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Semiclassical propagation up to the Heisenberg time Superconductivity and magnetic order in the half-Heusler compound ErPdBi An experimental evidence-based computational paradigm for new logic-gates in neuronal activity Universality in the symmetric exclusion process and diffusive systems [ABSTRACT FROM AUTHOR]

The widespread sharing of information on the Internet has given rise to ecological studies that use data from digital sources including digitized museum records and social media posts. Most of these studies have focused on understanding species occurrences and distributions. In this essay, we argue that data from digital sources also offer many opportunities to study animal behavior including long-term and large-scale comparisons within and between species. Following Nikko Tinbergen's classical roadmap for behavioral investigation, we show how using videos, photos, text, and audio posted on social media and other digital platforms can shed new light on known behaviors, particularly in a changing world, and lead to the discovery of new ones., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Vardi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The first target of the Convention for Biological Diversity (Aichi target 1) was to increase public awareness of the values of biodiversity and actions needed to conserve it-a key prerequisite for other conservation targets. Monitoring success in achieving this target at a global scale has been difficult; however, increased digitization of human life in recent decades has made it easier to measure people's interests at an unprecedented scale and allows for a more comprehensive evaluation of Aichi target 1 than previously attempted. We used Google search volume data for over a thousand search terms related to different aspects of biodiversity and conservation to evaluate global interest in biodiversity and its conservation. We also investigated the correlation of interest in biodiversity and conservation across countries to variables related to biodiversity, economy, demography, research, education, internet use, and presence of environmental organizations. From 2013 to 2020, global searches for biodiversity components increased, driven mostly by searches for charismatic fauna (59% of searches were for mammal species). Searches for conservation actions, driven mostly by searches for national parks, decreased since 2019, likely due to the COVID-19 pandemic. Economic inequality was negatively correlated with interest in biodiversity and conservation, whereas purchasing power was indirectly positively correlated with higher levels of education and research. Our results suggest partial success toward achieving Aichi target 1 in that interest in biodiversity increased widely, but not for conservation. We suggest that increased outreach and education efforts aimed at neglected aspects of biodiversity and conservation are still needed. Popular topics in biodiversity and conservation could be leveraged to increase awareness of other topics with attention to local socioeconomic contexts., (© 2023 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.)

Learning classification tasks of [Formula: see text] inputs typically consist of [Formula: see text]) max-pooling (MP) operators along the entire feedforward deep architecture. Here we show, using the CIFAR-10 database, that pooling decisions adjacent to the last convolutional layer significantly enhance accuracies. In particular, average accuracies of the advanced-VGG with [Formula: see text] layers (A-VGGm) architectures are 0.936, 0.940, 0.954, 0.955, and 0.955 for m = 6, 8, 14, 13, and 16, respectively. The results indicate A-VGG8's accuracy is superior to VGG16's, and that the accuracies of A-VGG13 and A-VGG16 are equal, and comparable to that of Wide-ResNet16. In addition, replacing the three fully connected (FC) layers with one FC layer, A-VGG6 and A-VGG14, or with several linear activation FC layers, yielded similar accuracies. These significantly enhanced accuracies stem from training the most influential input-output routes, in comparison to the inferior routes selected following multiple MP decisions along the deep architecture. In addition, accuracies are sensitive to the order of the non-commutative MP and average pooling operators adjacent to the output layer, varying the number and location of training routes. The results call for the reexamination of previously proposed deep architectures and their accuracies by utilizing the proposed pooling strategy adjacent to the output layer., (© 2023. Springer Nature Limited.)

