A Heterosynaptic Spiking Neural System for the Development of Autonomous Agents

Artificial neural systems for computation were first proposed three quarters of a century ago and the concepts developed by the pioneers still shape the field today. The first generation of neural systems was developed in the nineteen forties in the context of analogue electronics and the theoretical research in logic and mathematics that led to the first digital computers in nineteen forties and fifties. The second generation of neural systems implemented on digital computers was born in the nineteen fifties and great progress was made in the subsequent half century with neural networks being applied to many problems in pattern recognition and machine learning. Through this history there has been an interplay between biologically inspired neural systems and their implementation by engineers on digital machines. This thesis concerns the third generation of neural networks, Spiking Neural Networks, which is making possible the creation of new kinds of brain inspired computing architectures that offer the potential to increase the level of realism and sophistication in terms of autonomous machine behaviour and cognitive computing. This thesis presents the development and demonstration of a new theoretical architecture for third generation neural systems, the Integrate-and-Fire based Spiking Neural Model with extended Neuro-modulated Spike Timing Dependent Plasticity capabilities. This proposed architecture overcomes the limitation of the homosynaptic architecture underlying existing implementations of spiking neural networks that it lacks a natural spike timing dependent plasticity regulation mechanism, and this results in ‘run away’ dynamics. To overcome this ad hoc procedures have been implemented to overcome the ‘run away’ dynamics that emerge from the use of spike timing dependent plasticity among other hebbian-based plasticity rules. The new heterosynaptic architecture presented, explicitly abstracts the modulation of complex biochemical mechanisms into a simplified mechanism that is suitable for the engineering of artificial systems with low computational complexity. Neurons work by receiving input signals from other neurons through synapses. The difference between homosynaptic and heterosynaptic plasticity is that, in the former the change in the properties of a synapse (e.g. synaptic efficacy) depends on the point to point activity in either of the sending and receiving neurons, in contrast for heterosynaptic plasticity the change in the properties of a synapse can be elicited by neurons that are not necessary presynaptic or postsynaptic to the synapse in question. The new architecture is tested by a number of implementations in simulated and real environments. This includes experiments with a simulation environment implemented in Netlogo, and an implementation using Lego Mindstorms as the physical robot platform. These experiments demonstrate the problems with the traditional Spike timing dependent plasticity homosynaptic architecture and how the new heterosynaptic approach can overcome them. It is concluded that the new theoretical architecture provides a natural, theoretically sound, and practical new direction for research into the role of modulatory neural systems applied to spiking neural networks

Machine and social intelligent peer-assessment systems for assessing large student populations in massive open online education

The motivation of the European Etoile project is to create high quality free open education in complex systems science, including quality assured certification. Universities and colleges around the world are increasingly using online platforms to offer courses open to the public. Massive Open Online Courses or MOOCs give millions of people access to lectures delivered by prestigious universities. However, although some of these courses provide certification of attendance and completion, most do not provide any academic or professional recognition since this would imply a rigorous and complete evaluation of the student’s achievements. Since the number of students enrolled may exceed tens of thousands, it is impractical for a lecturer (or group of lecturers) to evaluate all students using conventional hand marking. Thus in order to be scalable, assessment must be automated. The state-of-the-art in automated assessment includes various methods and computerised tools including multiple choice questions, and intelligent marking techniques (involving complex semantic analysis). However, none of these completely cover the requirements needed for the implementation of an assessment system able to cope with very large populations of students and also able to guarantee the quality of evaluation required for higher education. The goal of this research is to propose, implement and evaluate a computer mediated social interaction system which can be applied to massive online learning communities. This must be a scalable system able to assess fairly and accurately student coursework and examinations. We call this approach “machine and socially intelligent peer assessment”. This paper describes our system and illustrates its application. Our approach combines the concepts of peer assessment and reputation systems to provide an independent computerised system which determines the degree and type of interaction between student peers based on a reputation score which emerges from the marking behaviour of each student and the interaction with other individuals of the community. A simulation experiment will be reported showing how reputation-based social structure can evolve in our peer marking system. A pilot experiment using a population of ninety 16-year old high school students in Colombia measured the marking accuracy of our system by comparing the statistical differences between the scores resulting from teacher marking (the ‘gold standard’), peer assessment using average scores, and our intelligent reputation-based peer assessment. This addresses the research question: to what extent does the proposed approach improve peer marking in terms of marking accuracy and fairness? We report the first results of this experiment, summarise the lessons learned, and describe further work

