Biological Realism Research Articles

Effects of temperature and water turbulence on vertebral number and body shape in Astyanax mexicanus (Teleostei: Characidae).

Environmental changes can modify the phenotypic characteristics of populations, which in turn can influence their evolutionary trajectories. In ectotherms like fishes, temperature is a particularly important environmental variable that is known to have significant impacts on the phenotype. Here, we raised specimens of the surface ecomorph of Astyanax mexicanus at temperatures of 20°C, 23°C, 25°C, and 28°C to examine how temperature influenced vertebral number and body shape. To increase biological realism, specimens were also subjected to two water turbulence regimes. Vertebral number was counted from x-rays and body shape variation was analysed using geometric morphometric methods. Temperature significantly impacted mean total vertebral number, which increased at the lowest and highest temperatures. Fish reared at lower temperatures had relatively more precaudal vertebrae while fish reared at higher temperatures had relatively more caudal vertebrae. Vertebral anomalies, especially vertebral fusions, were most frequent at the extreme temperature treatments. Temperature significantly impacted body shape as well, with fish reared at 20°C being particularly divergent. Water turbulence also impacted body shape in a generally predictable manner, with specimens reared in high turbulence environments being more streamlined and having extended dorsal and anal fin bases. Variation in environmental variables thus resulted in significant changes in morphological traits known to impact fish fitness, indicating that A. mexicanus has the capacity to exhibit a range of phenotypic plasticity when challenged by environmental change. Understanding the biochemical mechanisms underlying this plasticity and whether adaptive plasticity has influenced the evolutionary radiation of the Characidae, are major directions for future research.

Beta Diversity Patterns Derived from Island Biogeography Theory.

Metacommunity theory and its constituent theory of island biogeography (TIB) have the potential to unify ecology across different scales. The TIB has been successful in predicting alpha diversity patterns, such as species-area relationships and species-abundance distributions, but lags behind in predicting spatial beta diversity patterns. In this study we use island biogeography theory as the starting point to integrate spatial beta diversity patterns into metacommunity theory. We first derive theoretical predictions for the expected beta diversity patterns under the classic MacArthur and Wilson framework, where all species have equal colonization and extinction rates. We then test these predictions for the avian community composition of 42 islands (and 93 species) in Thousand Island Lake, China. Our theoretical results corroborate that longer distance and smaller area lead to higher beta diversity and further reveal that pairwise beta diversity is independent of the size of the mainland species pool. We also find that for the partitioned pairwise beta diversity components, the turnover component increases with the ratio of extinction rates and colonization rates, while the nestedness component is a unimodal function of the ratio of extinction rates and colonization rates. For the empirical island system, we find that beta diversity patterns better distinguish a species-equivalent model from a species-nonequivalent model than alpha diversity patterns. Our findings suggest that beta diversity patterns provide a powerful tool in detecting nonneutral processes, and our model has the potential to incorporate more biological realism in future analyses.

Combining multi-state species distribution models, mortality estimates, and landscape connectivity to model potential species distribution for endangered species in human dominated landscapes

Species ranges are changing in response to human-related disturbances and often management and conservation decisions must be based on incomplete information. In this context, species distribution models (SDMs) represent the most widely used tool, but they often lack any reference to demographic performance of the population under study, spatial structure of the habitat patches, or connectivity at the landscape level.Combining a multi-state SDM with a landscape pattern analysis and a mortality model, we developed a spatially-explicit, integrated model to assist and inform conservation planning for the Apennine brown bear in central Italy. We identified 15 critical habitat areas, potentially hosting 76 adult female bears. Many of these areas are, however, characterized by high levels of human-related mortality, making them attractive sink-like areas. Structural connectivity was higher in the northern part of the study area while only limited connectivity characterizes the core area, where most of the bears currently live.Our integrated model indicates that the conservation of this relict and isolated bear population is a realistic conservation goal, as we estimated that 192–270 bears could live across the Apennines. Our modelling framework enhances the biological realism of traditional SDMs and provides a conservation planning tool that integrates habitat suitability, mortality risk (as a component of the total demographic performance) and structural connectivity among habitat patches at the landscape scale. It is particularly suited for endangered species living in a human-modified landscapes where establishing a realistic and spatially explicit conservation goal would facilitate pro-active management.

Scalable Digital Neuromorphic Architecture for Large-Scale Biophysically Meaningful Neural Network With Multi-Compartment Neurons.