Purpose: To assess the association of a novel spectral domain optical coherence tomography biomarker with 6-month visual acuity in in the Study of COmparative Treatments for REtinal Vein Occlusion 2., Methods: Spectral domain optical coherence tomography volume scans were evaluated for inner retinal hyperreflectivity, quantified by optical intensity ratio (OIR) and OIR variation. Baseline visual acuity letter score (VALS), baseline OCT biomarkers, and month 1 OIR were correlated with VALS at month 6. Regression trees, a machine learning technique yielding readily interpretable models, were used to assess for variable interaction., Results: Only baseline VALS correlated positively with month 6 VALS in multivariate regression. Regression trees detected a novel functional and anatomical interaction in a subgroup. Among patients with a baseline VALS worse than 43, those with an OIR variation at month 1 of more than 0.09 had a mean of 13 fewer letters of vision at 6 months compared with patients with an OIR variation of 0.09 or less., Conclusions: Baseline VALS was the strongest predictor of month 6 VALS. Regression tree analysis detected an interaction effect, in which higher OIR variation at month 1 predicted worse 6-month VALS in patients with low VALS at baseline. OIR variation may serve as a predictor for poor visual outcome despite treatment of macular edema secondary to retinal vein occlusion in patients with poor vision at baseline., Translational Relevance: Pixel heterogeneity in three-dimensional OCT data may serve as measure of disruption of the retinal laminations, and this factor may carry visually prognostic value.

Real-time sequence identification is a core use-case of artificial neural networks (ANNs), ranging from recognizing temporal events to identifying verification codes. Existing methods apply recurrent neural networks, which suffer from training difficulties; however, performing this function without feedback loops remains a challenge. Here, we present an experimental neuronal long-term plasticity mechanism for high-precision feedforward sequence identification networks (ID-nets) without feedback loops, wherein input objects have a given order and timing. This mechanism temporarily silences neurons following their recent spiking activity. Therefore, transitory objects act on different dynamically created feedforward sub-networks. ID-nets are demonstrated to reliably identify 10 handwritten digit sequences, and are generalized to deep convolutional ANNs with continuous activation nodes trained on image sequences. Counterintuitively, their classification performance, even with a limited number of training examples, is high for sequences but low for individual objects. ID-nets are also implemented for writer-dependent recognition, and suggested as a cryptographic tool for encrypted authentication. The presented mechanism opens new horizons for advanced ANN algorithms., (© 2022. The Author(s).)

Synaptic plasticity is a long-lasting core hypothesis of brain learning that suggests local adaptation between two connecting neurons and forms the foundation of machine learning. The main complexity of synaptic plasticity is that synapses and dendrites connect neurons in series and existing experiments cannot pinpoint the significant imprinted adaptation location. We showed efficient backpropagation and Hebbian learning on dendritic trees, inspired by experimental-based evidence, for sub-dendritic adaptation and its nonlinear amplification. It has proven to achieve success rates approaching unity for handwritten digits recognition, indicating realization of deep learning even by a single dendrite or neuron. Additionally, dendritic amplification practically generates an exponential number of input crosses, higher-order interactions, with the number of inputs, which enhance success rates. However, direct implementation of a large number of the cross weights and their exhaustive manipulation independently is beyond existing and anticipated computational power. Hence, a new type of nonlinear adaptive dendritic hardware for imitating dendritic learning and estimating the computational capability of the brain must be built., (© 2022. The Author(s).)

Refractoriness is a fundamental property of excitable elements, such as neurons, indicating the probability for re-excitation in a given time lag, and is typically linked to the neuronal hyperpolarization following an evoked spike. Here we measured the refractory periods (RPs) in neuronal cultures and observed that an average anisotropic absolute RP could exceed 10 ms and its tail is 20 ms, independent of a large stimulation frequency range. It is an order of magnitude longer than anticipated and comparable with the decaying membrane potential time scale. It is followed by a sharp rise-time (relative RP) of merely ∼1 md to complete responsiveness. Extracellular stimulations result in longer absolute RPs than solely intracellular ones, and a pair of extracellular stimulations from two different routes exhibits distinct absolute RPs, depending on their order. Our results indicate that a neuron is an accurate excitable element, where the diverse RPs cannot be attributed solely to the soma and imply fast mutual interactions between different stimulation routes and dendrites. Further elucidation of neuronal computational capabilities and their interplay with adaptation mechanisms is warranted.