Hypernetwork-based peer marking for scalable certificated mass education

In the context of the need for massive free education for the Complex Systems Society and the UNESCO Complex Systems Digital Campus, scalable methods are essential for assessing tens of thousands of students’ work for certification. Automated marking is a partial solution but has many drawbacks. Peer marking, where students mark each others’ assignments, is a scalable solution since every extra student is an extra marker. However there are concerns about the quality of peer marking, since some students may not be competent to mark the work of others. Some students are better than others and often the best students are well qualified to assess the work of their peers. To make peer marking high quality we are using new hypernetwork-based methods to extend previous methods to discover which students are good markers and which students are less good as a course progresses

Peer assessment in architecture education

The role of peer assessment in education has become of particular interest in recent years, mainly because of its potential benefits in improving student’s learning and benefits in time management by allowing teachers and tutors to use their time more efficiently to get the results of student’s assessments quicker. Peer assessment has also relevant in the context of distance learning and massive open online courses (MOOCs)

Emergent communication enhances foraging behaviour in evolved swarms controlled by Spiking Neural Networks

Social insects such as ants communicate via pheromones which allows them to coordinate their activity and solve complex tasks as a swarm, e.g. foraging for food. This behavior was shaped through evolutionary processes. In computational models, self-coordination in swarms has been implemented using probabilistic or simple action rules to shape the decision of each agent and the collective behavior. However, manual tuned decision rules may limit the behavior of the swarm. In this work we investigate the emergence of self-coordination and communication in evolved swarms without defining any explicit rule. We evolve a swarm of agents representing an ant colony. We use an evolutionary algorithm to optimize a spiking neural network (SNN) which serves as an artificial brain to control the behavior of each agent. The goal of the evolved colony is to find optimal ways to forage for food and return it to the nest in the shortest amount of time. In the evolutionary phase, the ants are able to learn to collaborate by depositing pheromone near food piles and near the nest to guide other ants. The pheromone usage is not manually encoded into the network; instead, this behavior is established through the optimization procedure. We observe that pheromone-based communication enables the ants to perform better in comparison to colonies where communication via pheromone did not emerge. We assess the foraging performance by comparing the SNN based model to a rule based system. Our results show that the SNN based model can efficiently complete the foraging task in a short amount of time. Our approach illustrates self coordination via pheromone emerges as a result of the network optimization. This work serves as a proof of concept for the possibility of creating complex applications utilizing SNNs as underlying architectures for multi-agent interactions where communication and self-coordination is desired.Comment: 27 pages, 16 figure

Evaluación de la adquisición de competencias en sistema cardiovascular en Medicina: autopercepción, asistencia a clase y rendimiento académico.

