Liquid computing and analysis of sound signals

Liquid Computing Theory is a proposal of modelling the behaviour of neural microcircuits.It focuses on creating a group of neurons, known as a liquid layer, responsible for preprocessingof the signal that is being analysed. Specific information is achieved by the readout layers, task orientedgroups of neurons, taught to extract particular information from the state of liquid layer. TheLSMs have been used to analyse sound signals. The liquid layer was implemented in the PCSIM Simulator,and the readout layer has been prepared in the JNNS simulator. It could successfully recognisecertain sounds despite noises. Those results encourage further research of the computational potentialof Liquid State Machines including working in parallel with many readout layers

Developing brain electric activity acquisition software for Linux

This article discusses the first successful phase of our work on the construction of a complete modular research station for EEG signal acquisition and analysis performed in real-time mode and in a way that meets our needs. Our intentions were presented in [1]

Electroencephalographic detection of synesthesia

In this paper the research on a person declaring synesthetic abilities will be presented.According to the current state of knowledge synesthesia activates additional cortical fields in the brainwhich can be found in the EEG. The research was conducted using an EGI-EEG system (ElectricalGeodesic Inc., Eugene, Oregon, USA) with the GeoSource software. GeoSource is a tool that implementsthe algorithms LAURA, LORETA and sLORETA. Using these algorithms for EEG analysis wecan determine where in the brain the source of activity is. The authors will try to answer the questionwhether the use of these tools can prove the occurrence of synesthesia

Modelling 3D scene based on rapid face tracking and objects recognition

Mixed reality techniques are presented. Implementation and application of CAMSHIFT algorithm is discussed to some extent. In the initial stage of the research the technology of real object's edge detection and geometrical figures' representation in the virtual scene has been worked out

Hebbian encoding in the biological visual system

We examined neural networks built of several hundred Hodgkin-Huxley neurons. The main aim of the research described below was to simulate memory processes occurring in hippocampus and biological visual system. In our model we chose the ancient Chinese I-Ching Oracle as a set of input patterns. Maps of Hebbian weights appearing on the output device of the model can be analysed by artificial neural networks playing a role of some kind of visual consciousness

Computational ability of LSM ensemble in the model of mammalian visual system

Ensembles of artificial Hodgkin-Huxley neural microcircuits are examined. The networks discussed in this article simulate the cortex of the primate visual system. We use a modular architecture of the cortex divided into columns. The results of parallel simulations based on the liquid computing theory are presented in some detail. Separation ability of groups of neural microcircuits is observed. We show that such property may be useful for explaining some pattern recognition phenomena

Investigating Mammalian Visual System with methods of informational theory

We examine a simple model of mammalian visual system. This structure is simulated by means of several hundred Hodgkin-Huxley neurons. We investigate signal processing properties of the model. Some methods taken from informational theory are applied to the analysis of Primary Visual Cortex' dynamics. Discussion of efficiency of such methods in two dimensional movement detection is presented in some detail

Synchronous SSVEP Data Acquisition System

Steady State Visually Evoked Potentials have been known for several decades and theyappear in the primary visual cortex of brain as a result of light stimulation of the sense of sight. Inthis article a simple method for electroencephalographic data acquisition is presented. The system isbased on the DSM-51 unit connected to goggles with blinking diodes and Mindset-1000 EEG amplifierwith 16 channels. We present self-developed hardware and method of effective synchronization for thelight stimulation and brain activity recording

Simple cyclic movements as a distinct autism feature - computational approach

Diversity of symptoms in autism dictates a broad definition of Autism Spectrum of Disorders(ASD). Each year percentage of children diagnosed with ASD is growing. One common diag-nostic feature in individuals with ASD is the tendency to atypical simple cyclic movements.The motor brain activity seems to generate periodic attractor state that is hard to escape.Despite numerous studies scientists and clinicians do not know exactly if ASD is a result ofa simple but general mechanism, or a complex set of mechanisms, both on neural, molecularand system levels. Simulations using biologically relevant neural network model presentedhere may help to reveal simplest mechanisms that may be responsible for specific behavior.Abnormal neural fatigue mechanisms may be responsible for motor as well as many if notall other symptoms observed in ASD