criticality maximizes a number of abstract computational properties. We challenge this assumption by evaluating the performance of a spiking recurrent neural network on a set of tasks of varying complexity at - and away from critical network dynamics. To that end, we developed a plastic spiking network on a neuromorphic chip. We show that the distance to criticality can be easily adapted by changing the input strength, and then demonstrate a clear relation between criticality, task-performance and information-theoretic fingerprint. Whereas the information-theoretic measures all show that network capacity is maximal at criticality, only the complex tasks profit from criticality, whereas simple tasks suffer. Thereby, we challenge the general assumption that criticality would be beneficial for any task, and provide instead an understanding of how the collective network state should be tuned to task requirement.

Lack of Adoption of a Mobile App to Support Patient Self-Management of Diabetes and Hypertension in a Federally Qualified Health Center: Interview Analysis of Staff and Patients in a Failed Randomized Trial.

BACKGROUND: Thousands of mobile health (mHealth) apps have been developed to support patients' management of their health, but the effectiveness of many of the apps remains unclear. While mHealth apps appear to hold promise for improving the self-management of chronic conditions across populations, failure to balance the system demands of the app with the needs, interests, or resources of the end users can undermine consumers' adoption of these technologies. OBJECTIVE: The original aim of this study was to evaluate the effectiveness of a commercial mHealth app in improving clinical outcomes for adult patients in a Federally Qualified Health Center (FQHC) with uncontrolled diabetes and/or hypertension. Patients entered clinical data into the app, which also supported messaging between patients and providers. After a 4-month period of vigorous recruitment, the trial was suspended due to low enrollment and inconsistent use of the app by enrolled patients. The project aim was changed to understanding why the trial was unsuccessful. METHODS: We used the user-task-context (eUTC) usability framework to develop a set of interview questions for patients and staff who were involved in the trial. All interviews were done by phone and lasted 20 to 30 minutes. Interviews were not recorded. RESULTS: There was a poor fit between the app, end users, and recruitment and treatment approaches in our setting. Usability testing might have revealed this prior to launch but was not an option. There was not sufficient time during routine care for clinical staff to familiarize patients with the app or to check clinical data and messages, which are unreimbursed activities. Some patients did not use the app appropriately. The lack of integration with the electronic health record (EHR) was cited as a problem for both patients and staff who also said the app was just one more thing to attend to. CONCLUSIONS: This brief trial underscores the pitfalls in the utilization of mHealth apps. Effective use of mHealth tools requires a good fit between the app, the users' electronic health (eHealth) literacy, the treatment approach, staff time, and reimbursement for services. The last 3 are contextual factors of the setting that affected the adoption of the app and context is an important factor in implementation science. We recommend that researchers address contextual factors in the trial and adoption of mHealth technologies.

Age-Related Macular Degeneration and Incident Stroke: A Systematic Review and Meta-Analysis.

BACKGROUND: Age-related macular degeneration (AMD) is the leading cause of vision loss and blindness in people over 65 years old in the United States and has been associated with cardiovascular risk and decreased survival. There is conflicting data, however, regarding the contribution of AMD to the prediction of stroke. AIM: To determine whether AMD is a risk indicator for incident stroke in a meta-analysis of available prospective and retrospective cohort studies published in the English literature. METHODS: We performed a systematic literature search of all studies published in English with Pub Med and other databases from 1966 to August 2014, reporting stroke incidence in patients with macular degeneration. Two investigators independently extracted the data. A random effects model was used to report Odds ratios (OR), with corresponding 95% confidence intervals (CI). Meta-regression using a mixed linear model was used to understand potential heterogeneity amongst studies. RESULTS: We identified 9 studies that reported stroke incidence in patients with and without early AMD (N = 1,420,978). No significant association was found between early AMD with incident stroke. Combined, these 9 studies demonstrated random effects (OR, 1.12; CI, 0.86-1.47; I2 = 96%). Meta-regression on baseline covariates of age, sex, and year of publication did not significantly relate to heterogeneity. CONCLUSIONS: We found no significant relationship between AMD and incident stroke. Further studies are needed to clarify other causes of decreased survival in patients with AMD.

Impact of Antarctic Circumpolar Current development on late Paleogene ocean structure.

Global cooling and the development of continental-scale Antarctic glaciation occurred in the late middle Eocene to early Oligocene (~38 to 28 million years ago), accompanied by deep-ocean reorganization attributed to gradual Antarctic Circumpolar Current (ACC) development. Our benthic foraminiferal stable isotope comparisons show that a large δ(13)C offset developed between mid-depth (~600 meters) and deep (>1000 meters) western North Atlantic waters in the early Oligocene, indicating the development of intermediate-depth δ(13)C and O(2) minima closely linked in the modern ocean to northward incursion of Antarctic Intermediate Water. At the same time, the ocean's coldest waters became restricted to south of the ACC, probably forming a bottom-ocean layer, as in the modern ocean. We show that the modern four-layer ocean structure (surface, intermediate, deep, and bottom waters) developed during the early Oligocene as a consequence of the ACC.

Association of depressive symptoms and disease activity in children with asthma: methodological and clinical implications.