Multicompartment emulation is an essential step to enhance the biological realism of neuromorphic systems and to further understand the computational power of neurons. In this paper, we present a hardware efficient, scalable, and real-time computing strategy for the implementation of large-scale biologically meaningful neural networks with one million multi-compartment neurons (CMNs). The hardware platform uses four Altera Stratix III field-programmable gate arrays, and both the cellular and the network levels are considered, which provides an efficient implementation of a large-scale spiking neural network with biophysically plausible dynamics. At the cellular level, a cost-efficient multi-CMN model is presented, which can reproduce the detailed neuronal dynamics with representative neuronal morphology. A set of efficient neuromorphic techniques for single-CMN implementation are presented with all the hardware cost of memory and multiplier resources removed and with hardware performance of computational speed enhanced by 56.59% in comparison with the classical digital implementation method. At the network level, a scalable network-on-chip (NoC) architecture is proposed with a novel routing algorithm to enhance the NoC performance including throughput and computational latency, leading to higher computational efficiency and capability in comparison with state-of-the-art projects. The experimental results demonstrate that the proposed work can provide an efficient model and architecture for large-scale biologically meaningful networks, while the hardware synthesis results demonstrate low area utilization and high computational speed that supports the scalability of the approach.

The case for close biological realism when attempting biomimicry: Comment on “Does being multi-headed make you better at solving problems? A survey of Physarum-based models and computations” by C. Gao et al.

The case for close biological realism when attempting biomimicry: Comment on “Does being multi-headed make you better at solving problems? A survey of Physarum-based models and computations” by C. Gao et al.

The importance of spatiotemporal fish population dynamics in barrier mitigation planning

In this study, we propose a novel framework combining spatially explicit population viability analysis and optimization for prioritizing fish passage barrier mitigation decisions. Our model aims to maximize the equilibrium population size, or alternatively minimize the extinction risk, of a target fish species subject to a budget on the total cost of barrier mitigation. A case study involving a wild coho salmon (Oncorhynchus kisutch) population from the Tillamook basin, Oregon, USA is used to illustrate the benefits of our approach. We consider two different spawning adult dispersal patterns, river and reach level homing, as well as straying. Under density dependent population growth, we find that homing behavior type has a significant effect on barrier mitigation decisions. In particular, with reach homing, our model produces virtually the same population sizes as a more traditional barrier prioritization procedure designed to maximize accessible habitat. With river homing, however, we find that it is not necessary to remove all barriers in order to maximize equilibrium population size. Indeed, a stochastic version of our model reveals that removing all barriers actually results in a marginal increase in quasi-extinction risk. We hypothesize that this is due to a population thinning effect of barriers, resulting in a surplus of recruits in areas of low spawner density. Our findings highlights the importance of considering spatiotemporal fish population dynamics in river connectivity restoration planning. By adding greater biological realism, models such as ours can help conservation managers to more strategically allocate limited resources, resulting in both cost savings and improved population status for a focal species.

The SONATA Data Format for Efficient Description of Large-Scale Network Models

Increasing availability of comprehensive experimental datasets in neuroscience and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational network models. To support construction and simulation, as well as sharing of such large-scale models, a broadly applicable, flexible, and high-performance data format is necessary. To address this need, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. It is designed for memory and computational efficiency and works across multiple platforms. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. We provide reference Application Programming Interfaces and model examples to catalyze support and adoption of the format. SONATA format is free and open for the community to use and build upon with the goal of enabling efficient model building, sharing, and reproducibility.

Effects of model choice, network structure, and interaction strengths on knockout extinction models of ecological robustness.

Analysis of ecological networks is a valuable approach to understanding the vulnerability of systems to disturbance. The tolerance of ecological networks to coextinctions, resulting from sequences of primary extinctions (here termed “knockout extinction models”, in contrast with other dynamic approaches), is a widely used tool for modeling network “robustness”. Currently, there is an emphasis to increase biological realism in these models, but less attention has been given to the effect of model choices and network structure on robustness measures. Here, we present a suite of knockout extinction models for bipartite ecological networks (specifically plant–pollinator networks) that can all be analyzed on the same terms, enabling us to test the effects of extinction rules, interaction weights, and network structure on robustness. We include two simple ecologically plausible models of propagating extinctions, one new and one adapted from existing models. All models can be used with weighted or binary interaction data. We found that the choice of extinction rules impacts robustness; our two propagating models produce opposing effects in all tests on observed plant–pollinator networks. Adding weights to the interactions tends to amplify the opposing effects and increase the variation in robustness. Variation in robustness is a key feature of these extinction models and is driven by the structural heterogeneity of nodes (specifically, the skewness of the plant degree distribution) in the network. Our analysis therefore reveals the mechanisms and fundamental network properties that drive observed trends in robustness.

