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Aging drives cognitive decline, and mitochondrial dysfunction is a hallmark of age-induced neurodegeneration. Recently, we demonstrated that astrocytes secrete functional mitochondria (Mt), which help adjacent cells to resist damage and promote repair after neurological injuries. However, the relationship between age-dependent changes in astrocytic Mt function and cognitive decline remains poorly understood. Here, we established that aged astrocytes secret less functional Mt compared to young astrocytes. We found the aging factor C-C motif chemokine 11 (CCL11) is elevated in the hippocampus of aged mice, and that its level is reduced upon systemic administration of young Mt, in vivo. Aged mice receiving young Mt, but not aged Mt improved cognitive function and hippocampal integrity. Using a CCL11-induced aging-like model in vitro, we found that astrocytic Mt protect hippocampal neurons and enhance a regenerative environment through upregulating synaptogenesis-related gene expression and anti-oxidants that were suppressed by CCL11. Moreover, the inhibition of CCL11-specific receptor C-C chemokine receptor 3 (CCR3) boosted the expression of synaptogenesis-related genes in the cultured hippocampal neurons and restored the neurite outgrowth. This study suggests that young astrocytic Mt can preserve cognitive function in the CCL11-mediated aging brain by promoting neuronal survival and neuroplasticity in the hippocampus.

Microglia (MG) play critical roles in modulating inflammation and interferon signaling is a driver of neuroinflammation in various neurodegenerative diseases. Recently, Ifitm3 (a member of the “interferon-inducible transmembrane” family) has been revealed as a molecular mediator between neuroinflammation and neurodegeneration. However, expression of Ifitm3 in MG under inflammatory conditions, is not well described. Methods: To define the expression of Ifitm3 following stroke, permanent distal middle cerebral artery occlusion was performed with aged mice (18-22 mos). Brains were isolated at post-stroke day (PSD) 14 for single-cell RNAseq (scRNAseq), and at PSD 3, 7, and 14 for immunostaining and qRT-PCR. Primary microglial culture was established to evaluate mRNA levels of Ifitm3 and other pro-inflammatory genes to cytokine treatment. Results: Our scRNAseq revealed that Ifitm3 is among the top 10 genes upregulated in MG following stroke in aged brains. To validate these findings, we utilized a stroke model in aged mice and primary cell culture. We found that ischemic injury causes an induction of Ifitm3 in the striatum and peri-infarcted area following stroke, with peak induction PSD 7 (n=3-5, pIfitm3 was markedly induced in the damaged cortex of PSD 3 brains (n=4-6). We found that Ifitm3 expression is induced in primary MG following treatment with proinflammatory mediators (TNFα/IFNγ) for 6 hours (n=5-6, pIl1b and Casp1 . There was a close correlation between Ifitm3 and Casp1 expression, suggesting a potential of Ifitm3 in inflammasome activation. Conclusions: We demonstrated the profound induction of Ifitm3 in activated MG within the stroke brain in aged mice and in response to inflammatory factors in primary microglia culture. These studies identify Ifitm3 as a novel player involved in microglial modulation in response to a variety of inflammatory conditions. Future studies are warranted to determine if Ifitm3 deletion or inhibition can reduce neuroinflammation in the stroke brain.

Microglia, the resident innate immune cells of the brain, become more highly reactive with aging and diseased conditions. In collaboration with other cell types in brains, microglia can contribute both to worsened outcome following stroke or other neurodegenerative diseases and to the recovery process by changing their phenotype toward reparative microglia. Recently, IFITM3 (a member of the “interferon-inducible transmembrane” family) has been revealed as a molecular mediator between amyloid pathology and neuroinflammation. Expression of IFITM3 in glial cells, especially microglia following stroke, is not well described. Here, we present evidence that ischemic stroke causes an increase in IFITM3 expression along with increased microglial activation marker genes in aged brains. To further validate the induction of IFITM3 in post-stroke brains, primary microglia and microglial-like cells were exposed to a variety of inflammatory conditions, which significantly induced IFITM3 as well as other inflammatory markers. These findings suggest the critical role of IFITM3 in inducing inflammation. Our findings on the expression of IFITM3 in microglia and in aged brains following stroke could establish the basic foundations for the role of IFITM3 in a variety of neurodegenerative diseases, particularly those that are prevalent or enhanced in the aged brain.

Resumo Este ensaio destaca o perfil do trabalhador de área rural, portador da Esquistossomose mansônica, doença endêmica adquirida durante suas atividades laborais em áreas alagadas da baixada maranhense. Faz-se uma análise da legislação previdenciária e trabalhista utilizada para fins de concessão de benefícios e do nexo causal que estabelece a relação entre a situação de trabalho e o surgimento da doença. Para tal, utilizamos pesquisa bibliográfica sobre a temática e pesquisa documental do plano jurídico formal da previdência. O estudo aponta para a necessidade de reconhecer esta relação nas regiões endêmicas, no sentido de aprimorar o proposto na “Lista de Doenças Relacionadas ao Trabalho”.

This study highlights the profile of rural workers with schistosomiasis mansoni, an endemic disease acquired during their work activities in flooded areas in the Baixada Maranhense. In order to analyze the social security and labor legislation used to grant benefits and the causal link that establishes the relationship between the work situation and the onset of the disease, we performed a bibliographical research on the topic and a documentary research on the formal legal plan of social security. This study addresses the need to recognize this relationship in endemic regions in order to improve what is proposed by the List of Work-Related Diseases.

Innate lymphoid cells are the innate counterpart to CD4+ T cells that are mediated by the same transcription factors and produce similar cytokines. ILCs are being investigated in many different disease states, but the field current lacks foundational information on ILC representation whether it be in tissues, between males and females, or in aging as these are all vital components in disease etiology and severity. Our descriptive study used flow cytometry to characterize ILCs compared to the entire CD45+ (e.g., lymphocyte) and lineage negative (e.g., ILC) compartments to understand their homeostatic balance and plasticity. Moreover, we defined ILC2 expression and subsets based on their cytokine production and created several mathematical models to elucidate the correlation of extra- and intra-cellular ILC2 markers from least to most complex. ILC studies would benefit from more unbiased, holistic experiments including RNA-seq and mass spectroscopy to further define ILCs in steady state before adding more complex pathways like different disease states to enhance translational value and therapeutic targeting of these cells.

