Turing learning: a metric-free approach to inferring behavior and its application to swarms

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for ‘tricking’ the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product—the classifiers—that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.     

A geographical genetics framework for inferring homing reproductive behavior in fishes

One of the most intriguing patterns of migration and gene flow that affects genetic structure is the reproductive homing behavior of fishes, wherein the adults return to the areas in which they were spawned. Here we reviewed the literature on homing behavior in fish and propose an analytical framework for testing hypotheses regarding this behavior and its effects on the genetic structure of fish in an explicit geographical context, using a geographical genetics toolbox. Although disentangling the many potential causes underlying genetic population structure and unambiguously demonstrating that the homing behavior causes these genetic patterns is difficult, our framework allows the successive testing of homing behavior with increasing levels of complexity based on the following: (1) establishment of population structures among waterheads; (2) patterns of genetic variability throughout the adult migratory pool; (3) analyses of the non-migratory adult pool; and (4) comparisons among successive generations. We expect that the framework presented here will help delineating the appropriate uses of different sampling designs to make inferences regarding homing behavior and illustrate the limits imposed by the interpretation of different types of genetic data. More importantly, we hope this framework enables researchers to understand how a particular dataset can be utilized in a broader context as an ongoing part of a larger research program and thus guide future research by developing better and more integrated sampling designs.     

Minreg: inferring an active regulator set

Regulatory relations between genes are an important component of molecular pathways. Here, we devise a novel global method that uses a set of gene expression profiles to find a small set of relevant active regulators, identify the genes that they regulate, and automatically annotate them. We show that our algorithm is capable of handling a large number of genes in a short time and is robust to a wide range of parameters. We apply our method to a combined dataset of S. cerevisiae expression profiles, and validate the resulting model of regulation by cross-validation and extensive biological analysis of the selected regulators and their derived annotations.     

PoolHap: inferring haplotype frequencies from pooled samples by next generation sequencing

With the advance of next-generation sequencing (NGS) technologies, increasingly ambitious applications are becoming feasible. A particularly powerful one is the sequencing of polymorphic, pooled samples. The pool can be naturally occurring, as in the case of multiple pathogen strains in a blood sample, multiple types of cells in a cancerous tissue sample, or multiple isoforms of mRNA in a cell. In these cases, it's difficult or impossible to partition the subtypes experimentally before sequencing, and those subtype frequencies must hence be inferred. In addition, investigators may occasionally want to artificially pool the sample of a large number of individuals for reasons of cost-efficiency, e.g., when carrying out genetic mapping using bulked segregant analysis. Here we describe PoolHap, a computational tool for inferring haplotype frequencies from pooled samples when haplotypes are known. The key insight into why PoolHap works is that the large number of SNPs that come with genome-wide coverage can compensate for the uneven coverage across the genome. The performance of PoolHap is illustrated and discussed using simulated and real data. We show that PoolHap is able to accurately estimate the proportions of haplotypes with less than 2% error for 34-strain mixtures with 2X total coverage Arabidopsis thaliana whole genome polymorphism data. This method should facilitate greater biological insight into heterogeneous samples that are difficult or impossible to isolate experimentally. Software and users manual are freely available at http://arabidopsis.gmi.oeaw.ac.at/quan/poolhap/.     

Oral chemical irritation: psychophysical properties

Concentration series of four oral chemical irritants derivedfrom spices (capsicum oleoresin, vanillyl nonamide, piperineand ginger oleoresin) were evaluated for perceived intensityand perceived areas of sensation over time. Growth of sensationwith concentration was described by power function relationshipsand decrements in sensation over time were described by exponentialdecay. Peak sensation intensity was correlated with stimulatedsalivary flow rate. The number of oral areas reported as irritatedincreased with concentration and decreased over time. Whilesome differences were observed among compounds in the areasof stimulation, the dorsal tongue surface and tongue edges werehighly responsive to all compounds. A sequential desensitizationwas observed, in which stimuli were perceived as less strongwhen following a stronger irritant than when following a weakerone.     

The correspondence between proximal phalanx morphology and locomotion: implications for inferring the locomotor behavior of fossil catarrhines

Phalanges are considered to be highly informative in the reconstruction of extinct primate locomotor behavior since these skeletal elements directly interact with the substrate during locomotion. Variation in shaft curvature and relative phalangeal length has been linked to differences in the degree of suspension and overall arboreal locomotor activities. Building on previous work, this study investigated these two skeletal characters in a comparative context to analyze function, while taking evolutionary relationships into account. This study examined the correspondence between proportions of suspension and overall substrate usage observed in 17 extant taxa and included angle of curvature and relative phalangeal length. Predictive models based on these traits are reported. Published proportions of different locomotor behaviors were regressed against each phalangeal measurement and a size proxy. The relationship between each behavior and skeletal trait was investigated using ordinary least-squares, phylogenetic generalized least-squares (pGLS), and two pGLS transformation methods to determine the model of best-fit. Phalangeal curvature and relative length had significant positive relationships with both suspension and overall arboreal locomotion. Cross-validation analyses demonstrated that relative length and curvature provide accurate predictions of relative suspensory behavior and substrate usage in a range of extant species when used together in predictive models. These regression equations provide a refined method to assess the amount of suspensory and overall arboreal locomotion characterizing species in the catarrhine fossil record.     

Spikes alone do not behavior make: why neuroscience needs biomechanics

Neural circuits do not function in isolation; they interact with the physical world, accepting sensory inputs and producing outputs via muscles. Since both these pathways are constrained by physics, the activity of neural circuits can only be understood by considering biomechanics of muscles, bodies, and the exterior world. We discuss how animal bodies have natural stable motions that require relatively little activation or control from the nervous system. The nervous system can substantially alter these motions, by subtly changing mechanical properties such as body or leg stiffness. Mechanics can also provide robustness to perturbations without sensory reflexes. By considering a complete neuromechanical system, neuroscientists and biomechanicians together can provide a more integrated view of neural circuitry and behavior.     

The genomic code: inferring Vibrionaceae niche specialization

The Vibrionaceae show a wide range of niche specialization, from free-living forms to those attached to biotic and abiotic surfaces, from symbionts to pathogens and from estuarine inhabitants to deep-sea piezophiles. The existence of complete genome sequences for closely related species from varied aquatic niches makes this group an excellent case study for genome comparison.     

Action observation: inferring intentions without mirror neurons

A recent study has shown, using fMRI, that the mirror neuron system does not mediate action understanding when the observed action is novel or when it is hard to understand.