Graph-Based Models of Cortical Axons for the Prediction of Neuronal Response to Extracellular Electrical Stimulation.

Over the past decade, many important insights to brain function have been obtained through clever application of detailed compartmental model neurons. New computing capabilities brought opportunities to study large networks of model neurons. Certain applications for these models, such as extracellular electrical stimulation, demand a very high degree of biological realism. While dendrites and somatic morphology may be obtained from explicit reconstructions, this approach is less useful for axonal structures, which are more difficult to characterize across a neuronal population. The purpose of this paper is to extend neuronal morphology generative models to highly branched axon terminal arbors as well as to present a clear use-case for such models in the study of cortical tissue response to externally applied electric fields. The results of this work are (i) presentation and quantitative/qualitative description of generated fibers and (ii) an extracellular electrical stimulation strength-duration study.

A memristive plasticity model of voltage-based STDP suitable for recurrent bidirectional neural networks in the hippocampus

Memristive systems have gained considerable attention in the field of neuromorphic engineering, because they allow the emulation of synaptic functionality in solid state nano-physical systems. In this study, we show that memristive behavior provides a broad working framework for the phenomenological modelling of cellular synaptic mechanisms. In particular, we seek to understand how close a memristive system can account for the biological realism. The basic characteristics of memristive systems, i.e. voltage and memory behavior, are used to derive a voltage-based plasticity rule. We show that this model is suitable to account for a variety of electrophysiology plasticity data. Furthermore, we incorporate the plasticity model into an all-to-all connecting network scheme. Motivated by the auto-associative CA3 network of the hippocampus, we show that the implemented network allows the discrimination and processing of mnemonic pattern information, i.e. the formation of functional bidirectional connections resulting in the formation of local receptive fields. Since the presented plasticity model can be applied to real memristive devices as well, the presented theoretical framework can support both, the design of appropriate memristive devices for neuromorphic computing and the development of complex neuromorphic networks, which account for the specific advantage of memristive devices.

Kuramoto Model for Excitation-Inhibition-Based Oscillations.

The Kuramoto model (KM) is a theoretical paradigm for investigating the emergence of rhythmic activity in large populations of oscillators. A remarkable example of rhythmogenesis is the feedback loop between excitatory (E) and inhibitory (I) cells in large neuronal networks. Yet, although the EI-feedback mechanism plays a central role in the generation of brain oscillations, it remains unexplored whether the KM has enough biological realism to describe it. Here we derive a two-population KM that fully accounts for the onset of EI-based neuronal rhythms and that, as the original KM, is analytically solvable to a large extent. Our results provide a powerful theoretical tool for the analysis of large-scale neuronal oscillations.

The influence of environmental variance on the evolution of signaling behavior

A recent meta-analysis has indicated that environmental quality and variability can influence whether offspring begging and parental responses to these signals are motivated by offspring need or offspring quality. We create a model to verify and apply evolutionary logic to this hypothesis. We determine the ecological and social conditions under which species signal and respond to need in favorable environments and quality in poor environments. The environmental conditions that favor this shift are widest when signaling costs and differences in quality between offspring are moderate. Low relatedness between siblings coupled with high signaling costs, as well as moderate relatedness between siblings coupled with low signaling costs, allow for the shift between signals of need and signals of quality to occur in more volatile environments. Furthermore, only species whose offspring are highly dependent on parents for survival are not expected to use both signals of need and of quality. Ultimately, this shift between signaling need and signaling quality is the result of high-quality offspring benefiting more from meager amounts of parental provisioning, whereas low-quality offspring have most to gain when parents can contribute more substantially. We show that this differential benefit of resources depends substantially upon offspring fitness as functions of parental investments, a variable which lacks both diversity and biological realism in previous theoretical approaches. We then use this work to reassess previous theory on signals of need and of quality.