Astrocytes release functional mitochondria (Mt) that play regulatory and prosurvival functions on entering adjacent cells. We recently demonstrated that these released Mts could enter microglia to promote their reparative/prophagocytic phenotype that assists in hematoma cleanup and neurological recovery after intracerebral hemorrhage (ICH). However, the relevance of astrocytic Mt transfer into neurons in protecting brain after ICH is unclear. Here, we found that ICH causes a robust increase in superoxide generation and elevated oxidative damage that coincides with loss of the mitochondrial enzyme manganese superoxide dismutase (Mn-SOD). The damaging effect of ICH was reversed by intravenous transplantation of astrocytic Mt, which on entering the brain (and neurons), restored Mn-SOD levels and reduced neurological deficits in male mice subjected to ICH. Using an in vitro ICH-like injury model in cultured neurons, we established that astrocytic Mt on entering neurons prevented reactive oxygen species-induced oxidative stress and neuronal death by restoring neuronal Mn-SOD levels while at the same time promoted neurite extension and upregulation of synaptogenesis-related gene expression. Furthermore, we found that Mt genome-encoded small peptide humanin, which is normally abundant in Mt, could simulate Mt-transfer effect on neuronal Mn-SOD expression, oxidative stress, and neuroplasticity under ICH-like injury. This study demonstrates that adoptive astrocytic Mt transfer enhances neuronal Mn-SOD-mediated antioxidative defense and neuroplasticity in the brain, which potentiate functional recovery following ICH. SIGNIFICANCE STATEMENT Mitochondrial dysfunction and antioxidant defense play essential roles in brain damage after ICH. Astrocytes release functional Mt that enters adjacent cells to help brain homeostatic function. Here, we show that systemic transplantation of astrocytic Mt restores ICH-impaired neuronal antioxidative defense, enhances neurite outgrowth, and improves stroke recovery after ICH. Our study suggests that systemic transplantation of astrocytic Mt could be considered as a novel and potentially promising strategy for ICH treatment.

Aging affects immunologic responses by a global suppression of the immune system, including dysregulation of cytokine mediators, leading to increased inflammation throughout all systems, termed inflammaging. However, understanding mechanisms of healthy aging can bypass this effect. Inflammaging also leads to poor outcomes during brain injury, making immune-targeting therapeutics tantamount to overall brain health and longevity. Two candidate immune cells microglia and group 2 innate lymphoid cells (ILC2s) may control immune responses in the aged brain Therefore, we hypothesized that ILC2s are the brains’ gatekeepers of microglia polarization to a reparative, M2-like phenotype under: (1) homeostatic conditions and (2) with aging. To address this, we used flow cytometry to quantify ILC2s between young and aged male and female C57Bl6/J mice, showing an age-related increase in aged males. Moreover, when we stimulated ILC2s, the data indicated an age-related increase in cytokine production in males. We also utilized conditioned media transfer experiments from stimulated young and aged ILC2s to quiescent microglia and assessed their morphologic, genomic, proteomic, and functional changes using microscopy, qRT-PCR, Western immunoblotting, and phagocytosis assays. We showed more complex microglia morphology (indicating microglial priming) and an increase in reparative mRNA, proteins, and phagocytic function in microglia, which was augmented by aged ILC2s. Taken together, we have defined the capacity of ILC2 soluble factors to polarize microglia to a reparative phenotype and maintain this in aging. Supported by the NIH NINDS F31NS118983

The reactivation of very large landslides may cause severe damage to society. Its prevention and management requires detailed information on the geometry and structure of these landslides, but the use of standard techniques (boreholes) may be prohibitive from an economic point of view. To overcome these difficulties, geophysical techniques are of special interest because they allow for studying very large areas at a reasonable cost. In this paper, we present a case study wherein the analysis of ambient noise allowed us to produce a model of a large landslide near Granada (southern Spain). The geometry and location of the failure zone, as well as the assessment of the state of involved materials, were estimated by combining two available boreholes and different geophysical techniques (downhole tests and the spectral analysis of ambient noise, horizontal to vertical spectral ratios (HVSR) and the frequency-wavenumber (f-k) methods). The results have allowed us to differentiate between values within the landslide mass with respect to those of stable materials, and to perform for the first time a comprehensive geological model of this unstable mass. Differences were also observed within the landslide mass (earth flow vs. slide zones), which are attributed to differences in the degree of alteration and the disturbance of the internal structure of materials constituting the landslide mass. These results show that techniques based on the measurement of ambient noise are of special interest for studying very large, highly remolded landslide masses., EU (FEDER), by the Secretaría de Estado de Investigación, Desarrollo e Innovación of the Spanish government (projects CGL2015-65602-R and CGL2016-77688- R), Junta de Andalucía (project GGI3002IDIN), Programa Operativo FEDER Andalucía 2014–2020, Research Groups VIGROB-184 and VIGROB-116 (University of Alicante), University of Jaén

This article integrates work from authors from different research groups and has been funded by the EU Grant HBP (H2020 SGA3. 945539), the Spanish Ministry of Economy and Competitiveness (RTI2018-095993-B-I00), the national grant INTSENSO (MICINN-FEDER-PID2019-109991GB-I00), the regional grants of Junta de Andalucia (CEREBIO: JA FEDER P18-FR-2378, P18-RT-1193, and A-TIC-276-UGR18), and the University of Almeria (UAL18-TIC-A020-B)., This article extends a recent methodological workflow for creating realistic and computationally efficient neuron models whilst capturing essential aspects of singleneuron dynamics. We overcome the intrinsic limitations of the extant optimization methods by proposing an alternative optimization component based on multimodal algorithms. This approach can natively explore a diverse population of neuron model configurations. In contrast to methods that focus on a single global optimum, the multimodal method allows directly obtaining a set of promising solutions for a single but complex multi-feature objective function. The final sparse population of candidate solutions has to be analyzed and evaluated according to the biological plausibility and their objective to the target features by the expert. In order to illustrate the value of this approach, we base our proposal on the optimization of cerebellar granule cell (GrC) models that replicate the essential properties of the biological cell. Our results show the emerging variability of plausible sets of values that this type of neuron can adopt underlying complex spiking characteristics. Also, the set of selected cerebellar GrC models captured spiking dynamics closer to the reference model than the single model obtained with off-the-shelf parameter optimization algorithms used in our previous article. The method hereby proposed represents a valuable strategy for adjusting a varied population of realistic and simplified neuron models. It can be applied to other kinds of neuron models and biological contexts., EU Grant HBP H2020 SGA3. 945539, Spanish Ministry of Economy and Competitiveness RTI2018-095993-B-I00, national grant INTSENSO MICINN-FEDER-PID2019-109991GB-I00, Junta de Andalucia CEREBIO: JA FEDER P18-FR-2378 P18-RT-1193 A-TIC-276-UGR18, University of Almeria UAL18-TIC-A020-B

The basal ganglia (BG) represent a critical center of the nervous system for sensorial discrimination. Although it is known that Huntington’s disease (HD) affects this brain area, it still remains unclear how HD patients achieve paradoxical improvement in sensorial discrimination tasks. This paper presents a computational model of the BG including the main nuclei and the typical firing properties of their neurons. The BG model has been embedded within an auditory signal detection task. We have emulated the effect that the altered levels of dopamine and the degree of HD affectation have in information processing at different layers of the BG, and how these aspects shape transient and steady states differently throughout the selection task. By extracting the independent components of the BG activity at different populations, it is evidenced that early and medium stages of HD affectation may enhance transient activity in the striatum and the substantia nigra pars reticulata. These results represent a possible explanation for the paradoxical improvement that HD patients present in discrimination task performance. Thus, this paper provides a novel understanding on how the fast dynamics of the BG network at different layers interact and enable transient states to emerge throughout the successive neuron populations.

