Spiking Neural P Systems. Recent Results, Research Topics

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

The Kinetic Basis of Self-Organized Pattern Formation

In his seminal paper on morphogenesis (1952), Alan Turing demonstrated that different spatio-temporal patterns can arise due to instability of the homogeneous state in reaction-diffusion systems, but at least two species are necessary to produce even the simplest stationary patterns. This paper is aimed to propose a novel model of the analog (continuous state) kinetic automaton and to show that stationary and dynamic patterns can arise in one-component networks of kinetic automata. Possible applicability of kinetic networks to modeling of real-world phenomena is also discussed.Comment: 8 pages, submitted to the 14th International Conference on the Synthesis and Simulation of Living Systems (Alife 14) on 23.03.2014, accepted 09.05.201

Towards a unified approach

"Decision-making in the presence of uncertainty is a pervasive computation. Latent variable decoding—inferring hidden causes underlying visible effects—is commonly observed in nature, and it is an unsolved challenge in modern machine learning. On many occasions, animals need to base their choices on uncertain evidence; for instance, when deciding whether to approach or avoid an obfuscated visual stimulus that could be either a prey or a predator. Yet, their strategies are, in general, poorly understood. In simple cases, these problems admit an optimal, explicit solution. However, in more complex real-life scenarios, it is difficult to determine the best possible behavior. The most common approach in modern machine learning relies on artificial neural networks—black boxes that map each input to an output. This input-output mapping depends on a large number of parameters, the weights of the synaptic connections, which are optimized during learning.(...)

On Trace Languages Generated by Spiking Neural P Systems

We extend to spiking neural P systems a notion investigated in the “stan- dard” membrane systems: the language of the traces of a distinguished object. In our case, we distinguish a spike by “marking” it and we follow its path through the neurons of the system, thus obtaining a language. Several examples are discussed and some preliminary results about this way of associating a language with a spiking neural P system are given, together with a series of topics for further research. For instance, we show that each regular language is the morphic image of a trace language intersected with a very particular regular language, while each recursively enumerable language over the one-letter alphabet is the projection of a trace language

Membrane computing: traces, neural inspired models, controls

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

Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 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

A new class of neural architectures to model episodic memory : computational studies of distal reward learning

A computational cognitive neuroscience model is proposed, which models episodic memory based on the mammalian brain. A computational neural architecture instantiates the proposed model and is tested on a particular task of distal reward learning. Categorical Neural Semantic Theory informs the architecture design. To experiment upon the computational brain model, embodiment and an environment in which the embodiment exists are simulated. This simulated environment realizes the Morris Water Maze task, a well established biological experimental test of distal reward learning. The embodied neural architecture is treated as a virtual rat and the environment it acts in as a virtual water tank. Performance levels of the neural architectures are evaluated through analysis of embodied behavior in the distal reward learning task. Comparison is made to biological rat experimental data, as well as comparison to other published models. In addition, differences in performance are compared between the normal and categorically informed versions of the architecture

Théorie des valeurs extrêmes pour systèmes dynamiques, avec applications au climat et en neurosciences

