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

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

Predicting and identifying signs of criticality near neuronal phase transition

This thesis examines the critical transitions between distinct neural states associated with the transition to neuron spiking and with the induction of anaesthesia. First, mathematical and electronic models of a single spiking neuron are investigated, focusing on stochastic subthreshold dynamics on close approach to spiking and to depolarisation-blocked quiescence (spiking death) transition points. Theoretical analysis of subthreshold neural behaviour then shifts to the anaesthetic-induced phase transition into unconsciousness using a mean-field model for interacting populations of excitatory and inhibitory neurons. The anaesthetic-induced changes are validated experimentally using published electrophysiological data recorded in anaesthetised rats. The criticality hypothesis associated with brain state change is examined using neuronal avalanches for experimentally recorded rat local field potential (LFP) data and mean-field pseudoLFP simulation data. We compare three different implementations of the FitzHugh--Nagumo single spiking neuron model: a mathematical model by H. R. Wilson, an alternative due to Keener and Sneyd, and an op-amp based nonlinear oscillator circuit. Although all three models can produce nonlinear ``spiking" oscillations, our focus is on the altering characteristics of noise-induced fluctuations near spiking onset and death via Hopf bifurcation. We introduce small-amplitude white noise to enable a linearised stochastic analyses using Ornstein--Uhlenbeck theory to predict variance, power spectrum and correlation of voltage fluctuations during close approach to the critical point, identified as the point at which the real part of the dominant eigenvalue becomes zero. We validate the theoretical predictions with numerical simulations and show that the fluctuations exhibit critical slowing down divergences when approaching the critical point: power-law increases in the variance of the fluctuations simultaneous with prolongation of the system response. We expand the study of stochastic behaviour to two spatial dimensions using the Waikato mean-field model operating near phase transition points controlled by the infusion or elimination of anaesthetic inhibition. Specifically, we investigate close approach to the critical point (CP), and to the points of loss of consciousness (LOC) and recovery of consciousness (ROC). We select the equilibrium states using $\lambda$ anaesthetic inhibition and $\Delta V^{\text{rest}}_e$ cortical excitation as control parameters, then analyse the voltage fluctuations evoked by small-amplitude spatiotemporal white noise. We predict the variance and power spectrum of voltage fluctuations near the marginally stable LOC and ROC transition points, then validate via numerical simulation. The results demonstrate a marked increase in voltage fluctuations and spectral power near transition points. This increased susceptibility to low-intensity white noise stimulation provides an early warning of impending phase transition. Effects of anaesthetic agents on cortical activity are reflected in local field potentials (LFPs) by the variation of amplitude and frequency in voltage fluctuations. To explore these changes, we investigate LFPs acquired from published electrophysiological experiments of anaesthetised rats to extract amplitude distribution, variance and time-correlation statistics. The analysis is broadened by applying detrended fluctuation analysis (DFA) to detect long-range dependencies in the time-series, and we compare DFA results with power spectral density (PSD). We find that the DFA exponent increases with anaesthetic concentration, but is always close to 1. The penultimate chapter investigates the evidence of criticality in anaesthetic induced phase-transitions using avalanche analysis. Rat LFP data reveal an avalanche power-law exponent close to $\alpha = 1.5$, but this value depends on both the time-bin width chosen to separate the events and the \textit{z}-score threshold used to detect these events. Power-law behaviour is only evident at lower anaesthetic concentrations; at higher concentrations the avalanche size distribution fails to align with a power-law nature. Criticality behaviour is also indicated in the Waikato mean-field model for anaesthetic-induced phase-transition using avalanches detected from the pseudoLFP time-series, but only at the critical point (CP) and at the secondary phase-transition points of LOC and ROC. In summary, this thesis unveils evidence of characteristic changes near phase transition points using computer-based mathematical modelling and electrophysiological data analysis. We find that noise-driven fluctuations become larger and persist for longer as the critical point is closely approached, with similar properties being seen not only in single-neuron and neural population models, but also in biological LFP signals. These results consistent with an increase of susceptibility to noise perturbations near phase transition point. Identification of neuronal avalanches in rat LFP data for low anaesthetic concentrations provides further support for the criticality hypothesis