The global lockdown to mitigate COVID-19 pandemic health risks has altered human interactions with nature. Here, we report immediate impacts of changes in human activities on wildlife and environmental threats during the early lockdown months of 2020, based on 877 qualitative reports and 332 quantitative assessments from 89 different studies. Hundreds of reports of unusual species observations from around the world suggest that animals quickly responded to the reductions in human presence. However, negative effects of lockdown on conservation also emerged, as confinement resulted in some park officials being unable to perform conservation, restoration and enforcement tasks, resulting in local increases in illegal activities such as hunting. Overall, there is a complex mixture of positive and negative effects of the pandemic lockdown on nature, all of which have the potential to lead to cascading responses which in turn impact wildlife and nature conservation. While the net effect of the lockdown will need to be assessed over years as data becomes available and persistent effects emerge, immediate responses were detected across the world. Thus, initial qualitative and quantitative data arising from this serendipitous global quasi-experimental perturbation highlights the dual role that humans play in threatening and protecting species and ecosystems. Pathways to favorably tilt this delicate balance include reducing impacts and increasing conservation effectiveness., Competing Interests: Authors declare no competing interests., (© 2021 Published by Elsevier Ltd.)

Ongoing loss of biological diversity is primarily the result of unsustainable human behavior. Thus, the long-term success of biodiversity conservation depends on a thorough understanding of human-nature interactions. Such interactions are ubiquitous but vary greatly in time and space and are difficult to monitor efficiently at large spatial scales. However, the Information Age also provides new opportunities to better understand human-nature interactions because many aspects of daily life are recorded in a variety of digital formats. The emerging field of conservation culturomics aims to take advantage of digital data sources and methods to study human-nature interactions and thus to provide new tools for studying conservation at relevant temporal and spatial scales. Nevertheless, technical challenges associated with the identification, access, and analysis of relevant data hamper the wider adoption of culturomics methods. To help overcome these barriers, we propose a conservation culturomics research framework that addresses data acquisition, analysis, and inherent biases. The main sources of culturomic data include web pages, social media, and other digital platforms from which metrics of content and engagement can be obtained. Obtaining raw data from these platforms is usually desirable but requires careful consideration of how to access, store, and prepare the data for analysis. Methods for data analysis include network approaches to explore connections between topics, time-series analysis for temporal data, and spatial modeling to highlight spatial patterns. Outstanding challenges associated with culturomics research include issues of interdisciplinarity, ethics, data biases, and validation. The practical guidance we offer will help conservation researchers and practitioners identify and obtain the necessary data and carry out appropriate analyses for their specific questions, thus facilitating the wider adoption of culturomics approaches for conservation applications., (© 2021 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.)

Culturomic tools enable the exploration of trends in human-nature interactions, although they entail inherent biases and necessitate careful validation. Furthermore, people may engage with nature across different culturomic data sets differently. We evaluated people's digital interest and engagement with plant species based on Wikipedia and Google data and explored the conservation implications of these temporal interest patterns. As a case study, we explored the digital footprints of the most popular plant species in Israel. We analyzed 4 years of daily page views from Hebrew Wikipedia and 10 years of daily Google search volume in Israel. We modeled popularity of plant species in these 2 data sets based on a suite of plant attributes. We further explored the seasonal trends of people's interest in each species. We found differences in how people interacted digitally with plants in Wikipedia and Google. Overall, in Google, searches for species that have utility to humans were more common, whereas in Wikipedia, plants that serve as cultural emblems received more attention. Furthermore, in Google, popular species attracted more attention over time, opposite to the trend in Wikipedia. In Google, interest in species with short bloom duration exhibited more pronounced seasonal patterns, whereas in Wikipedia, seasonality of interest increased as bloom duration increased. Together, our results suggest that people's digital interactions with nature may be inherently different depending on the sources explored, which may affect use of this information for conservation. Although culturomics holds much promise, better understanding of its underpinnings is important when translating insights into conservation actions., (© 2021 Society for Conservation Biology.)

