136 research outputs found

    Modelling and analysis of spiking neural P systems with anti-spikes using Pnet lab

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    a b s t r a c t Petri Nets are promising methods for modelling and simulating biological systems. Spiking Neural P system with anti-spikes (SN PA systems) is a biologically inspired computing model that incorporates two types of objects called spikes and anti-spikes thus representing binary information in a natural way. In this paper, we propose a methodology to simulate SN PA systems using a Petri net tool called Pnet Lab. It provides a promising way for SN PA systems because of its parallel execution semantics and appropriateness to represent typical working processes of these systems. This enables us to verify system properties, system soundness and to simulate the dynamic behaviour

    Spiking Neural P Systems. Recent Results, Research Topics

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    After a quick introduction of spiking neural P systems (a class of P systems inspired from the way neurons communicate by means of spikes, electrical impulses of identical shape), and presentation of typical results (in general equivalence with Turing machines as number computing devices, but also other issues, such as the possibility of handling strings or infinite sequences), we present a long list of open problems and research topics in this area, also mentioning recent attempts to address some of them. The bibliography completes the information offered to the reader interested in this research area.Ministerio de Educación y Ciencia TIN2006-13425Junta de Andalucía TIC-58

    Membrane computing: traces, neural inspired models, controls

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    Membrane Computing:Traces, Neural Inspired Models, ControlsAutor: Armand-Mihai IonescuDirectores: Dr. Victor Mitrana (URV)Dr. Takashi Yokomori (Universidad Waseda, Japón)Resumen Castellano:El presente trabajo está dedicado a una área muy activa del cálculo natural (que intenta descubrir la odalidad en la cual la naturaleza calcula, especialmente al nivel biológico), es decir el cálculo con membranas, y más preciso, a los modelos de membranas inspirados de la funcionalidad biológica de la neurona.La disertación contribuye al área de cálculo con membranas en tres direcciones principales. Primero, introducimos una nueva manera de definir el resultado de una computación siguiendo los rastros de un objeto especificado dentro de una estructura celular o de una estructura neuronal. A continuación, nos acercamos al ámbito de la biología del cerebro, con el objetivo de obtener varias maneras de controlar la computación por medio de procesos que inhiben/de-inhiben. Tercero, introducimos e investigamos en detallo - aunque en una fase preliminar porque muchos aspectos tienen que ser clarificados - una clase de sistemas inspirados de la manera en la cual las neuronas cooperan por medio de spikes, pulsos eléctricos de formas idénticas.English summary:The present work is dedicated to a very active branch of natural computing (which tries to discover the way nature computes, especially at a biological level), namely membrane computing, more precisely, to those models of membrane systems mainly inspired from the functioning of the neural cell.The present dissertation contributes to membrane computing in three main directions. First, we introduce a new way of defining the result of a computation by means of following the traces of a specified object within a cell structure or a neural structure. Then, we get closer to the biology of the brain, considering various ways to control the computation by means of inhibiting/de-inhibiting processes. Third, we introduce and investigate in a great - though preliminary, as many issues remain to be clarified - detail a class of P systems inspired from the way neurons cooperate by means of spikes, electrical pulses of identical shapes

    Spiking Neural Networks

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    Biopolymeric microbeads as a 3D scaffold for soft tissue engineering