In this study, an inventory of landslides induced by the 2011 Lorca earthquake (Mw 5.1) has been used in order to develop a new procedure to obtain objective logic-tree weights for a probabilistic earthquake-induced landslide hazard analysis. The 2011 Lorca earthquake triggered more than 250 landslides, mainly of disrupted type. The logic-tree was designed having regard to variability of relevant geotechnical parameters involved in the problem and uncertainties associated with the use of several empirical relationships in order to compute Newmark displacements. For the purpose, the resulting hazard maps were compared with this landslide inventory, and weights estimated for each branch of the logic tree based on these results. The best model for seismic landslide hazard mapping for a moderate earthquake correctly identifies around 72% of landslide areas. Based on the set of parameters that comprises (depth of failure surface, specific weight, cohesion, friction angle and Newmark displacement model), the corresponding weights were objectively established. These weights are reliable enough for the obtaining seismic landslide hazard maps and may be implemented in similar environments characterized by moderate-low magnitude earthquakes (Mw

Aging is a non-modifiable risk factor for stroke. Aging is accompanied by chronic low-grade inflammation and gut dysbiosis (a pathological imbalance of microbial organisms in the gut). Age-related gut dysbiosis exacerbates stroke outcomes and can be reversed by manipulation of the gut microbiota (GM) via fecal microbiota transplants (FMTs) from young animals, or “rejuvenation.” But, the mechanisms that mediate these effects are poorly understood. Dendritic cells (DCs) are potent antigen presenting cells and uniquely equipped to mediate the effects of dysbiosis. DCs constantly sample their environment to regulate the inflammatory response to antigens and tissue injury. In this study we investigated the role of intestinal DCs in mediating the detrimental effects of dysbiosis on stroke outcomes. We hypothesize that age-related dysbiosis exacerbates stroke outcomes by inducing an inflammatory and migratory phenotype in intestinal DCs. We studied four cohorts of C57Bl6 mice consisting of (1) naïve young (4mo), (2) naïve aged (22-26mo), (3) middle-aged (14mo) with FMT from aged donors, and (4) naïve young with 60-min middle cerebral artery occlusion (MCAO). Phenotyping of DCs by flow cytometry was performed. Results: In our MCAO cohort, we found a significant increase in activated DCs in the gut (1.4% vs. 7.6%, p = 0.051) but a decrease in frequency of activated DCs in the brain (8.4% vs. 3.9%, p = 0.042). In our FMT cohort, frequency of intestinal DCs was altered in a subset-specific manner after FMT from aged donors. Specifically, our data showed that the MHC-II expression by DC subsets with a migratory phenotype (CD11b + DCs) and resident DCs (CD103 + DCs) were significantly increased when middle-aged mice received FMT from aged donors (p < 0.05). In our naïve cohorts, we found a significant decrease of MHC-II surface expression in brain DCs (p = 0.044) and a significant increase in splenic DCs (p = 0.049) with aging. Conclusion: Our findings show that frequency and maturity state of DCs significantly differ with aging in a tissue- specific manner and can be influenced by manipulation of the gut microbiota. Our data also support the notion that intestinal DCs are involved in mediating the detrimental effects of age-related gut dysbiosis on stroke outcomes.

The work presented here is a novel biological approach for the compliant control of a robotic arm in real time (RT). We integrate a spiking cerebellar network at the core of a feedback control loop performing torque-driven control. The spiking cerebellar controller provides torque commands allowing for accurate and coordinated arm movements. To compute these output motor commands, the spiking cerebellar controller receives the robot's sensorial signals, the robot's goal behavior, and an instructive signal. These input signals are translated into a set of evolving spiking patterns representing univocally a specific system state at every point of time. Spike-timing-dependent plasticity (STDP) is then supported, allowing for building adaptive control. The spiking cerebellar controller continuously adapts the torque commands provided to the robot from experience as STDP is deployed. Adaptive torque commands, in turn, help the spiking cerebellar controller to cope with built-in elastic elements within the robot's actuators mimicking human muscles (inherently elastic). We propose a natural integration of a bio inspired control scheme, based on the cerebellum, with a compliant robot. We prove that our compliant approach outperforms the accuracy of the default factory-installed position control in a set of tasks used for addressing cerebellar motor behavior: controlling six degrees of freedom (DoF) in smooth movements, fast ballistic movements, and unstructured scenario compliant movements.

Newmark displacement estimation is generally computed using empirical models. These models are estimated from large datasets that mainly comprise moderate-to-high magnitude events (Mw > 6.0). In this work, we study the performance of several of these models to study moderate-to-low magnitude scenarios. For this purpose, data from the Betic Cordillera, S Spain, with magnitudes ranging from Mw 3.5 to 6.3, were used to compare with model predictions. The results show that errors in the estimates depend on the magnitude of events or on the yielding acceleration considered to estimate the displacement. The availability of an appropriate range of data (magnitude and yielding acceleration), when defining the regression model, may overcome the differences due to specific characteristics of the seismotectonic context of the area where data derives from. The results also show that performance of models including several ground motion predictors is better than those based on a single parameter, regardless of the combination these predictors. Furthermore, regression models with polynomial forms present a better performance than other functions based on the logarithm of these predictors. Finally, new specific models for the Betic Cordillera are proposed, especially suitable for low magnitude events (< 5.0) and low critical accelerations (< 0.1 g), based on simplified polynomial forms of models. This work was partially founded by the Spanish Ministry of Economy and EU FEDER funds (project EPILATES, CGL2015-65602-R) and by the Research Group VIGROB-184 (University of Alicante). Research was also partially funded by the Consejería de Economía, Conocimiento, Empresa y Universidad (Junta de Andalucía), in the frame of the Programa Operativo FEDER Andalucía 2014-2020 – call made by the University of Jaén 2018.