Spike avalanches in vivo suggest a driven, slightly subcritical brain state

In self-organized critical (SOC) systems avalanche size distributions follow power-laws. Power-laws have also been observed for neural activity, and so it has been proposed that SOC underlies brain organization as well. Surprisingly, for spiking activity in vivo, evidence for SOC is still lacking. Therefore, we analyzed highly parallel spike recordings from awake rats and monkeys, anesthetized cats, and also local field potentials from humans. We compared these to spiking activity from two established critical models: the Bak-Tang-Wiesenfeld model, and a stochastic branching model. We found fundamental differences between the neural and the model activity. These differences could be overcome for both models through a combination of three modifications: (1) subsampling, (2) increasing the input to the model (this way eliminating the separation of time scales, which is fundamental to SOC and its avalanche definition), and (3) making the model slightly sub-critical. The match between the neural activity and the modified models held not only for the classical avalanche size distributions and estimated branching parameters, but also for two novel measures (mean avalanche size, and frequency of single spikes), and for the dependence of all these measures on the temporal bin size. Our results suggest that neural activity in vivo shows a mélange of avalanches, and not temporally separated ones, and that their global activity propagation can be approximated by the principle that one spike on average triggers a little less than one spike in the next step. This implies that neural activity does not reflect a SOC state but a slightly sub-critical regime without a separation of time scales. Potential advantages of this regime may be faster information processing, and a safety margin from super-criticality, which has been linked to epilepsy.DFG, 103586207, GRK 1589: Verarbeitung sensorischer Informationen in neuronalen SystemenBMBF, 01GQ1005B, Bernstein Zentrum für Computational Neuroscience, Göttingen - Kooperative Dynamiken und Adaptivität in neuronalen SystemenBMBF, 01GQ0742, Verbundprojekt Bernstein Partner: Gedächtnis-Netzwerk, Teilprojekt

Self-organising Thermoregulatory Huddling in a Model of Soft Deformable Littermates

Thermoregulatory huddling behaviours dominate the early experiences of developing rodents, and constrain the patterns of sensory and motor input that drive neural plasticity. Huddling is a complex emergent group behaviour, thought to provide an early template for the development of adult social systems, and to constrain natural selection on metabolic physiology. However, huddling behaviours are governed by simple rules of interaction between individuals, which can be described in terms of the thermodynamics of heat exchange, and can be easily controlled by manipulation of the environment temperature. Thermoregulatory huddling thus provides an opportunity to investigate the effects of early experience on brain development in a social, developmental, and evolutionary context, through controlled experimentation. This paper demonstrates that thermoregulatory huddling behaviours can self-organise in a simulation of rodent littermates modelled as soft-deformable bodies that exchange heat during contact. The paper presents a novel methodology, based on techniques in computer animation, for simulating the early sensory and motor experiences of the developing rodent

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

Temporal ordering of input modulates connectivity formation in a developmental neuronal network model of the cortex

Preterm infant brain activity is discontinuous; bursts of activity recorded using EEG (electroencephalography), thought to be driven by subcortical regions, display scale free properties and exhibit a complex temporal ordering known as long-range temporal correlations (LRTCs). During brain development, activity-dependent mechanisms are essential for synaptic connectivity formation, and abolishing burst activity in animal models leads to weak disorganised synaptic connectivity. Moreover, synaptic pruning shares similar mechanisms to spike-timing dependent plasticity (STDP), suggesting that the timing of activity may play a critical role in connectivity formation. We investigated, in a computational model of leaky integrate-and-fire neurones, whether the temporal ordering of burst activity within an external driving input could modulate connectivity formation in the network. Connectivity evolved across the course of simulations using an approach analogous to STDP, from networks with initial random connectivity. Small-world connectivity and hub neurones emerged in the network structure—characteristic properties of mature brain networks. Notably, driving the network with an external input which exhibited LRTCs in the temporal ordering of burst activity facilitated the emergence of these network properties, increasing the speed with which they emerged compared with when the network was driven by the same input with the bursts randomly ordered in time. Moreover, the emergence of small-world properties was dependent on the strength of the LRTCs. These results suggest that the temporal ordering of burst activity could play an important role in synaptic connectivity formation and the emergence of small-world topology in the developing brain