Restricted human activity during the COVID-19 pandemic raised global attention to the presence of wildlife in cities. Here, we analyzed iNaturalist observations of prominent wildlife species around North-American urban centers, before and during the COVID-19 pandemic outbreak. We suggest that the popular notion of 'wildlife reclaiming cities' may have been exaggerated. We found that while pumas ventured deeper into urban habitats during the COVID-19 pandemic, bears, bobcats, coyotes, and moose did not. Species differential behavioral responses may highlight their evolutionary history cohabiting human habitats. Nevertheless, our results highlight the importance of urban nature for people during the pandemic. Our insights could help manage urban wildlife, better plan greenspaces, and promote positive nature engagements., Competing Interests: There is no conflict of interest to declare., (© 2021 Elsevier Ltd. All rights reserved.)

Intraspecific interactions among predators can change the game between a predator and its prey. Individuals of different size or sex can differ in their responses to conspecific competitors. We studied intraspecific interactions among pairs of little egrets (Egretta garzetta) while foraging on responsive prey (comet goldfish, Carassius auratus). Testing little egrets in an artificial patchy environment both singly and while engaged in social forging in pairs (male & female) at two prey densities, allowed us to explore individual differences in foraging success. We found sexual dimorphism with males being bigger and more aggressive than females. However, female foraging success was positively affected by the time they spent foraging with a conspecific male, suggesting they might be able to mitigate male aggressiveness with an indirect positive interaction. Despite the presence of direct interactions between individuals in the pair, egret foraging success was not affected by such interactions, nor by prey density. Our results demonstrate the importance of sex and an individual's ability to adjust its social behaviour based on the behaviour of others in this predator-prey foraging game., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Attempting to imitate the brain's functionalities, researchers have bridged between neuroscience and artificial intelligence for decades; however, experimental neuroscience has not directly advanced the field of machine learning (ML). Here, using neuronal cultures, we demonstrate that increased training frequency accelerates the neuronal adaptation processes. This mechanism was implemented on artificial neural networks, where a local learning step-size increases for coherent consecutive learning steps, and tested on a simple dataset of handwritten digits, MNIST. Based on our on-line learning results with a few handwriting examples, success rates for brain-inspired algorithms substantially outperform the commonly used ML algorithms. We speculate this emerging bridge from slow brain function to ML will promote ultrafast decision making under limited examples, which is the reality in many aspects of human activity, robotic control, and network optimization.

Recently, deep learning algorithms have outperformed human experts in various tasks across several domains; however, their characteristics are distant from current knowledge of neuroscience. The simulation results of biological learning algorithms presented herein outperform state-of-the-art optimal learning curves in supervised learning of feedforward networks. The biological learning algorithms comprise asynchronous input signals with decaying input summation, weights adaptation, and multiple outputs for an input signal. In particular, the generalization error for such biological perceptrons decreases rapidly with increasing number of examples, and it is independent of the size of the input. This is achieved using either synaptic learning, or solely through dendritic adaptation with a mechanism of swinging between reflecting boundaries, without learning steps. The proposed biological learning algorithms outperform the optimal scaling of the learning curve in a traditional perceptron. It also results in a considerable robustness to disparity between weights of two networks with very similar outputs in biological supervised learning scenarios. The simulation results indicate the potency of neurobiological mechanisms and open opportunities for developing a superior class of deep learning algorithms.

Experimental evidence recently indicated that neural networks can learn in a different manner than was previously assumed, using adaptive nodes instead of adaptive links. Consequently, links to a node undergo the same adaptation, resulting in cooperative nonlinear dynamics with oscillating effective link weights. Here we show that the biological reality of stationary log-normal distribution of effective link weights in neural networks is a result of such adaptive nodes, although each effective link weight varies significantly in time. The underlying mechanism is a stochastic restoring force emerging from a spontaneous temporal ordering of spike pairs, generated by strong effective link preceding by a weak one. In addition, for feedforward adaptive node networks the number of dynamical attractors can scale exponentially with the number of links. These results are expected to advance deep learning capabilities and to open horizons to an interplay between adaptive node rules and the distribution of network link weights.

