Most sensory stimuli are temporal in structure. How action potentials encode the information incoming from sensory stimuli remains one of the central research questions in neuroscience. Precise spike timing is known to represent information in spiking neuronal networks, yet the information processing mechanisms of spiking neuronal networks is poorly understood. One feasible way to understand the processing mechanism of a spiking network is to associate the structural connectivity of the network with the corresponding functional behaviour. This work demonstrates the structure-function mapping of spiking networks evolved (or handcrafted) for a temporal pattern recognition task. The task is to recognise a specific order of the input signals so that the output neuron of the network spikes only for the correct placement and remains silent for all others. The minimal networks obtained for this task revealed two complementary roles of autapses in recognition. First, autapses enable a seamless transition to the next network state when a new input signal arrives. Second, in the absence of the input signal, they allow the network to maintain a network state for an extended period, a form of memory. To show that the recognition task is accomplished by transitions between network states, we map the network states of a functional spiking neural network (SNN) onto the states of a finite-state transducer (FST). Finally, based on our understanding, we define rules for constructing the topology of a network handcrafted for recognising a subsequence of signals in a particular order. The analysis of minimal networks recognising patterns of different lengths revealed a positive correlation between the pattern length and the number of autaptic connections in the network. Furthermore, in agreement with the behaviour of neurons in the network, we were able to associate specific functional roles of 'locking,' 'switching,' and 'accepting' to neurons.

Epigenetic tracking (ET) is a model of development that is capable of generating diverse, arbitrary, complex three-dimensional cellular structures starting from a single cell. The generated structures have a level of complexity (in terms of the number of cells) comparable to multicellular biological organisms. In this article, we investigate the evolvability of the development of a complex structure inspired by the “French flag” problem: an “Italian Anubis” (a three-dimensional, doglike figure patterned in three colors). Genes during development are triggered in ET at specific developmental stages, and the fitness of individuals during simulated evolution is calculated after a certain stage. When this evaluation stage was allowed to evolve, genes that were triggered at later stages of development tended to be incorporated into the genome later during evolutionary runs. This suggests the emergence of the property of terminal addition in this system. When the principle of terminal addition was explicitly incorporated into ET, and was the sole mechanism for introducing morphological innovation, evolvability improved markedly, leading to the development of structures much more closely approximating the target at a much lower computational cost.

Biological and artificial spiking neural networks process information by changing their states in response to the temporal patterns of input and of the activity of the network itself. Here we analyse very small networks, evolved to recognize three signals in a specific pattern (ABC) in a continuous temporal stream of signals (...CABCACB...). This task can be accomplished by networks with just four neurons (three interneurons and one output). We show that evolving the networks in the presence of noise and variation of the intervals of silence between signals biases the solutions towards networks that can maintain their states (a form of memory), while the majority of networks evolved without variable intervals between signals cannot do so. We demonstrate that in most networks, the evolutionary process leads to the presence of superfluous connections that can be pruned without affecting the ability of the networks to perform the task and, if the unpruned network can maintain memory, so does the pruned network. We then analyse how these small networks can perform their tasks, using a paradigm of finite state transducers. This analysis shows that self-excitatory loops (autapses) in these networks are crucial for both the recognition of the pattern and for memory maintenance.

To understand how biological and bio-inspired complex computational networks can function in the presence of noise and damage, we have evolved very small spiking neural networks in the presence of ...

We evolved spiking neural network controllers for simple animats, allowing for these networks to change topologies and weights during evolution. The animats’ task was to discern one correct pattern (emitted from target objects) amongst other different wrong patterns (emitted from distractor objects), by navigating towards targets and avoiding distractors in a 2D world. Patterns were emitted with variable silences between signals of the same pattern in the attempt of creating a state memory. We analyse the network that is able to accomplish the task perfectly for patterns consisting of two signals, with 4 interneurons, maintaining its state (although not infinitely) thanks to the recurrent connections.

This article provides an overview of the 6 workshops held in conjunction with PPSN 2018 in Coimbra, Portugal. For each workshop, we list title, organizers, aim and scope as well as the accepted contributions.

