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The molecular and cellular organization of the primate cerebellum remains poorly characterized. We obtained single-cell spatial transcriptomic atlases of macaque, marmoset, and mouse cerebella and identified primate-specific cell subtypes, including Purkinje cells and molecular-layer interneurons, that show different expression of the glutamate ionotropic receptor Delta type subunit 2 ( GRID2 ) gene. Distinct gene expression profiles were found in anterior, posterior, and vestibular regions in all species, whereas region-selective gene expression was predominantly observed in the granular layer of primates and in the Purkinje layer of mice. Gene expression gradients in the cerebellar cortex matched well with functional connectivity gradients revealed with awake functional magnetic resonance imaging, with more lobule-specific differences between primates and mice than between two primate species. These comprehensive atlases and comparative analyses provide the basis for understanding cerebellar evolution and function.

Robot-to-robot learning, a specific case of social learning in robotics, enables multiple robots to share learned skills while completing a task. The literature offers various statements of its benefits. Robots using this type of social learning can reach a higher performance, an increased learning speed, or both, compared to robots using individual learning only. No general explanation has been advanced for the difference in observations, which make the results highly dependent on the particular system and parameter setting. In this paper, we perform a detailed analysis into the effects of robot-to-robot learning. As a result, we show that this type of social learning can reduce the sensitivity of the learning process to the choice of parameters in two ways. First, robot-to-robot learning can reduce the number of bad performing individuals in the population. Second, robot-to-robot learning can increase the chance of having a successful run, where success is defined as the presence of a high performing individual. Additionally, we show that robot-to-robot learning results in an increased learning speed for almost all parameter settings. Our results indicate that robot-to-robot learning is a powerful mechanism which leads to benefits in both performance and learning speed., (Copyright © 2019 Heinerman, Haasdijk and Eiben.)

We study evolutionary robot systems where not only the robot brains but also the robot bodies are evolvable. Such systems need to include a learning period right after 'birth' to acquire a controller that fits the newly created body. In this paper we investigate the possibility of bootstrapping infant robot learning through employing Lamarckian inheritance of parental controllers. In our system controllers are encoded by a combination of a morphology dependent component, a Central Pattern Generator (CPG), and a morphology independent part, a Compositional Pattern Producing Network (CPPN). This makes it possible to transfer the CPPN part of controllers between different morphologies and to create a Lamarckian system. We conduct experiments with simulated modular robots whose fitness is determined by the speed of locomotion, establish the benefits of inheriting optimized parental controllers, shed light on the conditions that influence these benefits, and observe that changing the way controllers are evolved also impacts the evolved morphologies., (Copyright © 2019 Jelisavcic, Glette, Haasdijk and Eiben.)

Selection is an essential component of any evolutionary system and analysing this fundamental force in evolution can provide relevant insights into the evolutionary development of a population. The 1990s and early 2000s saw a substantial number of publications that investigated selection pressure through methods such as takeover time and Markov chain analysis. Over the last decade, however, interest in the analysis of selection in evolutionary computing has waned. The established methods for analysis of selection pressure provide little insight when selection is based on more than comparison-of-fitness values. This can, for instance, be the case in coevolutionary systems, when measures unrelated to fitness affect the selection process (e.g., niching) or in systems that lack a crisply defined objective function. This article proposes two metrics that holistically consider the statistics of the evolutionary process to quantify selection pressure in evolutionary systems and so can be applied where traditionally used methods fall short. The metrics are based on a statistical analysis of the relation between reproductive success and a quantifiable trait: one method builds on an estimate of the probability that this relation is random; the other uses a correlation measure. These metrics provide convenient tools to analyse selection pressure and so allow researchers to better understand this crucial component of evolutionary systems. Both metrics are straightforward to implement and can be used in post-hoc analyses as well as during the evolutionary process, for example, to inform parameter control mechanisms. A number of case studies and a critical analysis show that the proposed metrics provide relevant and reliable measures of selection pressure.