NeuroSpike viewer: A graphical environment for efficient control, communication and display of large-scale real-time simulation of Spiking Neural Networks on embedded systems

In recent years, the scientific community's interest for developing software or hardware tools capable of processing and displaying real-time simulations of large-scale Spiking Neural Networks (SNN) has been growing exponentially. Visualization of the simulations is crucial to observe and study the dynamics of the SNN, because these models involve high level of biological realism by encoding spatial-temporal information into action potentials or spikes like biological neurons do. Existing tools are based on supercomputers or customized neuromorphic architectures to compute large-scale SNN models. However, many of them are unsuitable to carry out real-time analysis of large scale SNN with minimal cost as large amount of complex data needs to be processed and displayed at the same time. This work intends to provide an efficient cost effective and power efficient emulation tool based on hardware-software hybrid to process, control, and display large-scale SNN network behavior in real time. The solution consists of a customized hardware architecture that computes the neural-synaptic parameters and a software environment that displays the same on the screen of a general purpose computer (Host). The data to be visualized is transferred to the host through high speed Ethernet link. The proposed solution tailors the use of the Ethernet link bandwidth to an optimum such that the customized neuromorphic architecture and the host computer duo can be used to visualize the dynamics of SNN network of any size in real-time. The hardware and software platforms are scalable in terms of number of neurons and synapses. This solution gives flexibility to the user to visualize and analyze SNN network of any size avoiding saturation of the high-speed serial Ethernet link while supporting the visualization of SNN dynamics with a good screen resolution.

Real-Time Neuromorphic System for Large-Scale Conductance-Based Spiking Neural Networks.

The investigation of the human intelligence, cognitive systems and functional complexity of human brain is significantly facilitated by high-performance computational platforms. In this paper, we present a real-time digital neuromorphic system for the simulation of large-scale conductance-based spiking neural networks (LaCSNN), which has the advantages of both high biological realism and large network scale. Using this system, a detailed large-scale cortico-basal ganglia-thalamocortical loop is simulated using a scalable 3-D network-on-chip (NoC) topology with six Altera Stratix III field-programmable gate arrays simulate 1 million neurons. Novel router architecture is presented to deal with the communication of multiple data flows in the multinuclei neural network, which has not been solved in previous NoC studies. At the single neuron level, cost-efficient conductance-based neuron models are proposed, resulting in the average utilization of 95% less memory resources and 100% less DSP resources for multiplier-less realization, which is the foundation of the large-scale realization. An analysis of the modified models is conducted, including investigation of bifurcation behaviors and ionic dynamics, demonstrating the required range of dynamics with a more reduced resource cost. The proposed LaCSNN system is shown to outperform the alternative state-of-the-art approaches previously used to implement the large-scale spiking neural network, and enables a broad range of potential applications due to its real-time computational power.

A Computational Account of the Role of Cochlear Nucleus and Inferior Colliculus in Stabilizing Auditory Nerve Firing for Auditory Category Learning.

It is well known that auditory nerve (AN) fibers overcome bandwidth limitations through the volley principle, a form of multiplexing. What is less well known is that the volley principle introduces a degree of unpredictability into AN neural firing patterns that may be affecting even simple stimulus categorization learning. We use a physiologically grounded, unsupervised spiking neural network model of the auditory brain with spike time dependent plasticity learning to demonstrate that plastic auditory cortex is unable to learn even simple auditory object categories when exposed to the raw AN firing input without subcortical preprocessing. We then demonstrate the importance of nonplastic subcortical preprocessing within the cochlear nucleus and the inferior colliculus for stabilizing and denoising AN responses. Such preprocessing enables the plastic auditory cortex to learn efficient robust representations of the auditory object categories. The biological realism of our model makes it suitable for generating neurophysiologically testable hypotheses.

Does scale matter? A systematic review of incorporating biological realism when predicting changes in species distributions.

BackgroundThere is ample evidence that biotic factors, such as biotic interactions and dispersal capacity, can affect species distributions and influence species’ responses to climate change. However, little is known about how these factors affect predictions from species distribution models (SDMs) with respect to spatial grain and extent of the models.ObjectivesUnderstanding how spatial scale influences the effects of biological processes in SDMs is important because SDMs are one of the primary tools used by conservation biologists to assess biodiversity impacts of climate change.Data sources and study eligibility criteriaWe systematically reviewed SDM studies published from 2003–2015 using ISI Web of Science searches to: (1) determine the current state and key knowledge gaps of SDMs that incorporate biotic interactions and dispersal; and (2) understand how choice of spatial scale may alter the influence of biological processes on SDM predictions.Synthesis methods and limitationsWe used linear mixed effects models to examine how predictions from SDMs changed in response to the effects of spatial scale, dispersal, and biotic interactions.ResultsThere were important biases in studies including an emphasis on terrestrial ecosystems in northern latitudes and little representation of aquatic ecosystems. Our results suggest that neither spatial extent nor grain influence projected climate-induced changes in species ranges when SDMs include dispersal or biotic interactions.ConclusionsWe identified several knowledge gaps and suggest that SDM studies forecasting the effects of climate change should: 1) address broader ranges of taxa and locations; and 1) report the grain size, extent, and results with and without biological complexity. The spatial scale of analysis in SDMs did not affect estimates of projected range shifts with dispersal and biotic interactions. However, the lack of reporting on results with and without biological complexity precluded many studies from our analysis.

