Brain-Inspired Spiking Neural Network Controller for a Neurorobotic Whisker System

It is common for animals to use self-generated movements to actively sense the surrounding environment. For instance, rodents rhythmically move their whiskers to explore the space close to their body. The mouse whisker system has become a standard model for studying active sensing and sensorimotor integration through feedback loops. In this work, we developed a bioinspired spiking neural network model of the sensorimotor peripheral whisker system, modeling trigeminal ganglion, trigeminal nuclei, facial nuclei, and central pattern generator neuronal populations. This network was embedded in a virtual mouse robot, exploiting the Human Brain Project's Neurorobotics Platform, a simulation platform offering a virtual environment to develop and test robots driven by brain-inspired controllers. Eventually, the peripheral whisker system was adequately connected to an adaptive cerebellar network controller. The whole system was able to drive active whisking with learning capability, matching neural correlates of behavior experimentally recorded in mice

Protein kinase CK2 is widely expressed in follicular, Burkitt and diffuse large B-cell lymphomas and propels malignant B-cell growth.

Serine-threonine kinase CK2 is highly expressed and pivotal for survival and proliferation in multiple myeloma, chronic lymphocytic leukemia and mantle cell lymphoma. Here, we investigated the expression of \u3b1 catalytic and \u3b2 regulatory CK2 subunits by immunohistochemistry in 57 follicular (FL), 18 Burkitt (BL), 52 diffuse large B-cell (DLBCL) non-Hodgkin lymphomas (NHL) and in normal reactive follicles. In silico evaluation of available Gene Expression Profile (GEP) data sets from patients and Western blot (WB) analysis in NHL cell-lines were also performed. Moreover, the novel, clinical-grade, ATP-competitive CK2-inhibitor CX-4945 (Silmitasertib) was assayed on lymphoma cells. CK2 was detected in 98.4% of cases with a trend towards a stronger CK2\u3b1 immunostain in BL compared to FL and DLBCL. No significant differences were observed between Germinal Center B (GCB) and non-GCB DLBCL types. GEP data and WB confirmed elevated CK2 mRNA and protein levels as well as active phosphorylation of specific targets in NHL cells. CX-4945 caused a dose-dependent growth-arresting effect on GCB, non-GCB DLBCL and BL cell-lines and it efficiently shut off phosphorylation of NF-\u3baB RelA and CDC37 on CK2 target sites. Thus, CK2 is highly expressed and could represent a suitable therapeutic target in BL, FL and DLBCL NHL

Bayesian Integration in a Spiking Neural System for Sensorimotor Control

The brain continuously estimates the state of body and environment, with specific regions that are thought to act as Bayesian estimator, optimally integrating noisy and delayed sensory feedback with sensory predictions generated by the cerebellum. In control theory, Bayesian estimators are usually implemented using high-level representations. In this work, we designed a new spike-based computational model of a Bayesian estimator. The state estimator receives spiking activity from two neural populations encoding the sensory feedback and the cerebellar prediction, and it continuously computes the spike variability within each population as a reliability index of the signal these populations encode. The state estimator output encodes the current state estimate. We simulated a reaching task at different stages of cerebellar learning. The activity of the sensory feedback neurons encoded a noisy version of the trajectory after actual movement, with an almost constant intrapopulation spiking variability. Conversely, the activity of the cerebellar output neurons depended on the phase of the learning process. Before learning, they fired at their baseline not encoding any relevant information, and the variability was set to be higher than that of the sensory feedback (more reliable, albeit delayed). When learning was complete, their activity encoded the trajectory before the actual execution, providing an accurate sensory prediction; in this case, the variability was set to be lower than that of the sensory feedback. The state estimator model optimally integrated the neural activities of the afferent populations, so that the output state estimate was primarily driven by sensory feedback in prelearning and by the cerebellar prediction in postlearning. It was able to deal even with more complex scenarios, for example, by shifting the dominant source during the movement execution if information availability suddenly changed. The proposed tool will be a critical block within integrated spiking, brain-inspired control systems for simulations of sensorimotor tasks

