State-of-the-art DNA sequencing is providing ever more detailed insights into the genomes of humans, extant apes, and even extinct hominins, offering unprecedented opportunities to uncover the molecular variants that make us human. A common assumption is that the emergence of behaviorally modern humans after 200,000 years ago required—and followed—a specific biological change triggered by one or more genetic mutations. For example, Klein has argued that the dawn of human culture stemmed from a single genetic change that “fostered the uniquely modern ability to adapt to a remarkable range of natural and social circumstance”. But are evolutionary changes in our genome a cause or a consequence of cultural innovation?
Many methods have been proposed for community detection in networks. Some of the most promising are methods based on statistical inference, which rest on solid mathematical foundations and return excellent results in practice. In this paper we show that two of the most widely used inference methods can be mapped directly onto versions of the standard minimum-cut graph partitioning problem, which allows us to apply any of the many well-understood partitioning algorithms to the solution of community detection problems. We illustrate the approach by adapting the Laplacian spectral partitioning method to perform community inference, testing the resulting algorithm on a range of examples, including computer-generated and real-world networks. Both the quality of the results and the running time rival the best previous methods.
Significant progress has occurred in the field of brain–machine interfaces (BMI) since the first demonstrations with rodents, monkeys, and humans controlling different prosthetic devices directly with neural activity. This technology holds great potential to aid large numbers of people with neurological disorders. However, despite this initial enthusiasm and the plethora of available robotic technologies, existing neural interfaces cannot as yet master the control of prosthetic, paralyzed, or otherwise disabled limbs. Here I briefly discuss recent advances from our laboratory into the neural basis of BMIs that should lead to better prosthetic control and clinically viable solutions, as well as new insights into the neurobiology of action.
Geothermal heat provides sustainable energy for electricity generation and heating applications. Worldwide use of geothermal energy has increased steadily over the past few decades (1, 2), and exploration and development are ongoing at unprecedented levels in Iceland, New Zealand, East Africa, Germany, Chile, and Australia. Today, 24 countries generate electricity from geothermal energy and 78 countries use geothermal energy for direct uses. Yet, geothermal sources still represent less than one percent of global energy production. The accessibility of geothermal resources depends on temperature and depth (see the figure). What are the limitations of geothermal energy extraction, and can the use of this resource be increased?
Stochastic Model for the Vocabulary Growth in Natural Languages
What cultural and social processes determine the size and growth of the vocabulary of a natural language? Does such a vocabulary grow forever? From large text databases, such as the Google Ngram, that have become available only recently, researchers tease out new and systematic insights into these fundamental questions and develop a mathematical model with predictive power that describes vocabulary growth as a simple stochastic process.
Frontiers in Neurorobotics Research Topic: "Intrinsic motivations and open-ended development in animals, humans, and robots"
The aim of this Research Topic for Frontiers in Neurorobotics and Frontiers in Cognitive Science is to present state-of-the-art research, whether theoretical, empirical, or computational investigations, on open-ended development driven by intrinsic motivations. The topic will address questions such as: How do motivations drive learning? How are complex skills built up from a foundation of simpler competencies? What are the neural and computational bases for intrinsically motivated learning? What is the contribution of intrinsic motivations to wider cognition?
Now is an important moment in the study of intrinsically motivated open-ended development, requiring contributions and integration across a large number of fields within the cognitive sciences. This Research Topic aims to contribute to this effort by welcoming papers carried out with ethological, psychological, neuroscientific and computational approaches, as well as research that cuts across disciplines and approaches. Original research advancing specific aspects of the state-of-the art and review/theoretical papers aiming to systematize the field are both suitable for this Topic.
Gianluca Baldassarre, Italian National Research Council (CNR), Italy Andrew Barto, University of Massachusetts Amherst, USA Marco Mirolli, Istituto di Scienze e Tecnologie della Cognizione, Italy Peter Redgrave Richard M. Ryan, University of Rochester, USA Tom Stafford, University of Sheffield, United Kingdom
Deadline for full article submission: 21 May 2013
Extended deadline for full article submission: 21 Jun 2013