Modelling the evolution of learning

The ability to learn from past experience is an important adaptation, but how natural selection shapes learning is not well understood. Here, we present a novel way of modeling learning using small neural networks and a simple, biology-inspired learning algorithm. We used this model to study the evolution of learning under various environmental conditions and different scenarios for the trade-off between exploration (learning) and exploitation (foraging). Efficient learning regularly evolved in our individual-based simulations. However, the evolution of learning was less likely in relatively constant environments(where learning is less important) or in case of short-lived agents (that cannot afford to spend much of their lifetime on exploration). Once learning did evolve, the characteristics of the learning strategy and the average performance after learning were surprisingly little affected by the frequency and/or magnitude of environmental change. In contrast, agent lifespan had a strong effect on the evolved learning strategy.Interestingly, a longer learning period did not always lead to a better performance, indicating that the evolved neural networks differ in the effectiveness of learning. Overall, however, our study shows that even a relatively simple learning mechanism can lead to efficient adaptation

Upscaling in the sharing economy: insights from the UK

Aimed at academics, private businesses, investors and public sector bodies, this report develops a typology of upscaling models in the sharing economy across three key sectors: accommodation, transportation, and professional and personal services

Neural network models for the evolution of associative learning

The ability to learn from past experience is an important adaptation, but how natural selection shapes learning is not well understood. Here, we investigate the evolution of associative learning (the association of stimuli with rewards) by a modelling approach that is based on the evolution of neural networks (NNs) underlying learning. Individuals employ their genetically encoded NN to solve a learning task with fitness consequences. NNs inducing more efficient learning have a selective advantage and spread in the population. We show that in a simple learning task, the evolved NNs, even those with very simple architecture, outperform well-studied associative learning rules, such as the Rescorla-Wagner rule. During their evolutionary trajectory, NNs often pass a transitional stage where they functionally resemble Rescorla-Wagner learning, but further evolution shapes them to approximate the theoretically optimal learning rule. Networks with a simple architecture evolve much faster and tend to outperform their more complex counterparts on a shorter-term perspective. Also, on a longer-term perspective network complexity is not a reliable indicator of evolved network performance. These conclusions change somewhat when the learning task is more challenging. Then the performance of many evolved networks is not better than that of the Rescorla-Wagner rule; only some of the more complex networks reach a performance level close to the optimal Bayesian learning rule. In conclusion, we show that the mechanisms underlying learning influence the outcome of evolution. A neural-network approach allows for more flexibility and a wider set of evolutionary outcomes than most analytical studies, while at the same time, it provides a relatively straightforward and intuitive framework to study the learning process.Competing Interest StatementThe authors have declared no competing interest

Are autosomal sex-determining factors of the housefly (Musca domestica) spreading north?

Multiple sex-determining factors have been found in natural populations of the housefly, Musca domestica. Their distribution seems to follow a geographical cline. The 'standard' system, with a male-determining factor, M, located on the Y chromosome, prevails at higher latitudes and altitudes. At lower latitudes and altitudes M factors have also been found on any of the five autosomes. Such populations often also harbour a dominant autosomal factor, F(D), which induces female development even in the presence of several M factors. Autosomal M factors were first observed some 50 years ago. It has been hypothesized that following their initial appearance, they are spreading northwards, replacing the standard XY system, but this has never been systematically investigated. To scrutinize this hypothesis, we here compare the current distribution of autosomal M factors in continental Europe, on a transect running from Germany to southern Italy, with the distribution reported 25 years ago. Additionally, we analysed the frequencies of the F(D) factor, which has not been done before for European populations. In contrast to earlier predictions, we do not find a clear change in the distribution of sex-determining factors: as 25 years ago, only the standard XY system is present in the north, while autosomal M factors and the F(D) factor are prevalent in Italy. We discuss possible causes for this apparently stable polymorphism.</p

Glomerulopathy in patients with distal duplication of chromosome 6p

Background: Duplication of the distal part of chromosome 6p is a rare genetic syndrome. Renal involvement has been reported in the majority of patients, including a wide range of congenital abnormalities of kidney and urinary tract and, occasionally, a proteinuric glomerulopathy. Case presentation: Here, we report a 13-year-old girl with 6p25.3p22.1 duplication who presented with proteinuria in infancy, was later diagnosed as focal segmental glomerulosclerosis, progressed to end-stage renal disease and was successfully transplanted. Conclusion: A systematic literature review suggests that 15–20 % of individuals with distal 6p duplication develop progressive proteinuric glomerulopathy. Monitoring of kidney function should be recommended in all cases

ALMA observations of the "fresh" carbon-rich AGB star TX Piscium. The discovery of an elliptical detached shell

Aims. The carbon-rich asymptotic giant branch (AGB) star TX Piscium (TX Psc) has been observed multiple times during multiple epochs and at different wavelengths and resolutions, showing a complex molecular CO line profile and a ring-like structure in thermal dust emission. We investigate the molecular counterpart in high resolution, aiming to resolve the ring-like structure and identify its origin. Methods. Atacama Large Millimeter/submillimeter Array (ALMA) observations have been carried out to map the circumstellar envelope (CSE) of TX Psc in CO(2-1) emission and investigate the counterpart to the ring-like dust structure. Results. We report the detection of a thin, irregular, and elliptical detached molecular shell around TX Psc, which coincides with the dust emission. This is the first discovery of a non-spherically symmetric detached shell, raising questions about the shaping of detached shells. Conclusions. We investigate possible shaping mechanisms for elliptical detached shells and find that in the case of TX Psc, stellar rotation of 2 km/s can lead to a non-uniform mass-loss rate and velocity distribution from stellar pole to equator, recreating the elliptical CSE. We discuss the possible scenarios for increased stellar momentum, enabling the rotation rates needed to reproduce the ellipticity of our observations, and come to the conclusion that momentum transfer of an orbiting object with the mass of a brown dwarf would be sufficient