Multiplicative or divisive changes in tuning curves of individual neurons to one stimulus (“input”) as another stimulus (“modulation”) is applied, called gain modulation, play an important role in perception and decision making. Since the presence of modulatory synaptic stimulation results in a multiplicative operation by proportionally changing the neuronal input-output relationship, such a change affects the sensitivity of the neuron but not its selectivity. Multiplicative gain modulation has commonly been studied at the level of single neurons. Much less is known about arithmetic operations at the network level. In this work we have evolved small networks of spiking neurons in which the output neurons respond to input with non-linear tuning curves that exhibit gain modulation—the best network showed an over 3-fold multiplicative response to modulation. Interestingly, we have also obtained a network with only 2 interneurons showing an over 2-fold response.

We evolve both topology and synaptic weights of recurrent very small spiking neural networks in the presence of noise on the membrane potential. The noise is at a level similar to the level observed in biological neurons. The task of the networks is to recognise three signals in a particular order (a pattern ABC) in a continuous input stream in which each signal occurs with the same probability. The networks consist of adaptive exponential integrate and fire neurons and are limited to either three or four interneurons and one output neuron, with recurrent and self-connections allowed only for interneurons. Our results show that spiking neural networks evolved in the presence of noise are robust to the change of neuronal parameters. We propose a procedure to approximate the range, specific for every neuronal parameter, from which the parameters can be sampled to preserve, at least for some networks, high true positive rate and low false discovery rate. After assigning the state of neurons to states of the network corresponding to states in a finite state transducer, we show that this simple but not trivial computational task of temporal pattern recognition can be accomplished in a variety of ways.

We evolved spiking neural networks (SNNs) to control animats in a task requiring temporal pattern recognition and foraging in a 2D environment with two types of objects emitting patterns: a target and a distractor. The target emits a specific temporal pattern composed of two components, while the distractor emits random patterns that are all the other combinations of these two components. The fitness function rewarded finding targets and avoiding distractors. We show that the evolved animats are robust to changes of the number of objects in the environment, strength of the actuators, duration of signals, intervals between signals in the pattern and between patterns. Our long term goal is to understand the mechanisms governing the neural networks that accomplish simple but not trivial computational tasks inspired by minimally cognitive abilities of animals, such as phonotaxis.

We obtained, with artificial evolution, very small (one or two interneurons, one output neuron) spiking neural networks (SNNs) recognizing a simple temporal pattern in a continuous input stream. The patterns the network evolved to recognize consisted of three different signals. In other words, the task was equivalent to searching in a stream (sequence) of three symbols (say, ABBCACBC..) for a specific subsequence (ABC). The fitness function we used rewarded spiking after the occurrence of the correct pattern (subsequence), and penalized spikes elsewhere. We found out that the networks did not go below two interneurons when they evolved to solve this task with a brief interval of silence between signals. However-surprisingly-for a longer interval of silences between signals the task could be accomplished with just one interneuron. We then analyzed how the spiking networks work by mapping the states of the network onto states of Finite State Machines-a general model of computation on time series. Our long term goal is to understand the mechanisms governing the neural networks that accomplish computational tasks in a way that is robust to noise and damage.

One of the central questions of biology is how complex biological systems can continue functioning in the presence of perturbations, damage, and mutational insults. This paper investigates evolution of spiking neural networks, consisting of adaptive exponential neurons. The networks are encoded in linear genomes in a manner inspired by genetic networks. The networks control a simple animat, with two sensors and two actuators, searching for targets in a simple environment. The results show that the presence of noise on the membrane voltage during evolution allows for evolution of efficient control and robustness to perturbations to the value of the neural parameters of neurons.

This paper analyses the process of the creation of management plans for two marine protected areas in Puck Bay, Poland, belonging to the European Natura 2000 network. The review of documents, observations of public consultations and interviews with stakeholders allowed to identify the limitations of the process in terms of governance and legitimacy. Legitimacy—accountability of decision-makers; transparency and consistency of their decisions; and their consideration for the opinions of stakeholders—is a prerequisite for a wide support for conservation measures. Legitimate governance faces unique challenges in post-transition countries, such as Poland, where citizens tend to mistrust decision-makers and experts, and where the opinions of local citizens and municipalities were often ignored in the past. Because the creation of Natura 2000 sites—required under the EU Habitats and Birds Directives when Poland accessed to the EU—was not participatory, local communities consider these conservation measures as imposed and threatening their economic well-being. The analysis in this paper showed that transparency, consistency and accountability of the decision-making process could improve if the institutional responsibilities did not overlap, and if some key players, including the experts, did not play many, potentially conflicting, roles. The main conclusion is that the process is still based on bureaucratic and formal rules, even though a shift towards a more participative approach has started. Completing this shift will require replacing the participation rhetoric with participation practice, together with evidence-based decision making, and embracing openness and public debate on uncertainty.