Urban society and the English Revolution : the archaeology of the new Jerusalem

The English Revolution has long been a defining subject of English historiography, with a large and varied literature that reflects continuing engagement with the central themes of civil conflict, and deep-rooted social, political and religious change. By contrast, this period has failed to catch the imagination of archaeologists. This research seeks to understand the world of the English Revolution through its material expression in English towns. Identifying the material expressions of the period is central to developing an archaeological understanding of the period. The clearest material expressions are found, in the fortifications that were built to protect towns, the destruction that was wrought on towns and in the reconstruction of the material world of English towns. Towns, like any other artefact, have their meanings. These meanings are multivalent and ever shifting, defined by the interaction of their material fabric and those who experience it. As these meanings change over time, they can be traced through the structures and artefacts of the town, and through the myths and legends that accrete on them. Understanding the interactions of material, myth and memory allows archaeologists to understand the true meaning of the urban built environment to generate a deeper and more nuanced understanding of the nature of the English urban culture of the period. Towns were fundamental to the English imagination as much as they were economically, politically or socially important. The English Revolution sits at the heart of the accepted conception of historical archaeology, but has been curiously neglected by historical archaeologists. The cultural conflict of this period embodies the themes that are central to historical archaeology, and nowhere is this more apparent than in urban culture.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Spiking Neural Network With Distributed Plasticity Reproduces Cerebellar Learning in Eye Blink Conditioning Paradigms

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 application

I sistemi carbonatici giurassici della Sardegna orientale (Golfo di Orosei) ed eventi deposizionali nel sistema carbonatico giurassico-cretacico della Nurra (Sardegna nord-occidentale)

This field trip gives a panoramic of the facies association and sedimentological-stratigraphic evolution of Jurassic-Cretaceous depositional systems of eastern (Golfo di Orosei) and western (Nurra) Sardinia. Carbonate deposition in western Sardinia occurred in an epeiric sea during Jurassic and Cretaceous whereas carbonates of the eastern Sardinia figure out a complex depositional settings with intraplatformal basins facing the Alpine Tethys from a basal transgression in the Bajocian to Berriasian. The presence of partly coeval succession allows a comparison between these two depositional systems and highlights relation with global and regional events. The Jurassic-Cretaceous carbonate succession of Sardinia shows similarities with coeval succession of the Provencal-Pyrenean domain (Nurra), nevertheless differences, both in terms of facies characters and distribution and range of stratigraphic gaps, occur between the successions of eastern Sardinia. These differences can be ascribed to different paleogeographic and depositional settings

Multiscale modeling of neuronal dynamics in hippocampus CA1

The development of biologically realistic models of brain microcircuits and regions constitutes currently a very relevant topic in computational neuroscience. One of the main challenges of such models is the passage between different scales, going from the microscale (cellular) to the meso (microcircuit) and macroscale (region or whole-brain level), while keeping at the same time a constraint on the demand of computational resources. In this paper we introduce a multiscale modeling framework for the hippocampal CA1, a region of the brain that plays a key role in functions such as learning, memory consolidation and navigation. Our modeling framework goes from the single cell level to the macroscale and makes use of a novel mean-field model of CA1, introduced in this paper, to bridge the gap between the micro and macro scales. We test and validate the model by analyzing the response of the system to the main brain rhythms observed in the hippocampus and comparing our results with the ones of the corresponding spiking network model of CA1. Then, we analyze the implementation of synaptic plasticity within our framework, a key aspect to study the role of hippocampus in learning and memory consolidation, and we demonstrate the capability of our framework to incorporate the variations at synaptic level. Finally, we present an example of the implementation of our model to study a stimulus propagation at the macro-scale level, and we show that the results of our framework can capture the dynamics obtained in the corresponding spiking network model of the whole CA1 area

Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue

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