This article provides an overview of evolutionary robotics techniques applied to online distributed evolution for robot collectives, namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. This article also presents a comprehensive summary of research published in the field since its inception around the year 2000, providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an online distributed learning method for designing collective behaviors in swarm-like collectives. This article concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research., (Copyright © 2018 Bredeche, Haasdijk and Prieto.)

Evolutionary robotics using real hardware is currently restricted to evolving robot controllers, but the technology for evolvable morphologies is advancing quickly. Rapid prototyping (3D printing) and automated assembly are the main enablers of robotic systems where robot offspring can be produced based on a blueprint that specifies the morphologies and the controllers of the parents. This article addresses the problem of gait learning in newborn robots whose morphology is unknown in advance. We investigate a reinforcement learning method and conduct simulation experiments using robot morphologies with different size and complexity. We establish that reinforcement learning does the job well and that it outperforms two alternative algorithms. The experiments also give insights into the online dynamics of gait learning and into the influence of the size, shape, and morphological complexity of the modular robots. These insights can potentially be used to predict the viability of modular robotic organisms before they are constructed.

Evolutionary robotics using real hardware has been almost exclusively restricted to evolving robot controllers, but the technology for evolvable morphologies is advancing quickly. We discuss a proof-of-concept study to demonstrate real robots that can reproduce. Following a general system plan, we implement a robotic habitat that contains all system components in the simplest possible form. We create an initial population of two robots and run a complete life cycle, resulting in a new robot, parented by the first two. Even though the individual steps are simplified to the maximum, the whole system validates the underlying concepts and provides a generic workflow for the creation of more complex incarnations. This hands-on experience provides insights and helps us elaborate on interesting research directions for future development.

Embodied evolutionary robotics is a sub-field of evolutionary robotics that employs evolutionary algorithms on the robotic hardware itself, during the operational period, i.e., in an on-line fashion. This enables robotic systems that continuously adapt, and are therefore capable of (re-)adjusting themselves to previously unknown or dynamically changing conditions autonomously, without human oversight. This paper addresses one of the major challenges that such systems face, viz. that the robots must satisfy two sets of requirements. Firstly, they must continue to operate reliably in their environment (viability), and secondly they must competently perform user-specified tasks (usefulness). The solution we propose exploits the fact that evolutionary methods have two basic selection mechanisms-survivor selection and parent selection. This allows evolution to tackle the two sets of requirements separately: survivor selection is driven by the environment and parent selection is based on task-performance. This idea is elaborated in the Multi-Objective aNd open-Ended Evolution (monee) framework, which we experimentally validate. Experiments with robotic swarms of 100 simulated e-pucks show that monee does indeed promote task-driven behaviour without compromising environmental adaptation. We also investigate an extension of the parent selection process with a 'market mechanism' that can ensure equitable distribution of effort over multiple tasks, a particularly pressing issue if the environment promotes specialisation in single tasks.

Ghrelin, the endogenous growth hormone secretagogue, has an important role in metabolic homeostasis. It exists in two major molecular forms: acylated (AG) and unacylated (UAG). Many studies suggest different roles for these two forms of ghrelin in energy balance regulation. In the present study, we compared the effects of acute intracerebroventricular administration of AG, UAG and their combination (AG+UAG) to young adult Wistar rats on food intake and central melanocortin system modulation. Although UAG did not affect food intake it significantly increased the number of c-Fos positive neurons in the arcuate (ARC), paraventricular (PVN) and solitary tract (NTS) nuclei. In contrast, UAG suppressed AG-induced neuronal activity in PVN and NTS. Central UAG also modulated hypothalamic expression of Mc4r and Bmp8b, which were increased and Mc3r, Pomc, Agrp and Ucp2, which were decreased. Finally, UAG, AG and combination treatments caused activation of c-Fos in POMC expressing neurons in the arcuate, substantiating a physiologic effect of these peptides on the central melanocortin system. Together, these results demonstrate that UAG can act directly to increase neuronal activity in the hypothalamus and is able to counteract AG-induced neuronal activity in the PVN and NTS. UAG also modulates expression of members of the melanocortin signaling system in the hypothalamus. In the absence of an effect on energy intake, these findings indicate that UAG could affect energy homeostasis by modulation of the central melanocortin system.