Astrocytes are an integral component of the neurovascular unit where they act as homeostatic regulators, especially after brain injuries, such as stroke. One process by which astrocytes modulate homeostasis is the release of functional mitochondria (Mt) that are taken up by other cells to improve their function. However, the mechanisms underlying the beneficial effect of Mt transfer are unclear and likely multifactorial. Using a cell culture system, we established that astrocytes release both intact Mt and humanin (HN), a small bioactive peptide normally transcribed from the Mt genome. Further experiments revealed that astrocyte-secreted Mt enter microglia, where they induce HN expression. Similar to the effect of HN alone, incorporation of Mt by microglia (1) upregulated expression of the transcription factor peroxisome proliferator-activated receptor gamma and its target genes (including mitochondrial superoxide dismutase), (2) enhanced phagocytic activity toward red blood cells (anin vitromodel of hematoma clearance after intracerebral hemorrhage [ICH]), and (3) reduced proinflammatory responses. ICH induction in male mice caused profound HN loss in the affected hemisphere. Intravenously administered HN penetrated perihematoma brain tissue, reduced neurological deficits, and improved hematoma clearance, a function that normally requires microglia/macrophages. This study suggests that astrocytic Mt-derived HN could act as a beneficial secretory factor, including when transported within Mt to microglia, where it promotes a phagocytic/reparative phenotype. These findings also indicate that restoring HN levels in the injured brain could represent a translational target for ICH. These favorable biological responses to HN warrant studies on HN as therapeutic target for ICH.SIGNIFICANCE STATEMENTAstrocytes are critical for maintaining brain homeostasis. Here, we demonstrate that astrocytes secrete mitochondria (Mt) and the Mt-genome-encoded, small bioactive peptide humanin (HN). Mt incorporate into microglia, and both Mt and HN promote a “reparative” microglia phenotype characterized by enhanced phagocytosis and reduced proinflammatory responses. Treatment with HN improved outcomes in an animal model of intracerebral hemorrhage, suggesting that this process could have biological relevance to stroke pathogenesis.

The H/V technique is used throughout this research to characterize the soil column at different locations of the metropolitan area of Málaga (Southern Spain). This region stands as a good example of a continuously growing zone, mostly developed on soft alluvial sediments, with moderate to low seismicity. The H/V analysis of the noise measurements reveals frequency peak values ranging from 3 to 4 Hz on the NE of the study area, where the basin basement outcrops, to 0.3 Hz in the Guadalhorce River alluvial plain. We correlate the presence of the main H/V peak with the appearance of the basin basement materials in the sedimentary column; whereas secondary peaks obtained in some measurement points may be related to Quaternary and Pliocene sedimentary fill. Shear wave velocity profiles, inverted from the experimental H/V curves, data available from previous boreholes and superficial geology information are all integrated to provide four characteristic 2D cross-sections of the basin. In the ground model described here we distinguish four layers: a shallow layer that is probably linked to the recent Quaternary sediments (Vs from 100 to 500 m/s); an intermediate layer that is related to the presence of late Pliocene materials (Vs from 300 to 1000 m/s); a deep layer associated with early Pliocene materials filling the trough formed by the basin basement (Vs from 700 to 1600 m/s); and finally the basement formation that shows stable velocity at around 2000 m/s. This research has been partially funded by the Research Groups VIGROB-184 (University of Alicante) and by the Spanish Government (Grants CGL2011-30153-C02-02/BTE and CGL2011-25162), the FPU Program at the Ministry of Science and Innovation (AP2008-04686) and the Instituto Alicantino de Cultura Juan Gil-Albert.

Anthropomimetic robots sense, behave, interact, and feel like humans. By this definition, they require human-like physical hardware and actuation but also brain-like control and sensing. The most self-evident realization to meet those requirements would be a human-like musculoskeletal robot with a brain-like neural controller. While both musculoskeletal robotic hardware and neural control software have existed for decades, a scalable approach that could be used to build and control an anthropomimetic human-scale robot has not yet been demonstrated. Combining Myorobotics, a framework for musculoskeletal robot development, with SpiNNaker, a neuromorphic computing platform, we present the proof of principle of a system that can scale to dozens of neurally controlled, physically compliant joints. At its core, it implements a closed-loop cerebellar model that provides real-time, low-level, neural control at minimal power consumption and maximal extensibility. Higher-order (e.g., cortical) neural networks and neuromorphic sensors like silicon retinae or cochleae can be incorporated.

Land-use planning in regard of earthquake-triggered land- slides is usually implemented by means of the production of hazard maps. The well-known Newmark rigid block method- ology is the most frequent used approach for this purpose. In this method, slope stability is evaluated by the estimation of the Newmark displacement, which is used to set differ- ent categories of hazard. This methodology presents limita- tions due to the difficulty of incorporating the variability of the used variables. For that reason, the logic-tree approach has been used in order to incorporate the epistemic uncer- tainties and compute probabilistic seismic-landslide hazard maps. However, the used weights in the logic-tree are usually set for each branch based on an expert judgement or sub- jective criteria. This article provide data obtained from the use of logic-tree methodology; this dataset is useful for de- riving the unbiased weights to use in such methodology and in moderate-to-low magnitude scenarios. The data presented here are related to the article entitled “Obtaining suitable logic-tree weights for probabilistic earthquake-induced land- slide hazard analyses"., MINECO/FEDER, UE CGL2015-65602-R, MINECO CGL2017-83931-C3-1-P, Programa Operativo FEDER Andalucía 2014-2020 -call made by the University of Jaen 2018, Research group "Planetary Geodynamics, Active Tectonics and Related Risks" of the University Complutense of Madrid UCM-910368, Research group "Applied Geology and Hydrogeology" of the University of Alicante VIGROB-184

Studying and understanding the computational primitives of our neural system requires for a diverse and complementary set of techniques. In this work, we use the Neuro-robotic Platform (NRP)to evaluate the vestibulo ocular cerebellar adaptatIon (Vestibulo-ocular reflex, VOR)mediated by two STDP mechanisms located at the cerebellar molecular layer and the vestibular nuclei respectively. This simulation study adopts an experimental setup (rotatory VOR)widely used by neuroscientists to better understand the contribution of certain specific cerebellar properties (i.e. distributed STDP, neural properties, coding cerebellar topology, etc.)to r-VOR adaptation. The work proposes and describes an embodiment solution for which we endow a simulated humanoid robot (iCub)with a spiking cerebellar model by means of the NRP, and we face the humanoid to an r-VOR task. The results validate the adaptive capabilities of the spiking cerebellar model (with STDP)in a perception-action closed-loop (r- VOR)causing the simulated iCub robot to mimic a human behavior.