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    The increase of different types of cell cultures, which can be used for the in vitro studies of physiological and/or pathological processes, has introduced the need to improve culture techniques through the use of materials and culture media that promote growth, recreating a cellular micro-environment that can be asserted in in vivo condition. Therefore, it is important to design and develop new biologically sustainable methods, such as to contribute to the \u201ccloser-to-in vivo\u201d condition. In particular, the design of a 3D in vitro model of neuronal culture is an important step to better understand the mechanisms of cell-cell communication, synaptogenesis and neurophysiological circuits. In order to mimic the ECM environment, a granular, porous and soft structure is preferred in the design of an artificial neural network. The granular structure is preferred due to the fact that CNS tissue seems to be organized as a greater proportion of the microscale tissue, that can be thought of as granular. For this reason, the thesis is focused on the production and characterization of bipolymeric microbeads as a 3D scaffold for soft tissue engineering. The biopolymer Chitosan is presented as an alternative adhesion factor and support for 2D and 3D neuronal cell cultures. Chitosan is a copolymer of glucosamine and N-acetyl-glucosamine, obtained by the deacetylation of chitin; it is well known for its low-cost, biocompatibility, biodegradability, muco-adhesiveness, antibacterial activity as well as its bioaffinity. Chitosan backbone shows positive charges of primary ammines that favor the electrostatic interactions with the negatively charged cell membranes promoting cell adhesion and growth. The standard studies focoused on the development of nervous system, have been performed using traditional monolayer culture onto supports modified by extracellular matrix components or synthetic biopolymers such as poly-ornithine and poly-lysine which are expressed at stages critical for neuronal differentiation in situ and are functional in neurite outgrowth in vitro, acting as adhesion proteins. Morphological and functional characterization of 2D neuronal culture grew up onto chitosan susbtrates are carried out and compared with the gold standard reported in literature, in order to validate the ability of chitosan to support neuronal adhesion, networks development and the differentiation capacity. 3D cultured neurons on chitosan microbeads based-scaffold, showed a structural development of a functional network that are more representative of the in vivo environment. The studies reported in this thesis, successfully demonstrate the alternative use of the polysaccharide chitosan as adhesion factor and as a structural component for 2D/3D neuronal cultures. Definitely, thanks to its low cost and versatility, it could be easily functionalized for the fabrication of personalized of in vitro models. In this thesis, a new technology to converts monodisperse microbead hydrogels to fine powders, is reported. Microengineered emulsion-to-powder (MEtoP) technology generates microgels with all the molecular, colloidal, and bulk characteristics of fresh microbeas upon resuspension in aqueous media. GelMA microbeads are fabricated by microfluidic technique, that is one of the most effective techniques, and allows precise tuning of the compositions and geometrical characteristics of microbeads. Gelatin-methacryloyl (GelMA) is a semi-synthetic hydrogel which consists of gelatin derivatized with methacrylamide and methacrylate groups. These hydrogels provide cells with an optimal biological environment (e.g., RGD motifs for adhesion) and can be quickly photo-crosslinked, which provide shape fidelity and stability at physiological temperature. MEtoP technology is based on protecting the dispersed phase of an emulsion to preserve its physical and chemical cues during harsh freezing and lyophilization procedures. This technology avoids the persistent problems of colloids, including difficulty in sterilization, bacterial and viral contamination, impaired stability, high processing costs, and difficult packaging and transportation

    Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

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    © 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

    Digital neural circuits : from ions to networks

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    PhD ThesisThe biological neural computational mechanism is always fascinating to human beings since it shows several state-of-the-art characteristics: strong fault tolerance, high power efficiency and self-learning capability. These behaviours lead the developing trend of designing the next-generation digital computation platform. Thus investigating and understanding how the neurons talk with each other is the key to replicating these calculation features. In this work I emphasize using tailor-designed digital circuits for exactly implementing bio-realistic neural network behaviours, which can be considered a novel approach to cognitive neural computation. The first advance is that biological real-time computing performances allow the presented circuits to be readily adapted for real-time closed-loop in vitro or in vivo experiments, and the second one is a transistor-based circuit that can be directly translated into an impalpable chip for high-level neurologic disorder rehabilitations. In terms of the methodology, first I focus on designing a heterogeneous or multiple-layer-based architecture for reproducing the finest neuron activities both in voltage-and calcium-dependent ion channels. In particular, a digital optoelectronic neuron is developed as a case study. Second, I focus on designing a network-on-chip architecture for implementing a very large-scale neural network (e.g. more than 100,000) with human cognitive functions (e.g. timing control mechanism). Finally, I present a reliable hybrid bio-silicon closed-loop system for central pattern generator prosthetics, which can be considered as a framework for digital neural circuit-based neuro-prosthesis implications. At the end, I present the general digital neural circuit design principles and the long-term social impacts of the presented work