The acquisition of competencies is essential in clinically relevant areas such as cardiovascular pathology. This study aims to assess the acquisition of the main competencies regarding the cardiovascular system by medical students, as well as their self-perception, and the relation with regular lecture attendance and academic performance. In order to achieve this aim, data were remotely obtained from 142 students in the fourth, fifth and sixth year of Medicine Degree at University of Málaga using an author-made questionnaire (0-15 points) with multiple-choice questions based on clinical situations and a self-evaluation survey about competencies. Analyzing the results, the competencies that were considered as acquired by the largest and the lowest number of students were, respectively, the handling of cardiovascular risk factors (100%) and cardiopulmonary auscultation (38.3%), respectively. Better results were obtained by students who regularly attended lectures (11.28±1.84 vs 9.54±2.45; p<0.01). In addition, it was demonstrated that self-perception (odds ratio [OR]=1.26; 95% confidence interval [95%CI]=1.07-1.50), lecture attendance (OR=3.55; 95%CI=1.64-7.70) and better academic performance (OR=2.6; 95%CI=1.46-4.63) were predictors of high scores in the questionnaire (≥11 points). In conclusion, lecture attendance seems to be fundamental in learning. In addition, self-perception could be used as a tool to guide teaching. On the other hand, it is detected an insufficient development of eminently practical competencies as well as the importance of interrelating knowledge, since a better general academic performance during degree is also reflected in the speciality studied in this research.  La adquisición de competencias resulta fundamental en ámbitos tan relevantes clínicamente como la patología cardiovascular. Este estudio pretende valorar la adquisición por parte de estudiantes de Medicina de las principales competencias respecto al sistema cardiovascular, así como su autopercepción y la relación con la asistencia presencial y el rendimiento académico. Para lograr este objetivo, se analizaron los datos obtenidos mediante la cumplimentación telemática por parte de 142 estudiantes de cuarto, quinto y sexto curso del Grado en Medicina de la Universidad de Málaga de un cuestionario de elaboración propia (0-15 puntos) con preguntas de elección múltiple basadas en situaciones clínicas y una autoevaluación sobre competencias. Analizando los resultados, las competencias consideradas como adquiridas por un mayor y menor número de estudiantes fueron, respectivamente, el manejo de los factores de riesgo cardiovascular (100%) y la auscultación cardiopulmonar (38,3%). Se obtuvieron mejores resultados en estudiantes que asistieron a clase (11,28±1,84 vs 9,54±2,45; p<0,01). Además, se demostró que la autopercepción de un correcto aprendizaje (odds ratio [OR]=1,26, intervalo de confianza al 95% [IC95%]=1,07-1,50), la asistencia a clase (OR=3,55; IC95%=1,64-7,7) y la nota media del expediente (OR=2,6; IC95%=1,46-4,63) son variables predictoras de calificaciones altas en el cuestionario (≥11 puntos). Esto nos permite inferir que la asistencia a clase se antoja fundamental en el aprendizaje. Además, la autopercepción podría utilizarse como herramienta para guiar la docencia. Por otro lado, se sugiere un insuficiente desarrollo de las competencias eminentemente prácticas, así como la importancia de interrelacionar conocimientos, pues un mejor rendimiento académico general durante el grado se refleja también en la especialidad estudiada en este trabaj

SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo

The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work

West Nile virus emergence in humans in Extremadura, Spain 2020

In Spain, the largest human West Nile virus (WNV) outbreak among humans was reported in 2020, constituting the second most important outbreak in Europe that season. Extremadura (southwestern Spain) was one of the affected areas, reporting six human cases. The first autochthonous human case in Spain was reported in Extremadura in 2004, and no other human cases were reported until 2020. In this work, we describe the first WNV human outbreak registered in Extremadura, focusing on the most important clinical aspects, diagnostic results, and control actions which followed. In 2020, from September to October, human WNV infections were diagnosed using a combination of molecular and serological methods (an in-house specific qRT-PCR and a commercial ELISA for anti-WNV IgM and IgG antibodies) and by analysing serum, urine, and/or cerebrospinal fluid samples. Serological positive serum samples were further tested using commercial kits against related flaviviruses Usutu and Tick-borne encephalitis in order to analyse serological reactivity and to confirm the results by neutralisation assays. In total, six cases of WNV infection (five with neuroinvasive disease and one with fever) were identified. Clinical presentation and laboratory findings are described. No viral RNA was detected in any of the analysed samples, but serological cross-reactivity was detected against the other tested flaviviruses. Molecular and serological methods for WNV detection in various samples as well as differential diagnosis are recommended. The largest number of human cases of WNV infection ever registered in Extremadura, Spain, occurred in 2020 in areas where circulation of WNV and other flaviviruses has been previously reported in humans and animals. Therefore, it is necessary to enhance surveillance not only for the early detection and implementation of response measures for WNV but also for other emerging flaviviruses that could be endemic in this area.This research was partially funded by the project PI19CIII/00014 from the Instituto de Salud Carlos III.S