The January 25, 2016, Mw 6.3 Alborán Sea earthquake shook the autonomous city of Melilla (Spain) with a macro-seismic intensity of VI (EMS-98). In spite of this low intensity, significant non-structural damages were reported, whose cost was estimated in more than 13 million euros. The damages were concentrated in the modernist district, which is considered the most important and valuable part of the city. This scenario is not new in Melilla, since historical and instrumental seismicity studies based on intensities felt in Melilla have revealed that earthquakes with intensities of V–VI have a return period of approximately 25 years. However, seismic microzonation studies have not been carried out so far. In this paper, we present a seismic microzonation study based on seismic noise measurements and the foreshock, mainshock and aftershock records of the January 25, 2016, earthquake. The seismic signals were processed using the horizontal-to-vertical spectral ratio (HVSR) technique. The frequency amplification results were correlated with geological formations, and after that they were correlated with the distribution of damages. The lagoon and the recent alluvial deposits show the maximum number of damaged buildings and maximum frequency amplifications of 2–8 between 2 and 7 Hz. In the coastal deposits, some amplification in the same frequency range has been observed, but other formations show a minimum number of damaged buildings and a flat spectral response ratio. Two important factors in this damage pattern are the high vulnerability of ornamental facades characteristics (non-structural elements) of the modern architecture buildings and their location on the lagoon and the recent alluvial deposits where maximum site amplification is reached. The authors would like to thank the European Union ERDF for its financial support via the ‘‘Monitorización sísmica de deslizamientos. Criterios de reactivación y alerta temprana’’ at the ‘‘Programa Operativo FEDER de Andalucia 2007–2013’’ (G-GI3002-DIN). This work has also been partially funded by the Government of the autonomous city of Melilla via the contract 2017-GPRY-008 and by the EU (ERDF) and the MINECO under projects CGL2015-65602-R and CGL2016-80687-R.

Geomatic techniques based on digital images have become a very useful tool for landslide monitoring, quantifying landslide movements over a long period of time. One of these techniques is based on digital image correlation. Digital image correlation estimates 2D displacements by means of statistical techniques, automatically matching identical points in the two digital images. It may be applied using remote sensing or aerial images. Digital image correlation has been applied to multi-temporal aerial photographs at the Cerro Gordo landslide (Granada, Spain) which is badly affecting an urbanized area. This landslide consists of a main deep slide and several minor shallow slides, located around the head scarp or affecting some parts of the main landslide body. Slides involve existing landfills used for urban works as well as in situ materials (colluvial deposits and weathered schists). The period under study (2002–2010) has been divided into four subperiods of approximately two years each. Results provide horizontal displacement in the landslide area and surroundings which allow to assess the landslide movement evolution at different stages according to triggering factors and structural measures that were carried out to stabilize the affected area. Over the period of 2002–2004, displacement rates varied from 2 to 6 cm, meanwhile for the periods of 2004–2006 and 2006–2008 displacements continued with 4–6 cm rates. Then, the period of 2008–2010 generally shows a higher activity with more areas showing 6 cm displacement rates. Surface observations of these periods go along with both location and direction of displacements as shown by the results of digital image correlation techniques. Image correlation results have provided very useful information to understand the complex movements of the landslide as well as the effectiveness of the structural measures implemented.

Time-driven simulation methods in traditional CPU architectures perform well and precisely when simulating small-scale spiking neural networks. Nevertheless, they still have drawbacks when simulating large-scale systems. Conversely, event-driven simulation methods in CPUs and time-driven simulation methods in graphic processing units (GPUs) can outperform CPU time-driven methods under certain conditions. With this performance improvement in mind, we have developed an event-and-time-driven spiking neural network simulator suitable for a hybrid CPU-GPU platform. Our neural simulator is able to efficiently simulate bio-inspired spiking neural networks consisting of different neural models, which can be distributed heterogeneously in both small layers and large layers or subsystems. For the sake of efficiency, the low-activity parts of the neural network can be simulated in CPU using event-driven methods while the high-activity subsystems can be simulated in either CPU (a few neurons) or GPU (thousands or millions of neurons) using time-driven methods. In this brief, we have undertaken a comparative study of these different simulation methods. For benchmarking the different simulation methods and platforms, we have used a cerebellar-inspired neural-network model consisting of a very dense granular layer and a Purkinje layer with a smaller number of cells (according to biological ratios). Thus, this cerebellar-like network includes a dense diverging neural layer (increasing the dimensionality of its internal representation and sparse coding) and a converging neural layer (integration) similar to many other biologically inspired and also artificial neural networks.

Background and Purpose— Intracerebral hemorrhage (ICH) is a devastating disease with a 30-day mortality of ~50%. There are no effective therapies for ICH. ICH results in brain damage in 2 major ways: through the mechanical forces of extravasated blood and then through toxicity of the intraparenchymal blood components including hemoglobin/iron. LTF (lactoferrin) is an iron-binding protein, uniquely abundant in polymorphonuclear neutrophils (PMNs). After ICH, circulating blood PMNs enter the ICH-afflicted brain where they release LTF. By virtue of sequestrating iron, LTF may contribute to hematoma detoxification. Methods— ICH in mice was produced using intrastriatal autologous blood injection. PMNs were depleted with intraperitoneal administration of anti-Ly-6G antibody. Treatment of mouse brain cell cultures with lysed RBC or iron was used as in vitro model of ICH. Results— LTF mRNA was undetectable in the mouse brain, even after ICH. Unlike mRNA, LTF protein increased in ICH-affected hemispheres by 6 hours, peaked at 24 to 72 hours, and remained elevated for at least a week after ICH. At the single cell level, LTF was detected in PMNs in the hematoma-affected brain at all time points after ICH. We also found elevated LTF in the plasma after ICH, with a temporal profile similar to LTF changes in the brain. Importantly, mrLTF (recombinant mouse LTF) reduced the cytotoxicity of lysed RBC and FeCl 3 to brain cells in culture. Ultimately, in an ICH model, systemic administration of mrLTF (at 3, 24, and 48 hours after ICH) reduced brain edema and ameliorated neurological deficits caused by ICH. mrLTF retained the benefit in reducing behavioral deficit even with 24-hour treatment delay. Interestingly, systemic depletion of PMNs at 24 hours after ICH worsened neurological deficits, suggesting that PMN infiltration into the brain at later stages after ICH could be a beneficial response. Conclusions— LTF delivered to the ICH-affected brain by infiltrating PMNs may assist in hematoma detoxification and represent a powerful potential target for the treatment of ICH.

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fninf. 2017.00007/full#supplementary-material, Modeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under increasing levels of neural complexity., This study was supported by the European Union NR (658479-Spike Control), the Spanish National Grant NEUROPACT (TIN2013-47069-P) and by the Spanish National Grant PhD scholarship (AP2012-0906). We gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan GPUs for current EDLUT development.

