Alexander Toschev

Simulation of a Fear-like State on a Model of Dopamine System of Rat Brain

In this paper we present the following hypothesis: the neuromodulatory mechanisms that control the emotional states of mammals could be translated and re-implemented in a computer by means of controlling the computational performance of a hosted computational system. In our specific implementation we represent the simulation of the fear-like state based on the three dimensional neuromodulatory model of affects (here the basic emotional inborn states) that we have inherited from works of Hugo Lovheim. We have managed to simulate 1000 ms of work of the dopamine system using NEST Neural Simulation Tool and the rat brain as the model. We also present the results of that simulation and evaluate them to validate the overall correctness of our hypothesis.

Thinking Lifecycle as an Implementation of Machine Understanding in Software Maintenance Automation Domain

The main goal of our work is to test the feasibility study of automation of incident processing in Infrastructure as Service domain to optimize the operational costs of management services that are delivered remotely. This paper also describes a framework that authors have developed to deliver an integrated incident, problem solution and resolution approach as an event-driven Automated Incident Solving System, for Remote Infrastructure Management (RIM) Model. Current approaches are mainly automated scripts, but this is a specific approach for one specific problem. Those systems can’t think. Our approach is a system that exploits a thinking model thus can think and can learn. In other words system is capable of recombining its knowledge to solve new problems. Based on Minsky [11] thinking model we have created a machine understanding prototype which is capable of learning and understanding primitive incident description texts.

“Thinking-Understanding” Approach in Spiking Reasoning System

In this position paper we propose the approach to use “Thinking-Understanding” architecture for the management of the real-time operated robotic system. Based on the “Robot dream” architecture, the robotic system digital input is been translated in form of “pseudo-spikes” and provided to a simulated spiking neural network, then elaborated and fed back to a robotic system as updated behavioural strategy rules. We present the reasoning rule-based system for intelligent spike processing translating spikes into software actions or hardware signals is thus specified. The reasoning is based on pattern matching mechanisms that activates critics that in their turn activates other critics or ways to think inherited from the work of Marvin Minsky “The emotion machine” [7].

TU Framework in Automatic Formatting a Digital Library

Intelligent search in the digital libraries is very important. It is very important for efficient research to obtain relevant information quickly. In the paper, we propose methods for automatic processing of online resources for the institution library using scanned copies or/and pdf files to make MathML model and provide extended search capacity. The key idea is to use Thinking–Understanding framework to provide automatic document-type detection and processing using the thinking flow to combine different open-source engines like OCR and approaches like Word2vec.

Evolution of Thinking Models in Automatic Incident Processing Systems

In this paper we describe the evolution of the application of thinking models in automatically processing a user’s incidents in natural language, starting with the model based on decision trees and ends up finishing with the human thinking model. Every model has been developed, prototyped and tested. The article contains experiments results and conclusions for every model. After evolving several theories, we found the most suitable for solving the problem of automatically processing a users incidents.