A large scale analysis presented in this article focuses on biological and physiological variety of bacteriophages. A collection of 83 bacteriophages, isolated from urban sewage and able to propagate in cells of different bacterial hosts, has been obtained (60 infecting Escherichia coli , 10 infecting Pseudomonas aeruginosa , 4 infecting Salmonella enterica , 3 infecting Staphylococcus sciuri , and 6 infecting Enterococcus faecalis ). High biological diversity of the collection is indicated by its characteristics, both morphological (electron microscopic analyses) and biological (host range, plaque size and morphology, growth at various temperatures, thermal inactivation, sensitivity to low and high pH, sensitivity to osmotic stress, survivability upon treatment with organic solvents and detergents), and further supported by hierarchical cluster analysis. By the end of the research no larger collection of phages from a single environmental source investigated by these means had been found. The finding was confirmed by whole genome analysis of 7 selected bacteriophages. Moreover, particular bacteriophages revealed unusual biological features, like the ability to form plaques at low temperature (4 °C), resist high temperature (62 °C or 95 °C) or survive in the presence of an organic solvents (ethanol, acetone, DMSO, chloroform) or detergent (SDS, CTAB, sarkosyl) making them potentially interesting in the context of biotechnological applications.

mously reviewed papers for the journal during the preparation of Volume 21. Their generosity, judicious judgment, and prompt response substantially helped us publish a journal that both is timely and has exacting scientific standards. The responsibility for sustaining the quality and relevance of the Journal rests largely in the hands of our reviewers. We at the Journal, and all of our readers, are deeply indebted to them. It is a genuine pleasure to thank them collectively for their dedicated service. (If anyone has been inadvertently omitted from this list, we sincerely apologize and ask them to let us know so that we can include their name in a future list of referees.)

The ability to search for resources is an example of a minimally cognitive behavior---a behavior shown by even the simplest animals, and that can be explored using simple robots [1]. Even very simple networks (such as natural and artificial genetic or neural networks) allow for control of the simplest search behaviors [2]. Moreover, this cognitive task can be made more difficult [3], so it can be seen as a possible stepping step toward advanced cognitive skills, both in biology and robotics [1]. In biology, the topology and synaptic weights of simple networks is rather evolved than learned. Here we used an artificial life platform called GReaNs that allows to use a genetic algorithm to evolve simple spiking neural networks (SNNs) using a mixed bio-inspired paradigm - the way the topology and weighs are encoded in artificial genomes is inspired by genetic networks, but computational units in the network are modeled as either leaky integrate and fire neurons with a fixed threshold or adaptive-exponential integrate and fire neurons [3]. We evolved SNNs with GReaNs to control simple artificial robots (animats) whose task was to search for targets in a 2-dimensional artificial environment. The targets can be seen as food pellets from which food diffuses, and is sensed by robot's two smell/taste sensors, on the left and right front. The robot also has two actuators which generate thrust on the left and right back; when the thrust on one side is larger, the animat moves in a circle; when the thrusts are the same, the robot goes forward in a straight line. We have designed three ways to present the strength of the sensed signal to the network, and three ways to relate the thrust generated by the two actuators to the activity of the corresponding two motor neurons in the network. All the tested setups allowed us to evolve robots with correct search behavior. Out of three setups for sensors, two can be seen as biologically realistic. In one of them, easier to evolve, the sensory information was preprocessed. Pre-processing consisted of calculating the difference and the sum of the smell sensed by the sensors, using the results as arguments of two sigmoid functions to obtain two values that determined the percentages of activation of two populations of 100 primary sensory neurons, each connected with one synapse (with the same weight) to secondary sensory neurons. In the second setup, a Hill function was used to map the smell of two sensors as current injection to two sensory neurons (in other words, here there was no pre-processing of the difference between the smell strength on two sides of the robot). Out of two setups for actuators we tested, again two were biologically realistic. It proved easier to evolve a setup in which constant thrust was generated in an actuator when the corresponding motor neurons spiked. In the less evolvable approach, the thrust was determined by summing the number of spikes of the corresponding motor neuron over a sliding temporal window.