Evolution is one of the major omnipresent powers in the universe that has been studied for about two centuries. Recent scientific and technical developments make it possible to make the transition from passively understanding to actively using evolutionary processes. Today this is possible in Evolutionary Computing, where human experimenters can design and manipulate all components of evolutionary processes in digital spaces. We argue that in the near future it will be possible to implement artificial evolutionary processes outside such imaginary spaces and make them physically embodied. In other words, we envision the "Evolution of Things", rather than just the evolution of digital objects, leading to a new field of Embodied Artificial Evolution (EAE). The main objective of this paper is to present a unifying vision in order to aid the development of this high potential research area. To this end, we introduce the notion of EAE, discuss a few examples and applications, and elaborate on the expected benefits as well as the grand challenges this developing field will have to address.

We present a model of social learning of both language and skills, while assuming—insofar as possible—strict autonomy, virtual embodiment, and situatedness. This model is built by integrating various previous models of language development and social learning, and it is this integration that, under the mentioned assumptions, provides novel challenges. The aim of the article is to investigate what sociocognitive mechanisms agents should have in order to be able to transmit language from one generation to the next so that it can be used as a medium to transmit internalized rules that represent skill knowledge. We have performed experiments where this knowledge solves the familiar poisonous-food problem. Simulations reveal under what conditions, regarding population structure, agents can successfully solve this problem. In addition to issues relating to perspective taking and mutual exclusivity, we show that agents need to coordinate interactions so that they can establish joint attention in order to form a scaffold for language learning, which in turn forms a scaffold for the learning of rule-based skills. Based on these findings, we conclude by hypothesizing that social learning at one level forms a scaffold for the social learning at another, higher level, thus contributing to the accumulation of cultural knowledge.

We have investigated the expression of Hsp25, a heat shock protein constitutively expressed in motoneurons, in amyotrophic lateral sclerosis (ALS) mice that express G93A mutant SOD1 (G93A mice). Immunocytochemistry and Western blotting showed that a decrease of Hsp25 protein expression occurred in motoneurons of G93A mice prior to the onset of motoneuron death and muscle weakness. This decrease in Hsp25 expression also preceded the appearance of SOD1 aggregates as identified by cellulose acetate filtration and Western blot analysis. In contrast to Hsp25 protein levels, Hsp25 mRNA as determined by in situ hybridization and RT-PCR, remained unchanged. This suggests that the decrease in Hsp25 protein levels occurs post-transcriptionally. In view of the cytoprotective properties of Hsp25 and the temporal relationship between decreased Hsp25 expression and the onset of motoneuron death, it is feasible that reduced Hsp25 concentration contributes to the degeneration of motoneurons in G93A mice. These data are consistent with the idea that mutant SOD1 may reduce the availability of the protein quality control machinery in motoneurons.

Cytosolic Cu/Zn superoxide dismutase (SOD1) is a ubiquitous small cytosolic metalloenzyme, which catalyses the conversion of superoxide anion to hydrogen peroxide. Mutations in the SOD1 gene cause a familial form of amyotrophic lateral sclerosis (fALS). The mechanism by which mutant SOD1s cause the degeneration of motor neurons is not understood. Transgenic mice expressing multiple copies of fALS-mutant SOD1s develop an ALS-like motor neuron disease. Vacuolar degeneration of mitochondria has been identified as the main pathological feature associated with motor neuron death and paralysis in several lines of fALS-SOD1 mice. Using confocal and electron microscopy we show that mutant SOD1 is present at a high concentration in vacuolated mitochondria, where it colocalises with cytochrome c. Mutant SOD1 is also present in mildly swollen mitochondria prior to the appearance of vacuoles, suggesting that the leakage or translocation of mutant human SOD1 in mitochondria may be the primary event triggering their further degeneration. Vacuolated mitochondria containing SOD1 also occur in transgenic mice expressing a high concentration of wildtype human SOD1. In sum, our data suggest that both fALS-mutant and wild-type SOD1 may cross the mitochondrial outer membrane, and by doing so induce the degeneration of these mitochondria.