Effects of competition and climate variables on modelling height to live crown for three boreal tree species in Alberta, Canada

Using tree data from permanent sample plots and climate data from the ClimateWNA model, mixed-effects height to live crown (HTC) models were developed for three boreal tree species in Alberta, Canada: trembling aspen (Populus tremuloides Michx.), lodgepole pine (Pinus contorta var. latifolia Engelm.) and white spruce (Picea glauca (Moench) Voss). Three model forms, the Wykoff model, a logistic model and an exponential model, were evaluated for each species. Tree height was the most significant predictor of HTC and was used in all models. In addition, we investigated the effects of competition and climatic variables on HTC modelling. Height–diameter ratio and either total stand basal area or basal area of coniferous trees were used as competition measures in the models. Different climate variables were evaluated, and spring degree-days below 0 °C, mean annual precipitation and summer heat–moisture index were incorporated into the aspen, lodgepole pine and white spruce models, respectively. Site index was only significant in lodgepole pine models. Residual variances were modelled as functions of tree height to account for heteroscedasticity still present in the mixed-effects models after the inclusion of random parameters. Based on model fitting and validation results as well as biological realism, the mixed-effects Wykoff models were the best for aspen and white spruce, and the mixed-effects logistic model was the best for lodgepole pine.

Bayesian molecular dating: opening up the black box.

Molecular dating analyses allow evolutionary timescales to be estimated from genetic data, offering an unprecedented capacity for investigating the evolutionary past of all species. These methods require us to make assumptions about the relationship between genetic change and evolutionary time, often referred to as a 'molecular clock'. Although initially regarded with scepticism, molecular dating has now been adopted in many areas of biology. This broad uptake has been due partly to the development of Bayesian methods that allow complex aspects of molecular evolution, such as variation in rates of change across lineages, to be taken into account. But in order to do this, Bayesian dating methods rely on a range of assumptions about the evolutionary process, which vary in their degree of biological realism and empirical support. These assumptions can have substantial impacts on the estimates produced by molecular dating analyses. The aim of this review is to open the 'black box' of Bayesian molecular dating and have a look at the machinery inside. We explain the components of these dating methods, the important decisions that researchers must make in their analyses, and the factors that need to be considered when interpreting results. We illustrate the effects that the choices of different models and priors can have on the outcome of the analysis, and suggest ways to explore these impacts. We describe some major research directions that may improve the reliability of Bayesian dating. The goal of our review is to help researchers to make informed choices when using Bayesian phylogenetic methods to estimate evolutionary rates and timescales.

A Rate-Reduced Neuron Model for Complex Spiking Behavior

We present a simple rate-reduced neuron model that captures a wide range of complex, biologically plausible, and physiologically relevant spiking behavior. This includes spike-frequency adaptation, postinhibitory rebound, phasic spiking and accommodation, first-spike latency, and inhibition-induced spiking. Furthermore, the model can mimic different neuronal filter properties. It can be used to extend existing neural field models, adding more biological realism and yielding a richer dynamical structure. The model is based on a slight variation of the Rulkov map.

“Racial realism II: Are folk races real?”

AbstractThis article is Part II in a pair of articles on racial realism. In Part I, I defined “racial realism” and discussed the major attempts in the past twenty years among metaphysicians of race and biologists to defend racial realism from the viewpoint of what biologists mean by “race.” In this article, I continue discussing and critiquing how metaphysicians of race have conceived of and defended racial realism, but with a focus on how ordinary people use “race.” I focus on two broad groups of racial realisms in this article: biological racial realism and social racial realism. After defining each one, I summarize a representative sample of recent attempts from metaphysicians of race to defend both types of racial realism. I also discuss major criticisms against each attempt. I end by sketching a new, radical pluralist way of being a racial realist, and I provide some empirical motivation for why it's promising.