Experimental studies of the Central Nervous System (CNS) at multiple organization levels aim at understanding how information is represented and processed by the brain's neurobiological substrate. The information processed within different neural subsystems is neurocomputed using distributed and dynamic patterns of neural activity. These emerging patterns can be hardly understood by merely taking into account individual cell activities. Studying how these patterns are elicited in the CNS under specific behavioral tasks has become a groundbreaking research topic in system neuroscience. This methodology of synthetic behavioral experimentation is also motivated by the concept of embodied neuroscience, according to which the primary goal of the CNS is to solve/facilitate the body-environment interaction.With the aim to bridge the gap between system neuroscience and biological control, this paper presents how the CNS neural structures can be connected/integrated within a body agent; in particular, an efficient neural simulator based on EDLUT (Ros et?al., 2006) has been integrated within a simulated robotic environment to facilitate the implementation of object manipulating closed loop experiments (action-perception loop). This kind of experiment allows the study of the neural abstraction process of dynamic models that occurs within our neural structures when manipulating objects.The neural simulator, communication interfaces, and a robot platform have been efficiently integrated enabling real time simulations. The cerebellum is thought to play a crucial role in human-body interaction with a primary function related to motor control which makes it the perfect candidate to start building an embodied nervous system as illustrated in the simulations performed in this work. We integrated EDLUT neural simulator within a simulated robotic environment.As an embodiment example, we implemented a cerebelar-like structure controlling a simulated arm.The neural robotic simulator combines signals in analog/spike domains.Neural simulator, interface, and robotic platform operate conjointly in real time.

On February 23, 2015, an earthquake of magnitude Mw 4.7 (Imax = V, scale EMS) struck the center of the Spain, triggering dozens of instabilities in taluses and natural slopes of an area characterized by low relief. These instabilities were characterized by: (1) very small size, most of them with volumes lower than 1 m3, and (2) to occur in rock masses affected by multiple discontinuities, which pre-defined blocks that fell down during the shaking. The inventory of instabilities of this earthquake has shown that most of the instabilities occurred on the slopes of the road network, although the larger instabilities were observed in natural slopes. The comparative analysis of this inventory with those made for other recent earthquakes occurred in the SE of Spain (1999, 2002, 2005 and 2011), all of them of similar magnitude Mw (between 4.7 and 5.1), allow to recognize that the vast majority of instabilities induced by these earthquakes were rock/soil falls, being other typologies of landslides very rare. In all cases, the size of instabilities triggered were small, usually with volumes of 1 m3 or less, reaching the larger volumes up to 500–1000 m3. Data available from these events point out that large landslides, as known in relation with historical earthquakes in Spain, cannot be induced by moderate to low magnitude earthquakes. Besides, slope morphology seems to control the location of induced instabilities. Thus, when the terrain is steep, as in the area affected by the earthquake in Lorca (2011, Mw 5.1), most of instabilities occur in natural slopes and affect the upper part of slopes. As the relief is less rugged, natural slopes instabilities are progressively less frequent until the extreme case of the 2015 event, when instabilities were located mostly on slopes of the road network.

Array measurements of ambient noise have become a useful technique to estimate the surface wave dispersion curves and subsequently the subsurface elastic parameters that characterize the studied soil. One of the logistical handicaps associated with this kind of measurements is the requirement of several stations recording at the same time, which limits their applicability in the case of research groups without enough infrastructure resources. In this paper, we describe the theoretical basis of the ESAC method and we deduce how the number of stations needed to implement any array layout can be reduced to only two stations. In this way, we propose a new methodology to implement an N stations array layout by using only M stations (M < N), which will be recording in different positions of the original prearranged N stations geometry at different times. We also provide some practical guidelines to implement the proposed approach and we show different examples where the obtained results confirm the theoretical foundations. Thus, the study carried out reflects that we can use a minimum of 2 stations to deploy any array layout originally designed for higher number of sensors. This work has been developed thanks to the financial support of the Spanish Government (CGL2011-25162), Junta de Andalucia and FEDER fundings (G-GI3002/IDIN).

This paper presents how a simple cerebellumlike architecture can infer corrective models in the framework of a control task when manipulating objects that significantly affect the dynamics model of the system. The main motivation of this paper is to evaluate a simplified bio-mimetic approach in the framework of a manipulation task. More concretely, the paper focuses on how the model inference process takes place within a feedforward control loop based on the cerebellar structure and on how these internal models are built up by means of biologically plausible synaptic adaptation mechanisms. This kind of investigation may provide clues on how biology achieves accurate control of non-stiff-joint robot with low-power actuators which involve controlling systems with high inertial components. This paper studies how a basic temporal-correlation kernel including long-term depression (LTD) and a constant long-term potentiation (LTP) at parallel fiber-Purkinje cell synapses can effectively infer corrective models. We evaluate how this spike-timing-dependent plasticity correlates sensorimotor activity arriving through the parallel fibers with teaching signals (dependent on error estimates) arriving through the climbing fibers from the inferior olive. This paper addresses the study of how these LTD and LTP components need to be well balanced with each other to achieve accurate learning. This is of interest to evaluate the relevant role of homeostatic mechanisms in biological systems where adaptation occurs in a distributed manner. Furthermore, we illustrate how the temporal-correlation kernel can also work in the presence of transmission delays in sensorimotor pathways. We use a cerebellumlike spiking neural network which stores the corrective models as well-structured weight patterns distributed among the parallel fibers to Purkinje cell connections.

It is widely assumed that the cerebellum is one of the main nervous centers involved in correcting and refining planned movement and accounting for disturbances occurring during movement, for instance, due to the manipulation of objects which affect the kinematics and dynamics of the robot-arm plant model. In this brief, we evaluate a way in which a cerebellar-like structure can store a model in the granular and molecular layers. Furthermore, we study how its microstructure and input representations (context labels and sensorimotor signals) can efficiently support model abstraction toward delivering accurate corrective torque values for increasing precision during different-object manipulation. We also describe how the explicit (object-related input labels) and implicit state input representations (sensorimotor signals) complement each other to better handle different models and allow interpolation between two already stored models. This facilitates accurate corrections during manipulations of new objects taking advantage of already stored models.

The brain generates many rhythmic activities, and the olivo-cerebellar system is not an exception. In recent years, the cerebellum has revealed activities ranging from low frequency to very high-frequency oscillations. These rhythms depend on the brain functional state and are typical of certain circuit sections or specific neurons. Interestingly, the granular layer, which gates sensorimotor and cognitive signals to the cerebellar cortex, can also sustain low frequency (7-25 Hz) and perhaps higher-frequency oscillations. In this review we have considered (i) how these oscillations are generated in the granular layer network depending on intrinsic electroresponsiveness and circuit connections, (ii) how these oscillations are correlated with those in other cerebellar circuit sections, and (iii) how the oscillating cerebellum communicates with extracerebellar structures. It is suggested that the granular layer can generate oscillations that integrate well with those generated in the inferior olive, in deep-cerebellar nuclei and in Purkinje cells. These rhythms, in turn, might play a role in cognition and memory consolidation by interacting with the mechanisms of long-term synaptic plasticity. (C) 2009 IBRO. Published by Elsevier Ltd. All rights reserved.