Throughout the thesis, we will discuss, improve and provide a conceptual framework in which methods based on recurrence properties of chaotic dynamics can be understood. We will also provide new EVT-based methods to compute quantities of interest and introduce new useful indicators associated to the dynamics. Our results will be mathematically rigorous, although emphasis will be placed on physical applications and numerical computations, as the use of such methods is developing rapidly. We will start by an introductory chapter to the dynamical theory of extreme events, in which we will describe the principal results of the theory that will be used throughout the thesis. After a small chapter where we introduce some objects that are characteristic of the invariant measure of the system, namely local dimensions and generalized dimensions, we devote the following chapters to the use of EVT to compute such dimensional quantities. One of these methods defines naturally a novel global indicator on the hyperbolic properties of the system. In these chapters, we will present several numerical applications of the methods, both in real world and idealized systems, and study the influence of different kinds of noise on these indicators. We will then investigate a matter of physical importance related to EVT : the statistics of visits in some particular small target subsets of the phase-space, in particular for partly random, noisy systems. The results presented in this section are mostly numerical and conjectural, but reveal some universal behavior of the statistics of visits. The eighth chapter makes the connection between several local quantities associated to the dynamics and computed using a finite amount of data (local dimensions, hitting times, return times) and the generalized dimensions of the system, that are computable by EVT methods. These relations, stated in the language of large deviation theory (that we will briefly present), have profound physical implications, and constitute a conceptual framework in which the distribution of such computed local quantities can be understood. We then take advantage of these connections to design further methods to compute the generalized dimensions of a system. Finally, in the last part of the thesis, which is more experimental, we extend the dynamical theory of extreme events to more complex observables, which will allow us to study phenomena evolving over long temporal scales. We will consider the example of firing cascades in a model of neural network. Through this example, we will introduce a novel approach to study such complex systems.Tout au long de la thèse, nous discuterons, améliorerons et fournirons un cadre conceptuel dans lequel des méthodes basées sur les propriétés de récurrence de dynamiques chaotiques peuvent être comprises. Nous fournirons également de nouvelles méthodes basées sur l’EVT pour calculer les quantités importantes associées à la dynamique. Nos résultats sont rigoureux d’un point de vue mathématique, même si l’accent sera mis sur les applications physiques et les calculs numériques, car l’utilisation de telles méthodes se développe rapidement. Nous commencerons par un chapitre introductif à la théorie dynamique des événements extrêmes, dans lequel nous décrirons les principaux résultats de la théorie qui seront utilisés tout au long de la thèse. Après un petit chapitre dans lequel nous introduisons certains objets caractéristiques de la mesure invariante du système, à savoir les dimensions locales et les dimensions généralisées, nous consacrons les chapitres suivants à l’utilisation de l’EVT pour calculer de telles quantités dimensionnelles. L’une de ces méthodes définit naturellement un nouvel indicateur global sur les propriétés hyperboliques du système. Dans ces chapitres, nous présenterons plusieurs applications numériques de ces méthodes, à la fois dans des systèmes réels et idéalisés, et étudierons l’influence de différents types de bruit sur ces indicateurs. Nous examinerons ensuite une question d’importance physique liée à l’EVT : les statistiques de visites dans certains sous-ensembles cibles spécifiques de l’espace de phase, en particulier pour les systèmes partiellement aléatoires. Les résultats présentés dans cette section sont principalement numériques et hypothétiques, mais révèlent un comportement universel des statistiques de visites. Le huitième chapitre établit la connexion entre plusieurs quantités locales associées à la dynamique et calculées à l’aide d’une quantité finie de données (dimensions locales, temps d’entées, temps de retour) et les dimensions généralisées du système, qui calculables par les méthodes EVT. Ces relations, énoncées dans le langage de la théorie des grandes déviations (que nous exposerons brièvement), ont de profondes implications physiques et constituent un cadre conceptuel dans lequel le fait de calculer une distribution étalée de ces quantités locales peut être comprise. Nous tirons ensuite parti de ces connexions pour concevoir d’autres méthodes permettant de calculer les dimensions généralisées d’un système. Enfin, dans la dernière partie de la thèse, qui est plus expérimentale, nous étendons la théorie dynamique des événements extrêmes à des observables plus complexes, ce qui nous permettra d’étudier des phénomènes évoluant sur de longues échelles temporelles. Nous allons considérer l’exemple des cascades d’excitation dans un modèle de réseau de neurones. À travers cet exemple, nous allons introduire une nouvelle approche pour étudier de tels systèmes complexes

Complex Dynamics in Dedicated / Multifunctional Neural Networks and Chaotic Nonlinear Systems

We study complex behaviors arising in neuroscience and other nonlinear systems by combining dynamical systems analysis with modern computational approaches including GPU parallelization and unsupervised machine learning. To gain insights into the behaviors of brain networks and complex central pattern generators (CPGs), it is important to understand the dynamical principles regulating individual neurons as well as the basic structural and functional building blocks of neural networks. In the first section, we discuss how symbolic methods can help us analyze neural dynamics such as bursting, tonic spiking and chaotic mixed-mode oscillations in various models of individual neurons, the bifurcations that underlie transitions between activity types, as well as emergent network phenomena through synergistic interactions seen in realistic neural circuits, such as network bursting from non-intrinsic bursters. The second section is focused on the origin and coexistence of multistable rhythms in oscillatory neural networks of inhibitory coupled cells. We discuss how network connectivity and intrinsic properties of the cells affect the dynamics, and how even simple circuits can exhibit a variety of mono/multi-stable rhythms including pacemakers, half-center oscillators, multiple traveling-waves, fully synchronous states, as well as various chimeras. Our analyses can help generate verifiable hypotheses for neurophysiological experiments on central pattern generators. In the last section, we demonstrate the inter-disciplinary nature of this research through the applications of these techniques to identify the universal principles governing both simple and complex dynamics, and chaotic structure in diverse nonlinear systems. Using a classical example from nonlinear laser optics, we elaborate on the multiplicity and self-similarity of key organizing structures in 2D parameter space such as homoclinic and heteroclinic bifurcation curves, Bykov T-point spirals, and inclination flips. This is followed by detailed computational reconstructions of the spatial organization and 3D embedding of bifurcation surfaces, parametric saddles, and isolated closed curves (isolas). The generality of our modeling approaches could lead to novel methodologies and nonlinear science applications in biological, medical and engineering systems