Analysis of the neural hypercolumn in parallel PCSIM simulations

AbstractLarge and sudden changes in pitch or loudness occur statistically less frequently than gradual fluctuations, which means that natural sounds typically exhibit 1/f spectra. Experiments conducted on human subjects showed that listeners indeed prefer 1/f distributed melodies to melodies with faster or slower dynamics. It was recently demonstrated by using animal models, that neurons in primary auditory cortex of anesthetized ferrets exhibit a pronounced preference to stimuli that exhibit 1/f statistics. In the visual modality, it was shown that neurons in primary visual cortex of macaque monkeys exhibit tuning to sinusoidal gratings featuring 1/f dynamics.One might therefore suspect that neurons in mammalian cortex exhibit Self-Organizing Criticality. Indeed, we have found SOC-like phenomena in neurophysiological data collected in rat primary somatosensory cortex. In this paper we concentrated on investigation of the dynamics of cortical hypercolumn consisting of about 128 thousand simulated neurons. The set of 128 Liquid State Machines, each consisting 1024 neurons, was simulated on a simple cluster built of two double quad-core machines (16 cores).PCSIM was designed as a tool for simulating artificial biological-like neural networks composed of different models of neurons and different types of synapses. The simulator was written in C++ with a primary interface dedicated for the Python programming language. As its authors ensure it is intended to simulate networks containing up to millions of neurons and on the order of billions of synapses. This is achieved by distributing the network over different nodes of a computing cluster by using Message Passing Interface.The results obtained for Leaky Integrate-and-Fire model of neurons used for the construction of the hypercolumn and varying density of inter-column connections will be discussed. Benchmarking results for using the PCSIM on the cluster and predictions for grid computing will be presented to some extent. Research presented herein makes a good starting point for the simulations of very large parts of mammalian brain cortex and in some way leading to better understanding of the functionality of human brain

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

A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems

In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware-experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results

Iontophoresis of the eye - a computational approach

Iontophoresis is an effective, non-invasive method of intraocular drug delivery based on electric current. However, it has many limitations that can be addressed by effective computational models based on both machine learning (a data-driven approach) and other artificial intelligence methods and techniques. To date, computational models using AI/ML are lacking, including for the iontophoresis mechanism itself. Their wider use would help facilitate the delivery of drugs to the eye, which remains a major challenge due to the multiple barriers in the eye. The aim of this paper is to explore the feasibility of developing a computational model for ocular iontophoresis using available AI methods and techniques.Jonoforeza jest skuteczną, nieinwazyjną metodą wewnątrzgałkowego podawania leków opartą na prądzie elektrycznym. Ma jednak wiele ograniczeń, które można rozwiązać za pomocą skutecznych modeli obliczeniowych opartych zarówno na uczeniu maszynowym (podejście oparte na danych), jak i innych metodach i technikach sztucznej inteligencji. Do tej pory brakuje modeli obliczeniowych wykorzystujących AI/ML, w tym dla samego mechanizmu jonoforezy. Ich szersze zastosowanie pomogłoby ułatwić dostarczanie leków do oczu, co pozostaje poważnym wyzwaniem ze względu na liczne bariery w oku. Celem artykułu jest zbadanie wykonalności opracowania modelu obliczeniowego dla jonoforezy ocznej przy użyciu dostępnych metod i technik sztucznej inteligencji

Principles of electrostimulation of the face and neck muscles - a medical and biocybernetic approach

The facial nerve has a tortuous and complex course from the parotid-cerebellar junction to various target sites, withindividually varied and complex branching patterns and connections to several other cranial nerves. This makes research-based computational models a key component of modern diagnostics and therapy, as well as patient monitoring and the design of devices to support the above-mentioned processes. To date, no good computational model has been proposed in this area and the concepts presented are in the preliminary research phase. The aim of this study is to develop guidelines for a computational model of electrostimulation of facial and neck muscles in order to improve diagnosis and therapy, but also for the future development of a virtual twin for eHealth.Nerw twarzowy ma kręty i złożony przebieg od połączenia ślinianki przyusznej i móżdżku do różnych miejsc docelowych, z indywidualnie zróżnicowanymi i złożonymi wzorcami rozgałęzień i połączeniami z kilkoma innymi nerwami czaszkowymi. Sprawia to, że modele obliczeniowe oparte na badaniach są kluczowym elementem nowoczesnej diagnostyki i terapii, a także monitorowania pacjentów i projektowania urządzeń wspierających wyżej wymienione procesy. Do tej pory nie zaproponowano dobrego modelu obliczeniowego w tym obszarze, a przedstawione koncepcje znajdują się we wstępnej fazie badań. Celem niniejszego badania jest opracowanie wytycznych dla modelu obliczeniowego elektrostymulacji mięśni twarzy i szyi w celu poprawy diagnostyki i terapii, ale także dla przyszłego rozwoju wirtualnego bliźniaka dla eZdrowi

Mapping the Human Brain in Frequency Band Analysis of Brain Cortex Electroencephalographic Activity for Selected Psychiatric Disorders

There are still no good quantitative methods to be applied in psychiatric diagnosis. The interview is still the main and most important tool in the psychiatrist work. This paper presents the results of electroencephalographic research with the subjects of a group of 30 patients with psychiatric disorders compared to the control group of healthy volunteers. All subjects were solving working memory task. The digit-span working memory task test was chosen as one of the most popular tasks given to subjects with cognitive dysfunctions, especially for the patients with panic disorders, depression (including the depressive phase of bipolar disorder), phobias, and schizophrenia. Having such cohort of patients some results for the subjects with insomnia and Asperger syndrome are also presented. The cortical activity of their brains was registered by the dense array EEG amplifier. Source localization using the photogrammetry station and the sLORETA algorithm was then performed in five EEG frequency bands. The most active Brodmann Areas are indicated. Methodology for mapping the brain and research protocol are presented. The first results indicate that the presented technique can be useful in finding psychiatric disorder neurophysiological biomarkers. The first attempts were made to associate hyperactivity of selected Brodmann Areas with particular disorders