OBJECTIVE: This study was designed to assess the prevalence of depressive symptoms in children with asthma and the association between depression and asthma activity. METHOD: Children ages 7 to 17 (n = 129) were recruited from a hospital emergency department after presenting for asthma symptoms. The majority of subjects were from disadvantaged, inner city families. Subjects' asthma disease activity was assessed using the revised National Heart, Lung, and Blood Institute guidelines, and subjects' emotional status was assessed by a combination of self-, parent-, and clinician-reported measures. Parental emotional status was assessed by self-report. RESULTS: Depressive symptoms within the clinical range were reported in 26% of subjects and 43% of mothers, although symptom severity varied across scales. Self-reported depressive symptoms were more strongly correlated with asthma activity (r = 0.25) than clinician-reported (r = 0.14) or parent-reported symptoms (r = 0.12/0.18). Depressive symptoms in parents were correlated with child's depression scores but not with their asthma activity. CONCLUSIONS: Depressive symptoms were common and associated with asthma activity in this inner city population of asthmatic children. Self-reported depressive symptoms were more strongly associated with child's asthma activity than either parental depression or parental/clinician ratings of the child's depression.

The Phanerozoic record of global sea-level change.

We review Phanerozoic sea-level changes [543 million years ago (Ma) to the present] on various time scales and present a new sea-level record for the past 100 million years (My). Long-term sea level peaked at 100 +/- 50 meters during the Cretaceous, implying that ocean-crust production rates were much lower than previously inferred. Sea level mirrors oxygen isotope variations, reflecting ice-volume change on the 10(4)- to 10(6)-year scale, but a link between oxygen isotope and sea level on the 10(7)-year scale must be due to temperature changes that we attribute to tectonically controlled carbon dioxide variations. Sea-level change has influenced phytoplankton evolution, ocean chemistry, and the loci of carbonate, organic carbon, and siliciclastic sediment burial. Over the past 100 My, sea-level changes reflect global climate evolution from a time of ephemeral Antarctic ice sheets (100 to 33 Ma), through a time of large ice sheets primarily in Antarctica (33 to 2.5 Ma), to a world with large Antarctic and large, variable Northern Hemisphere ice sheets (2.5 Ma to the present).

The rise of oxygen over the past 205 million years and the evolution of large placental mammals.

On the basis of a carbon isotopic record of both marine carbonates and organic matter from the Triassic-Jurassic boundary to the present, we modeled oxygen concentrations over the past 205 million years. Our analysis indicates that atmospheric oxygen approximately doubled over this period, with relatively rapid increases in the early Jurassic and the Eocene. We suggest that the overall increase in oxygen, mediated by the formation of passive continental margins along the Atlantic Ocean during the opening phase of the current Wilson cycle, was a critical factor in the evolution, radiation, and subsequent increase in average size of placental mammals.

Mechanical effects of the extended trochanteric osteotomy.

BACKGROUND: The extended trochanteric osteotomy was introduced as a safe and effective exposure technique for revision hip surgery; however, intraoperative iatrogenic femoral fractures have been reported. This study examined the effects of the extended trochanteric osteotomy on the torsional strength of the femur with use of cadaver bones. We hypothesized that repair of the osteotomy fragment would restore the torsional strength to that of an intact femur and that an osteotomized femur containing a well-fixed stem would have the same torsional strength as an intact femur with a stem. METHODS: Fifty-eight cadaveric human femora were divided into five groups, according to the repair technique, to examine the effects of the extended trochanteric osteotomy: intact, osteotomy, repaired osteotomy, implant, and implant-repaired osteotomy. Osteotomy fragments were reattached with use of three double-looped 18-gauge wires. A femoral stem was cemented into the last two groups. Specimens were tested mechanically in rotation until failure. Rotational properties were compared with one-way analysis of variance followed by post hoc pairwise comparisons. Linear regression analysis was performed for bone mineral density and torsional strength. RESULTS: Torque to failure was reduced by 73% for the specimens in the osteotomy group compared with the intact group (p < 0.0001). Repair of the osteotomy did not improve torque to failure (p > 0.99). Femora in the implant-repaired osteotomy group displayed significantly improved torque-to-failure values compared with the specimens in the osteotomy and repaired osteotomy groups (p < 0.0001). However, the strength of the femora in the implant-repaired osteotomy group remained significantly less than that of the specimens in the implant group (p < 0.007). A significant linear relationship was observed between bone mineral density and torque to failure for femora in the intact (p < 0.006), osteotomy (p < 0.002), and repaired osteotomy (p < 0.001) groups. CONCLUSIONS: The extended trochanteric osteotomy reduces torsional strength by 73% even when the osteotomy fragment is repaired. Bone mineral density directly affects absolute femoral strength in this model.

Predicting length of stay of substance-using pregnant and postpartum women in day treatment.

Pregnant and postpartum substance-using women are a special population whose needs do not reflect those of the general substance-using communities. This study examined length of stay in a federally funded day treatment demonstration program in order to identify predictor variables that may help identify pregnant and postpartum substance-using women at high risk for dropping out of treatment. Variables from intake and exit questionnaires on a sample of 163 women were analyzed using multiple regression on both days in treatment and the logarithmic transformation of days in treatment. Few predictor variables were identified, although findings suggest that if a women is younger and self-referred, she may leave treatment sooner. As one of the first sets of published data on pregnant and postpartum women and retention in treatment, this study lays the groundwork for future research on the retention of pregnant and postpartum women in treatment, thereby facilitating the success of these women in overcoming their addiction.