Sensory and cognitive factors in multi-digit touch, and its integration with vision

Every tactile sensation – an itch, a kiss, a hug, a pen gripped between fingers, a soft fabric brushing against the skin – is experienced in relation to the body. Normally, they occur somewhere on the body’s surface – they have spatiality. This sense of spatiality is what allows us to perceive a partner’s caress in terms of its changing location on the skin, its movement direction, speed, and extent. How this spatiality arises and how it is experienced is a thriving research topic, compelled by growing interest in the nature of tactile experiences from product design to brain-machine interfaces. The present thesis adds to this flourishing area of research by examining the unified spatial quality of touch. How does distinct spatial information converge from separate areas of the body surface to give rise to our normal unified experience of touch? After explaining the importance of this question in Chapter 1, a novel paradigm to tackle this problem will be presented, whereby participants are asked to estimate the average direction of two stimuli that are simultaneously moved across two different fingerpads. This paradigm is a laboratory analogue of the more ecological task of representing the overall movement of an object held between multiple fingers. An EEG study in Chapter 2 will reveal a brain mechanism that could facilitate such aggregated perception. Next, by characterising participants’ performance not just in terms of error rates, but by considering perceptual sensitivity, bias, precision, and signal weighting, a series of psychophysical experiments will show that this aggregation ability differs for within- and between-hand perception (Chapter 3), is independent from somatotopically-defined circuitry (Chapter 4) and arises after proprioceptive input about hand posture is accounted for (Chapter 5). Finally, inspired by the demand for integrated tactile and visual experience in virtual reality and the potential of tactile interface to aid navigation, Chapter 6 will examine the contribution of tactile spatiality on visual spatial experience. Ultimately, the present thesis will reveal sensory factors that limit precise representation of concurrently occurring dynamic tactile events. It will point to cognitive strategies the brain may employ to overcome those limitations to tactually perceive coherent objects. As such, this thesis advances somatosensory research beyond merely examining the selectivity to and discrimination between experienced tactile inputs, to considering the unified experience of touch despite distinct stimulus elements. The findings also have practical implications for the design of functional tactile interfaces

