Spyridon RevithisUNSW Sydney | UNSW · School of Computer Science and Engineering
Spyridon Revithis
BSc, MS, PhD
About
21
Publications
2,627
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
49
Citations
Citations since 2017
Introduction
Formal education:
BSc (TEI-A/Univ.W.Attica - Athens, Greece), MS (Univ. Missouri, Columbia - MO, USA), PhD (UNSW - Sydney, Australia).
Research areas:
SOM neural networks; cognitive modelling; neurocomputational psychiatry / (developmental) psychology; cognitive load theory.
Membership: Cognitive Science Society; Organisation for Computational Neurosciences; Australian Association of Computational Neuroscientists and Neuromorphic Engineers; Artificial General Intelligence Society; IHIQS.
Publications
Publications (21)
Three guiding questions of a research blueprint for the study of brain disorders, using self-organising map (SOM) neural network modeling, are proposed: a) What does the current research on computational psychology/psychiatry and cognitive neuroscience suggest about atypical brain development and the associated behaviour? b) What cortical structure...
Oscillating-TN (Topological Neighborhood) Self-Organising-Map (SOM) artificial neural networks can facilitate the study of neurodevelopmental cognitive phenomena. Their cognitive modelling significance rests primarily on the premise of biological realism. Despite the difference in neuronal activity description between spike-train brain signaling an...
The role of Topological Neighborhood (TN) in SOM cognitive modeling has biological and computational implications. The modeling significance of the TN width function σ(epoch) is associated with the initial TN width parameter σ0. Furthermore, σ0 is decisive in determining the geometric area γ under the TN-width function curve through the epochs of S...
DSM and ICD classifications fail by design to properly address the biological dimension of mental disorders. A new approach has been emerging that aims to examine abnormal brain functioning from a different standpoint, inclusive of biological mechanisms, crossing the boundaries between currently classified disorders and eventually redefining them u...
Artificial General Intelligence (AGI) is a term that describes a variant of a Strong AI revival in the mind sciences. Irrespective of its definition limits, and leaving aside the non-scientific metaphysical or philosophical aspirations, AGI studies the feasibility and implementation aspects of artificial systems that would have the capacity for dom...
This thesis reports the author’s research on the role of neural self-organisation in cognition and cognitive development, the implications of the Self-Organizing Map (SOM) simulation of brain activity at the behavioural level, the prospects of SOM modelling as an explanatory framework to brain disorders, and the cognitive modelling role of SOM prop...
The emerging research interest on neural oscillation in neuroscience has resulted in an ever-increasing number of studies on various cognitive and neuro-developmental phenomena. There is, now, evidence linking brain physiological descriptions with certain phenotypes in normal and atypical behavior, involving neural oscillation. Case studies include...
The artificial neural network class of self-organizing maps (SOMs) is a powerful and promising cognitive modeling tool in the study of the brain and its disorders. Under this premise, this paper proposes a novel modification of the standard SOM algorithm in the form of an oscillating Topological Neighborhood (TN) width function. Existing research i...
The neural network class of self-organizing maps (SOMs) is a promising cognitive modeling tool in the study of the autistic spectrum pervasive developmental disorder. This work offers a novel validation of Gustafsson's neural circuit theory, according to which autism relates to formation characteristics of cortical brain maps. A previously construc...
Self-Organizing Map (SOM) neural network modeling is an alternative research paradigm, which could contribute to the resolution of important standing issues in psychiatry and clinical psychology. In recent computational work, the author has been exploring the significance of the topological neighborhood (TN) component of SOM models as an exegetic n...
The level at which a computational cognitive model provides explanations of phenomena is often unclear, especially when there is no sufficient distinction between behavior and cognition. It has been shown that human behavior is amenable to SOM modeling aiming at compressed classification and prediction. Reducible to an engineering level this modeli...
This work expands the scope of an earlier neurocomputational study of learning-performance aspects of human behaviour in automated learning environments by applying metrics of instructional efficiency within the conceptual framework of cognitive load theory. The test bed is a simulated learning environment, in which the organization of the learning...
SOM neural network modeling is an established approach towards the resolution of standing matters in psychiatry and clinical neurology. A resulting claim, supported by the author in recent computational work, is that in this approach there is an exegetic neurocomputational norm realized in the topological neighborhood (TN) component of the SOM mode...
It has been proposed in previous case studies, by the author and by other researchers, the claim that neurocomputational self-organizing behavioral modeling, based on the class of SOM neural networks, can be realized in two approaches: behavioral anticipation and exegetic prediction.
In the former approach, the model is utilized as a behavioral spa...
In this work we introduce a neurocomputational model of human behaviour applied in the domain of traffic forecast. A multiagent system, consisting of artificial agents, uses a Kohonen self-organizing traffic behavioural model in order to coordinate a human multiagent system situated in a metropolitan road network. The result of our analysis establi...
Cognitive modeling methodologies form the groundwork of significant studies in cognitive science. In this work a prototype computational model, called IPSOM, is introduced, which charts the spatial cognitive human behav- iour in completing interlocking puzzles. IPSOM is a neural network of the class of self-organizing maps, and has been implemented...
Appeared on-line in The Hamilton Institute for Policy Research, IHIQS
--- Extract from intro:
"Cognition, behavior, and technology:
The repertoire of human behavior is evidently vast and complex. Since the dawn of the classical Greek civilization a remarkable amount of effort has been put towards the systematic study of human behavior perplexitie...
In this work we focus on the potential of intelligent agents in e-learning environments. A central problem is the modeling of human learners so that the agent can facilitate personalized learning. The solution presented here is based on a connectionist approach. An intelligent agent, which replaces the human instructor, controls an e-learning envir...
This study focuses on the applicability potential of Artificial Intelligence on learning environments. The main problem that arises in such a domain is the modeling of the human learner, which reflects on the feasibility of automating the instructor’s role. The solution that is proposed in this work is based on a connectionist approach, namely, the...
Questions
Questions (2)
Taking into consideration that Australia has an active but, nevertheless, relatively small academic community, are there any signs that 'clinical' cognitive science research in Australia has really broken the barriers between the three traditionally involved disciplines, computer science, psychology and brain-related medicine, especially with the latter of the three (i.e., beyond one-off instances, or minority collaborations)?
Meta-reseach: in quest of reproducible and useful evidence by Prof. John Ioannidis (6 May 2021)