The interaction between seismic waves and slopes is an important topic to provide reliable scenarios for earthquake-(re)triggered landslides. The physical properties of seismic waves as well as slope topography and geology can significantly modify the local seismic response, influencing landslide triggering. A novel approach is here applied to two case studies in Andalusia (southern Spain) for computing the expected earthquake-induced displacements of existing landslide masses. Towards this aim, dynamic stress–strain numerical modelling was carried out using a selection of seismic signals characterized by different spectral content and energy. In situ geophysical measurements, consisting of noise records and temporary seismometric arrays, were carried out to control the numerical outputs in terms of local seismic response. The results consist of relationships between the characteristic period, Tm, of the seismic signals and the characteristic periods of the landslide masses, related to the thickness (Ts) and length (Tl), respectively. These relationships show that the larger the horizontal dimension (i.e. length of landslide) of a landslide is, the more effective the contribution (to the resulting coseismic displacement) of the long-period seismic waves is, as the maximum displacements are expected for a low Tm at each energy level of the input. On the other hand, when the local seismic response mainly depends on stratigraphy (i.e. landslide thickness), the maximum expected displacements occur close to the resonance period of the landslide, except for high-energy seismic inputs. The authors would like to thank the European Union ERDF for financial support via the “Monitorización sísmica de deslizamientos. Criterios de reactivación y alerta temprana” project of the “Programa Operativo FEDER de Andalucia 2007-2013”.

The cerebellar microcircuit has been the work bench for theoretical and computational modeling since the beginning of neuroscientific research. The regular neural architecture of the cerebellum inspired different solutions to the long-standing issue of how its circuitry could control motor learning and coordination. Originally, the cerebellar network was modeled using a statistical-topological approach that was later extended by considering the geometrical organization of local microcircuits. However, with the advancement in anatomical and physiological investigations, new discoveries have revealed an unexpected richness of connections, neuronal dynamics and plasticity, calling for a change in modeling strategies, so as to include the multitude of elementary aspects of the network into an integrated and easily updatable computational framework. Recently, biophysically accurate “realistic” models using a bottom-up strategy accounted for both detailed connectivity and neuronal non-linear membrane dynamics. In this perspective review, we will consider the state of the art and discuss how these initial efforts could be further improved. Moreover, we will consider how embodied neurorobotic models including spiking cerebellar networks could help explaining the role and interplay of distributed forms of plasticity. We envisage that realistic modeling, combined with closed-loop simulations, will help to capture the essence of cerebellar computations and could eventually be applied to neurological diseases and neurorobotic control systems.

Goal: In this study, we defined a realistic cerebellar model through the use of artificial spiking neural networks, testing it in computational simulations that reproduce associative motor tasks in multiple sessions of acquisition and extinction. Methods: By evolutionary algorithms, we tuned the cerebellar microcircuit to find out the near-optimal plasticity mechanism parameters that better reproduced human-like behavior in eye blink classical conditioning, one of the most extensively studied paradigms related to the cerebellum. We used two models: one with only the cortical plasticity and another including two additional plasticity sites at nuclear level. Results: First, both spiking cerebellar models were able to well reproduce the real human behaviors, in terms of both “timing” and “amplitude”, expressing rapid acquisition, stable late acquisition, rapid extinction, and faster reacquisition of an associative motor task. Even though the model with only the cortical plasticity site showed good learning capabilities, the model with distributed plasticity produced faster and more stable acquisition of conditioned responses in the reacquisition phase. This behavior is explained by the effect of the nuclear plasticities, which have slow dynamics and can express memory consolidation and saving. Conclusions: We showed how the spiking dynamics of multiple interactive neural mechanisms implicitly drive multiple essential components of complex learning processes. Significance: This study presents a very advanced computational model, developed together by biomedical engineers, computer scientists, and neuroscientists. Since its realistic features, the proposed model can provide confirmations and suggestions about neurophysiological and pathological hypotheses and can be used in challenging clinical applications.

The majority of operations carried out by the brain require learning complex signal patterns for future recognition, retrieval and reuse. Although learning is thought to depend on multiple forms of long-term synaptic plasticity, the way this latter contributes to pattern recognition is still poorly understood. Here, we have used a simple model of afferent excitatory neurons and interneurons with lateral inhibition, reproducing a network topology found in many brain areas from the cerebellum to cortical columns. When endowed with spike-timing dependent plasticity (STDP) at the excitatory input synapses and at the inhibitory interneuron–interneuron synapses, the interneurons rapidly learned complex input patterns. Interestingly, induction of plasticity required that the network be entrained into theta-frequency band oscillations, setting the internal phase-reference required to drive STDP. Inhibitory plasticity effectively distributed multiple patterns among available interneurons, thus allowing the simultaneous detection of multiple overlapping patterns. The addition of plasticity in intrinsic excitability made the system more robust allowing self-adjustment and rescaling in response to a broad range of input patterns. The combination of plasticity in lateral inhibitory connections and homeostatic mechanisms in the inhibitory interneurons optimized mutual information (MI) transfer. The storage of multiple complex patterns in plastic interneuron networks could be critical for the generation of sparse representations of information in excitatory neuron populations falling under their control.

The Ossa de Montiel (2015/02/23, Mw 4.7) earthquake struck the central part of Spain and was felt far from the epicenter (> 300 km). Even though ground shaking was slight (Imax = V, EMS-98 scale), the earthquake triggered many small rock falls, most at distances of 20–30 km from the epicenter, greater than previously recorded in S Spain (16 km) for earthquakes of similar magnitudes. The comparative analysis of available data for this event with records from other quakes of the Betic cordillera (S and SE Spain) seems to indicate a slower pattern of ground-motion attenuation in central Spain. This could explain why slope instabilities occurred at larger distances. Instability was more frequent, and occurred at larger distances, in road cuts than in natural slopes, implying that such slope types are highly susceptible to seismically induced landslides. This work has been funded by Spanish Ministry of Economy and Competitiveness, research project CGL2011-30153-C02-02, and by the research groups RNM-374 (Junta de Andalucía), TECTAC (UCM-910368) and VIGROB-184 (Universidad de Alicante). Mr. Loffredo acknowledges funding of the Università di Roma — La Sapienza (post-graduate grant) (Concorso A N.480 - Area CUN 04).