Cytosolic Cu/Zn superoxide dismutase (SOD1) is a ubiquitous small cytosolic metalloenzyme that catalyzes the conversion of superoxide anion to hydrogen peroxide (H(2)O(2)). Mutations in the SOD1 gene cause a familial form of amyotrophic lateral sclerosis (fALS). The mechanism by which mutant SOD1s causes ALS is not understood. Transgenic mice expressing multiple copies of fALS-mutant SOD1s develop an ALS-like motoneuron disease resembling ALS. Here we report that transgenic mice expressing a high concentration of wild-type human SOD1 (hSOD1(WT)) develop an array of neurodegenerative changes consisting of (1) swelling and vacuolization of mitochondria, predominantly in axons in the spinal cord, brain stem, and subiculum; (2) axonal degeneration in a number of long fiber tracts, predominantly the spinocerebellar tracts; and (3) at 2 years of age, a moderate loss of spinal motoneurons. Parallel to the development of neurodegenerative changes, hSOD1(WT) mice also develop mild motor abnormalities. Interestingly, mitochondrial vacuolization was associated with accumulation of hSOD1 immunoreactivity, suggesting that the development of mitochondrial pathology is associated with disturbed SOD1 turnover. In this study we also crossed hSOD1(WT) mice with a line of fALS-mutant SOD1 mice (hSOD1(G93A)) to generate "double" transgenic mice that express high levels of both wild-type and G93A mutant hSOD1. The "double" transgenic mice show accelerated motoneuron death, earlier onset of paresis, and earlier death as compared with hSOD1(G93A) littermates. Thus in vivo expression of high levels of wild-type hSOD1 is not only harmful to neurons in itself, but also increases or facilitates the deleterious action of a fALS-mutant SOD1. Our data indicate that it is important for motoneurons to control the SOD1 concentration throughout their processes, and that events that lead to improper synthesis, transport, or breakdown of SOD1 causing its accumulation are potentially dangerous., (Copyright 2000 Academic Press.)

Apolipoprotein E4 (apoE4) has been identified as a major risk factor for Alzheimer disease (AD). Previously it has been reported that levels of apoE are reduced in cerebrospinal fluid (CSF) of AD patients. Because it is known that apoE4 affects plasma lipid metabolism, we examined whether the presence of apoE4 might correlate with an altered lipid metabolism in the CSF of control subjects and AD patients. ApoE and lipid concentrations were determined in postmortem ventricular CSF of 30 neuropathologically confirmed AD cases and 31 age-matched control patients. ApoE genotyping was performed on frozen brain tissue of the same patients. In line with other reports, we found an increased APOE*4 allele frequency in the AD group (0.461) when compared with the control group (0.225). ApoE levels in CSF of AD patients were not significantly reduced when compared with the controls (mean +/-SD: 63+/-55 and 82+/-62 microg/dL for AD and controls, respectively). However, in the CSF of AD patients levels of free and esterified cholesterol (0.13+/-0.09 and 0.25+/-0.19 mg/dL, and 0.25+/-0.19 and 0.42+/-0.34, respectively), phospholipids (0.2+/-0.1 and 3.5+/-5.0 mg/dL) and, suprisingly, also fatty acids (4.5+/-3.2 and 28.0+/-18.5 micromol/L) were found to be significantly reduced. After correction for age, sex, postmortem delay, and pH the levels of phospholipids, fatty acids, and free cholesterol were still significantly reduced (p = 0.021, p = 0.026, andp = 0.012, respectively). The apoE and lipid levels in CSF of AD-and control patients appeared not to be affected by the number of APOE*4 alleles. In conclusion, our results suggest an altered lipid homeostasis in the brain of AD patients that is not related to the presence of apoE4. It is, therefore, unlikely that an effect of apoE4 on brain lipid metabolism is the underlying mechanism behind the role of apoE4 in the development of AD.