Experimental and theoretical results reveal a new underlying mechanism for fast brain learning process, dendritic learning, as opposed to the misdirected research in neuroscience over decades, which is based solely on slow synaptic plasticity. The presented paradigm indicates that learning occurs in closer proximity to the neuron, the computational unit, dendritic strengths are self-oscillating, and weak synapses, which comprise the majority of our brain and previously were assumed to be insignificant, play a key role in plasticity. The new learning sites of the brain call for a reevaluation of current treatments for disordered brain functionality and for a better understanding of proper chemical drugs and biological mechanisms to maintain, control and enhance learning.

Physical models typically assume time-independent interactions, whereas neural networks and machine learning incorporate interactions that function as adjustable parameters. Here we demonstrate a new type of abundant cooperative nonlinear dynamics where learning is attributed solely to the nodes, instead of the network links which their number is significantly larger. The nodal, neuronal, fast adaptation follows its relative anisotropic (dendritic) input timings, as indicated experimentally, similarly to the slow learning mechanism currently attributed to the links, synapses. It represents a non-local learning rule, where effectively many incoming links to a node concurrently undergo the same adaptation. The network dynamics is now counterintuitively governed by the weak links, which previously were assumed to be insignificant. This cooperative nonlinear dynamic adaptation presents a self-controlled mechanism to prevent divergence or vanishing of the learning parameters, as opposed to learning by links, and also supports self-oscillations of the effective learning parameters. It hints on a hierarchical computational complexity of nodes, following their number of anisotropic inputs and opens new horizons for advanced deep learning algorithms and artificial intelligence based applications, as well as a new mechanism for enhanced and fast learning by neural networks.

Neurons are the computational elements that compose the brain and their fundamental principles of activity are known for decades. According to the long-lasting computational scheme, each neuron sums the incoming electrical signals via its dendrites and when the membrane potential reaches a certain threshold the neuron typically generates a spike to its axon. Here we present three types of experiments, using neuronal cultures, indicating that each neuron functions as a collection of independent threshold units. The neuron is anisotropically activated following the origin of the arriving signals to the membrane, via its dendritic trees. The first type of experiments demonstrates that a single neuron's spike waveform typically varies as a function of the stimulation location. The second type reveals that spatial summation is absent for extracellular stimulations from different directions. The third type indicates that spatial summation and subtraction are not achieved when combining intra- and extra- cellular stimulations, as well as for nonlocal time interference, where the precise timings of the stimulations are irrelevant. Results call to re-examine neuronal functionalities beyond the traditional framework, and the advanced computational capabilities and dynamical properties of such complex systems.

Behavioral games predators play among themselves may have profound effects on behavioral games predators play with their prey. We studied the behavioral game between predators and prey within the framework of social foraging among predators. We tested how conspecific interactions among predators (little egret) change the predator-prey behavioral game and foraging success. To do so, we examined foraging behavior of egrets alone and in pairs (male and female) in a specially designed aviary consisting of three equally spaced pools with identical initial prey (comet goldfish) densities. Each pool was comprised of a risky microhabitat, rich with food, and a safe microhabitat with no food, forcing the fish to trade off food and safety. When faced with two versus one egret, we found that fish significantly reduced activity in the risky habitat. Egrets in pairs suffered reduced foraging success (negative intraspecific density dependence) and responded to fish behavior and to their conspecific by changing their visiting regime at the different pools-having shorter, more frequent visits. The time egret spent on each visit allowed them to match their long-term capture success rate across the environment to their capture success rate in the pool, which satisfies one aspect of optimality. Overall, egrets in pairs allocated more time for foraging and changed their foraging tactics to focus more on fish under cover and fish 'peeping' out from their shelter. These results suggest that both prey and predator show behavioral flexibility and can adjust to changing conditions as needed in this foraging game.