The cerebellum plays a crucial role in motor learning and it acts as a predictive controller. Modeling it and embedding it into sensorimotor tasks allows us to create functional links between plasticity mechanisms, neural circuits and behavioral learning. Moreover, if applied to real-time control of a neurorobot, the cerebellar model has to deal with a real noisy and changing environment, thus showing its robustness and effectiveness in learning. A biologically inspired cerebellar model with distributed plasticity, both at cortical and nuclear sites, has been used. Two cerebellum-mediated paradigms have been designed: an associative Pavlovian task and a vestibulo-ocular reflex, with multiple sessions of acquisition and extinction and with different stimuli and perturbation patterns. The cerebellar controller succeeded to generate conditioned responses and finely tuned eye movement compensation, thus reproducing human-like behaviors. Through a productive plasticity transfer from cortical to nuclear sites, the distributed cerebellar controller showed in both tasks the capability to optimize learning on multiple time-scales, to store motor memory and to effectively adapt to dynamic ranges of stimuli., This work was supported by grants of European Union: REALNET (FP7-ICT270434) and Human Brain Project (HBP-604102).

Eye blinking classical conditioning is one of the most extensively studied paradigms related to the cerebellum. In this work we have defined a realistic cerebellar model through the use of artificial spiking neural networks, testing it in computational simulations reproducing the eye blinking classical conditioning in multiple sessions of acquisition and extinction. We used two models: one with only the cortical plasticity and another with three plasticity sites, one plasticity at cortical level and two at nuclear level. We have compared the behavioral outcome of the two different models and proved that the model with a distributed plasticity produces a faster and more stable acquisition of conditioned responses in the reacquisition phase with respect to the single plasticity model. This behavior is explained by the effect of the nuclear plasticities, which have a slow dynamics and can express memory consolidation and savings.

The way long-term potentiation (LTP) and depression (LTD) are integrated within the different synapses of brain neuronal circuits is poorly understood. In order to progress beyond the identification of specific molecular mechanisms, a system in which multiple forms of plasticity can be correlated with large-scale neural processing is required. In this paper we take as an example the cerebellar network, in which extensive investigations have revealed LTP and LTD at several excitatory and inhibitory synapses. Cerebellar LTP and LTD occur in all three main cerebellar subcircuits (granular layer, molecular layer, deep cerebellar nuclei) and correspondingly regulate the function of their three main neurons: granule cells (GrCs), Purkinje cells (PCs) and deep cerebellar nuclear (DCN) cells. All these neurons, in addition to be excited, are reached by feed-forward and feed-back inhibitory connections, in which LTP and LTD may either operate synergistically or homeostatically in order to control information flow through the circuit. Although the investigation of individual synaptic plasticities in vitro is essential to prove their existence and mechanisms, it is insufficient to generate a coherent view of their impact on network functioning in vivo. Recent computational models and cell-specific genetic mutations in mice are shedding light on how plasticity at multiple excitatory and inhibitory synapses might regulate neuronal activities in the cerebellar circuit and contribute to learning and memory and behavioral control., This work was supported by European Union grants to ED [CEREBNETFP7-ITN238686, REAL NET FP7-ICT270434, Human Brain Project(HBP-604102)] and by Centro Fermi grant [13(14)] to LM.

A novel unconventional pseudostatic analysis is proposed here to infer on the sensitivity of a landslide to earthquakes characterized by different physical properties. Several sine waves with different amplitudes, frequencies and phases were applied to the landslide mass assuming limit equilibrium conditions. The unconventional approach was used for the Diezma landslide case study. The landslide is located 25 km from the city of Granada (Spain). Although the slope had repeatedly suffered small-scale stability problems since the construction of the A-92 highway, a larger failure occurred on 18 March 2001 and damaged the highway between kilometers 272.6 and 272.8. The landslide had an estimated volume of 1.2 Mm3 and involved a disordered deposit of silt and clay with heterometric blocks within the Numidoide Formation, which outcrops along the contact between the Malaguide and Dorsal domains of the Betic Cordillera mountain range. Despite the 18 million Euros spent since 1999 on geotechnical investigations and stabilization solutions, the numerous reactivations that occurred through 2010 and 2013 demonstrate the persistent activity of the landslide. The geometry of the large slope failure corresponding to the first activation of the Diezma landslide was used to back-analyze the stability of the slope based on a high-resolution engineering-geological model. The model was developed from the analysis of numerous borehole logs as well as from geophysical investigations consisting of seismic noise measurements. The results demonstrate that the safety factor (SF) of the Diezma landslide varies significantly for frequencies less than 1 Hz; moreover, unstable conditions are reached at frequency values between 0.5 and 1 Hz for water pressure distributions corresponding to Bishop factors (ru) between 0 and 0.36. To estimate the co-seismic displacements, the geometrical and mechanical properties of the landslide mass were used to derive its characteristic periods for thickness (Ts) and length (Tl), which were compared with the characteristic period of the earthquake (Tm). The results indicate that the maximum expected co-seismic displacements are up to 2 m for an earthquake with a Tm value close to 1 s and an Arias Intensity on the order of 1 m/s.

The cerebellum is involved in a large number of different neural processes, especially in associative learning and in fine motor control. To develop a comprehensive theory of sensorimotor learning and control, it is crucial to determine the neural basis of coding and plasticity embedded into the cerebellar neural circuit and how they are translated into behavioral outcomes in learning paradigms. Learning has to be inferred from the interaction of an embodied system with its real environment, and the same cerebellar principles derived from cell physiology have to be able to drive a variety of tasks of different nature, calling for complex timing and movement patterns. We have coupled a realistic cerebellar spiking neural network (SNN) with a real robot and challenged it in multiple diverse sensorimotor tasks. Encoding and decoding strategies based on neuronal firing rates were applied. Adaptive motor control protocols with acquisition and extinction phases have been designed and tested, including an associative Pavlovian task (Eye blinking classical conditioning), a vestibulo-ocular task and a perturbed arm reaching task operating in closed-loop. The SNN processed in real-time mossy fiber inputs as arbitrary contextual signals, irrespective of whether they conveyed a tone, a vestibular stimulus or the position of a limb. A bidirectional long-term plasticity rule implemented at parallel fibers-Purkinje cell synapses modulated the output activity in the deep cerebellar nuclei. In all tasks, the neurorobot learned to adjust timing and gain of the motor responses by tuning its output discharge. It succeeded in reproducing how human biological systems acquire, extinguish and express knowledge of a noisy and changing world. By varying stimuli and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions., European Union (Human Brain Project) REALNET FP7-ICT270434 CEREBNET FP7-ITN238686 HBP-604102