The mechanisms that allow biological networks to recognize temporal patterns of spikes that encode sensory inputs are unclear. Here we extend our previous work [1], using an artificial life software platform, GReaNs [2] to evolve spiking neural networks as state machines to recognize temporal patterns of spikes. GReaNs implements a genetic algorithm to obtain the topology of the networks (and the weights of the synaptic connections), starting from a population of networks of neurons connected randomly. The encoding of the neural networks in the genome is inspired by the encoding of genetic networks in biological genomes; neurons in GReaNs are modeled as either leaky integrate and fire neurons with a fixed threshold (LIF) or adaptive-exponential integrate and fire neurons [2]. The number of neurons in the network is not limited in GReaNs, but here as previously [1] we limit the size of the networks so that the analysis of the way the networks function is simplified. In the computational task we consider, the network has several input neurons and one output neuron. A spike or burst received by an input neuron corresponds to a certain symbol (for example, sound with a specific frequency; a flash of light with a specific color). The output neuron should be active only after the network receives a certain sequence of symbols (a temporal pattern). Our preliminary results with LIF networks with a fixed threshold networks suggest that the presence of recurrent connections in the network allows the interneurons to reach plateau subthreshold states that provide a memory of what symbols have been received thus far. Here we will investigate the robustness of this solution to noise in the network, and then discuss the possibility to extend the paradigm to evolve spiking networks to accept regular languages.

In contrast to the first part of life (development), ageing appears to be under less strict genetic control. The precise timing of events so characteristic of development seems to loosen its grasp, while stochastic and environmental factors seem to become the dominant force. Evolutionary theories put forward a decreasing evolutionary pressure over the course of life as the reason behind this pattern, yet dissenting views on ageing as a genetically programmed process linger. In this paper we address this dissent by presenting insights from an artificial evolutionary-developmental system, ET, and propose a new evo-devo theory of ageing-a theory that sees ageing as a continuation of development in the postreproductive period. In this theory both development and ageing are under genetic control. Nonetheless, while gene expression patterns that drive development are optimised by evolution, patterns that drive ageing are not optimised, because evolutionary pressure decreases with age. For these reasons, during ageing the changes orchestrated by genes are "pseudorandom"- deterministic but erratic-and their effects on an individual's health are more likely to be detrimental than beneficial. As such, they contribute to the continuous deterioration of bodily functions that characterise ageing.

Soil contamination with heavy metals is a widespread problem, especially prominent on grounds lying in the vicinity of mines, smelters, and other industrial facilities. Many such areas are located in Southern Poland; they are polluted mainly with Pb, Zn, Cd, or Cu, and locally also with Cr. As for now, little is known about most bacterial species thriving in such soils and even less about a core bacterial community—a set of taxa common to polluted soils. Therefore, we wanted to answer the question if such a set could be found in samples differing physicochemically and phytosociologically. To answer the question, we analyzed bacterial communities in three soil samples contaminated with Pb and Zn and two contaminated with Cr and lower levels of Pb and Zn. The communities were assessed with 16S rRNA gene fragments pyrosequencing. It was found that the samples differed significantly and Zn decreased both diversity and species richness at species and family levels, while plant species richness did not correlate with bacterial diversity. In spite of the differences between the samples, they shared many operational taxonomic units (OTUs) and it was possible to delineate the core microbiome of our sample set. The core set of OTUs comprised members of such taxa as Sphingomonas , Candidatus Solibacter , or Flexibacter showing that particular genera might be shared among sites ~40 km distant.

Nervous systems of biological organisms use temporal patterns of spikes to encode sensory input, but the mechanisms that underlie the recognition of such patterns are unclear. In the present work, we explore how networks of spiking neurons can be evolved to recognize temporal input patterns without being able to adjust signal conduction delays. We evolve the networks with GReaNs, an artificial life platform that encodes the topology of the network (and the weights of connections) in a fashion inspired by the encoding of gene regulatory networks in biological genomes. The number of computational nodes or connections is not limited in GReaNs, but here we limit the size of the networks to analyze the functioning of the networks and the effect of network size on the evolvability of robustness to noise. Our results show that even very small networks of spiking neurons can perform temporal pattern recognition in the presence of input noise.