    On microelectronic self-learning cognitive chip systems

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    After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory. From this interdisciplinary background support, we have motivations for novel approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research. And after reviewing some relevant background of robotic control methods followed by most recent advanced cognitive controllers, this Ph.D. thesis suggests that amongst many well-known ways of designing operational technologies, the design methodologies of those leading-edge high-tech devices such as cognitive chips that may well lead to intelligent machines exhibiting conscious phenomena should crucially be restricted to extremely well defined constraints. Roboticists also need those as specifications to help decide upfront on otherwise infinitely free hardware/software design details. In addition and most importantly, we propose these specifications as methodological guidelines tightly related to ethics and the nowadays well-identified workings of the human body and of its psyche

    Flexibility vs consistency: Quantifying differences in neuromodulatory elicited patterns of activity

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    Central pattern generating circuits underly fundamental behaviors such as respiration or locomotion and are under the influence of neuromodulators. The presence of neuromodulators is thought to confer flexibility to these circuits to generate distinct patterns of activity to meet distinct behavioral needs. Network output flexibility can be achieved by distinct classes of neuromodulators, those which have convergent cellular actions but divergent circuit actions or by those which have divergent cellular actions but convergent circuit actions. Both classes of neuromodulator exist in the stomatogastric nervous system of the crab Cancer borealis and influence the activity of a central pattern generating circuit in the stomatogastric ganglion, the pyloric network. The ability of both classes of neuromodulator, when applied individually, to generate qualitatively and quantitatively distinct patterns of activity has been demonstrated with respect to a baseline activity state. While it is assumed that each individual neuromodulator’s activity pattern is distinct, there has yet to be a fully quantitative description of the degree of difference between two modulated activity patterns. It is also unlikely that any single circuit will be under the influence of only a single neuromodulator at any point. Therefore, the possibility of generating distinct network outputs increases with each distinct combination of neuromodulators. While the actions of individual neuromodulators have been explored, the consequences of co-modulation on the pyloric network’s output are less understood. Previous attempts at quantifying the effects of a neuromodulator on the pyloric network output relied on evaluating only a single, often multi-dimensional, attribute of activity at a time and statistically testing the dependent parameters of that attribute with statistics that assume independence. This dissertation uses a new approach to quantify and statistically test how different one neuromodulator elicited pattern of activity is from another, preserving the inherent multi-dimensional nature of the attributes evaluated. The results of this dissertation show that the pyloric network output is able to generate statistically distinct network outputs with individual neuromodulators; however, flexibility is lost in favor of consistency under co-modulatory conditions

    Mechanisms of Feedback in the Visual System

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    Feedback is an ubiquitous feature of neural systems though there is little consensus on the roles of mechanisms involved with feedback. We set up an in vivo preparation to study and characterize an accessible and isolated feedback loop within the visual system of the leopard frog, Rana pipiens. We recorded extracellularly within the nucleus isthmi, a nucleus providing direct topographic feedback to the optic tectum, a nucleus that receives the vast majority of retinal output. The optic tectum and nucleus isthmi of the amphibian are homologous structures to the superior colliculus and parabigeminal nucleus in mammals, respectively. We formulated a novel threshold for detecting neuronal spikes within a low signal-to-noise environment, as exists in the nucleus isthmi due to its high density of small neuronal cell bodies. Combining this threshold with a recently developed spike sorting procedure enabled us to extract simultaneous recordings from up to 7 neurons at a time from a single extracellular electrode. We then stimulated the frog using computer driven dynamic spatiotemporal visual stimuli to characterize the responses of the nucleus isthmi neurons. We found that the responses display surprisingly long time courses to simple visual stimuli. Furthermore, we found that when stimulated with complex contextual stimuli the response of the nucleus isthmi is quite counter-intuitive. When a stimulus is presented outside of the classical receptive field along with a stimulus within the receptive field, the response is actually higher than the response to just a stimulus within the classical receptive field. Finally, we compared the responses of all of the simultaneously recorded neurons and, together with data from in vitro experiments within the nucleus isthmi, conclude that the nucleus isthmi of the frog is composed of just one electrophysiological population of cells
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