How Turing parasites expand the computational landscape of digital life.

Why are living systems complex? Why does the biosphere contain living beings with complexity features beyond those of the simplest replicators? What kind of evolutionary pressures result in more complex life forms? These are key questions that pervade the problem of how complexity arises in evolution. One particular way of tackling this is grounded in an algorithmic description of life: living organisms can be seen as systems that extract and process information from their surroundings to reduce uncertainty. Here we take this computational approach using a simple bit string model of coevolving agents and their parasites.

Evolution of Brains and Computers: The Roads Not Taken.

When computers started to become a dominant part of technology around the 1950s, fundamental questions about reliable designs and robustness were of great relevance. Their development gave rise to the exploration of new questions, such as what made brains reliable (since neurons can die) and how computers could get inspiration from neural systems. In parallel, the first artificial neural networks came to life.

Fate of Duplicated Neural Structures.

Statistical physics determines the abundance of different arrangements of matter depending on cost-benefit balances. Its formalism and phenomenology percolate throughout biological processes and set limits to effective computation. Under specific conditions, self-replicating and computationally complex patterns become favored, yielding life, cognition, and Darwinian evolution.

Ageing, computation and the evolution of neural regeneration processes.

Metazoans gather information from their environments and respond in predictable ways. These computational tasks are achieved with neural networks of varying complexity. Their performance must be reliable over an individual's lifetime while dealing with the shorter lifespan of cells and connection failure-thus rendering ageing a relevant feature.

Evolutionary aspects of reservoir computing.

Reservoir computing (RC) is a powerful computational paradigm that allows high versatility with cheap learning. While other artificial intelligence approaches need exhaustive resources to specify their inner workings, RC is based on a reservoir with highly nonlinear dynamics that does not require a fine tuning of its parts. These dynamics project input signals into high-dimensional spaces, where training linear readouts to extract input features is vastly simplified.

Urbanity and the dynamics of language shift in Galicia.

Sociolinguistic phenomena often involve interactions across different scales and result in social and linguistic changes that can be tracked over time. Here, we focus on the dynamics of language shift in Galicia, a bilingual community in northwest Spain. Using historical data on Galician and Spanish speakers, we show that the rate at which shift dynamics unfold correlates inversely with the internal complexity of a region (approximated by the proportion of urban area).

Zipf's Law, unbounded complexity and open-ended evolution.

A major problem for evolutionary theory is understanding the so-called open-ended nature of evolutionary change, from its definition to its origins. Open-ended evolution (OEE) refers to the unbounded increase in complexity that seems to characterize evolution on multiple scales. This property seems to be a characteristic feature of biological and technological evolution and is strongly tied to the generative potential associated with combinatorics, which allows the system to grow and expand their available state spaces.

Automated in vivo patch-clamp evaluation of extracellular multielectrode array spike recording capability.

Much innovation is currently aimed at improving the number, density, and geometry of electrodes on extracellular multielectrode arrays for in vivo recording of neural activity in the mammalian brain. To choose a multielectrode array configuration for a given neuroscience purpose, or to reveal design principles of future multielectrode arrays, it would be useful to have a systematic way of evaluating the spike recording capability of such arrays. We describe an automated system that performs robotic patch-clamp recording of a neuron being simultaneously recorded via an extracellular multielectrode array.

The morphospace of language networks.

What is the nature of language? How has it evolved in different species? Are there qualitative, well-defined classes of languages? Most studies of language evolution deal in a way or another with such theoretical contraption and explore the outcome of diverse forms of selection on the communication matrix that somewhat optimizes communication. This framework naturally introduces networks mediating the communicating agents, but no systematic analysis of the underlying landscape of possible language graphs has been developed. Here we present a detailed analysis of network properties on a generic model of a communication code, which reveals a rather complex and heterogeneous morphospace of language graphs.

Information theory, predictability and the emergence of complex life.

Despite the obvious advantage of simple life forms capable of fast replication, different levels of cognitive complexity have been achieved by living systems in terms of their potential to cope with environmental uncertainty. Against the inevitable cost associated with detecting environmental cues and responding to them in adaptive ways, we conjecture that the potential for predicting the environment can overcome the expenses associated with maintaining costly, complex structures. We present a minimal formal model grounded in information theory and selection, in which successive generations of agents are mapped into transmitters and receivers of a coded message.

Phase transitions in Pareto optimal complex networks.

The organization of interactions in complex systems can be described by networks connecting different units. These graphs are useful representations of the local and global complexity of the underlying systems. The origin of their topological structure can be diverse, resulting from different mechanisms including multiplicative processes and optimization.

Stochastic amplification of fluctuations in cortical up-states.

Cortical neurons are bistable; as a consequence their local field potentials can fluctuate between quiescent and active states, generating slow 0.5 2 Hz oscillations which are widely known as transitions between Up and Down States. Despite a large number of studies on Up-Down transitions, deciphering its nature, mechanisms and function are still today challenging tasks.

Successful attack on permutation-parity-machine-based neural cryptography.

An algorithm is presented which implements a probabilistic attack on the key-exchange protocol based on permutation parity machines. Instead of imitating the synchronization of the communicating partners, the strategy consists of a Monte Carlo method to sample the space of possible weights during inner rounds and an analytic approach to convey the extracted information from one outer round to the next one. The results show that the protocol under attack fails to synchronize faster than an eavesdropper using this algorithm.

Intimate partner violence: last year prevalence and association with socio-economic factors among women in Madrid, Spain.

Background: Intimate partner violence (IPV) is a public health problem with significant consequences on women's health. This study estimates the prevalence of intimate partner violence by type among Madrid's female population and assesses the association with socio-economic variables.

Methods: We conducted a cross-sectional study in 2004, 2136 women aged 18-70 years, living in the Madrid region with a partner or who had been in contact with an ex-partner in the previous year, were interviewed by telephone.