Coupling and stochasticity in mesoscopic brain dynamics

The brain is known to operate under the constant influence of noise arising from a variety of sources. It also organises its activity into rhythms spanning multiple frequency bands. These rhythms originate from neuronal oscillations which can be detected via measurements such as electroen-cephalography (EEG) and functional magnetic resonance (fMRI). Experimental evidence suggests that interactions between rhythms from distinct frequency bands play a key role in brain processing, but the dynamical mechanisms underlying this cross-frequency interactions are still under investigation. Some rhythms are pathological and harmful to brain function. Such is the case of epileptiform rhythms characterising epileptic seizures. Much has been learnt about the dynamics of the brain from computational modelling. Particularly relevant is mesoscopic scale modelling, which is concerned with spatial scales exceeding those of individual neurons and corresponding to processes and structures underlying the generation of signals registered in the EEG and fMRI recordings. Such modelling usually involves assumptions regarding the characteristics of the background noise, which represents afferents from remote, non-modelled brain areas. To this end, Gaussian white noise, characterised by a flat power spectrum, is often used. In contrast, macroscopic fluctuations in the brain typically follow a `1/f b ¿ spectrum, which is a characteristic feature of temporally correlated, coloured noise. In Chapters 3-5 of this Thesis we address by means of a stochastically driven mesoscopic neuronal model, the three following questions. First, in Chapter 3 we ask about the significance of deviations from the assumption of white noise in the context of brain dynamics, and in particular we study the role that temporally correlated noise plays in eliciting aberrant rhythms in the model of an epileptic brain. We find that the generation of epileptiform dynamics in the model depends non-monotonically on the noise correlation time. We show that this is due to the maximisation of the spectral content of epileptogenic rhythms in the noise. These rhythms fall into frequency bands that indeed were experimentally shown to increase in power prior to epileptic seizures. We explain these effects in terms of the interplay between specific driving frequencies and bifurcation structure of the model. Second, in Chapter 4 we show how coupling between cortical modules leads to complex activity patterns and to the emergence of a phenomenon that we term collective excitability. Temporal patterns generated by this model bear resemblance to clinically observed characteristics of epileptic seizures. In that chapter we also introduce a fast method of tracking a loss of stability caused by excessive inter-modular coupling in a neuronal network. Third, in Chapter 5 we focus on cross-frequency interactions occurring in a network of cortical modules, in the presence of coloured noise. We suggest a mechanism that underlies the increase of power in a fast rhythm due to driving with a slow rhythm, and we find the noise parameters that best recapitulate experimental power spectra. Finally, in Chapter 6, we examine models of haemodynamic and metabolic brain processes, we test them on experimental data, and we consider the consequences of coupling them with mesoscopic neuronal models. Taken together, our results show the combined influence of noise and coupling in computational models of neuronal activity. Moreover, they demonstrate the relevance of dynamical properties of neuronal systems to specific physiological phenomena, in particular related to cross-frequency interactions and epilepsy. Insights from this Thesis could in the future empower studies of epilepsy as a dynamic disease, and could contribute to the development of treatment methods applicable to drug-resistant epileptic patients.El cervell opera sota la influència de sorolls amb diversos orígens. També organitza la seva activitat en una sèrie de ritmes que s'expandeixen en diverses bandes de freqüència. Aquests ritmes tenen el seu origen en les osci∙lacions neuronals i poden detectar-se via mesures com les electroencefalogràfiques (EEG) o la ressonància magnètica funcional (fMRI). Les evidències experimentals suggereixen que les interaccions entre ritmes operant en bandes de freqüència diferents juguen un paper central en el processat cerebral però els mecanismes dinàmics subjacents a les interaccions inter-freqüència encara estan investigant-se. Alguns ritmes són patològics i fan malbé el funcionament cerebral. És el cas dels ritmes epileptiformes que caracteritzen les convulsions epilèptiques. Fent servir el modelatge computacional s'ha après molt sobre la dinàmica del cervell. Especialment rellevant és el modelatge a l’escala mesoscòpica, que té a veure amb les escales espacials superiors a les de les neurones individuals i que correspon als processos que generen EEG i fMRI. Tal modelatge, en general, implica supòsits relatius a les característiques del soroll de fons que representa zones remotes del cervell no modelades. Amb aquesta finalitat s'utilitza sovint el soroll blanc gaussià, que es caracteritza per un espectre de potència pla. Les fluctuacions macroscòpiques en el cervell, però, normalment segueixen un espectre '1/fb', que és un tret característic de les correlacions temporals i el soroll de color. Als Capítols 3-5 d'aquesta tesi abordem mitjançant un model neuronal mesoscòpic forçat estocàsticament, les tres preguntes següents. En primer lloc, en el Capítol 3 ens preguntem sobre la importància de les desviacions de l'assumpció de soroll blanc en el context de la dinàmica del cervell i, en particular, estudiem el paper que juga el soroll amb correlació temporal en l'obtenció de ritmes aberrants d'un cervell epilèptic. Trobem que la generació de les dinàmiques epileptiformes depèn de forma monòtona del temps de correlació del soroll. Aquests ritmes es divideixen en bandes de freqüència que, segons, s'ha mostrat experimentalment, augmenten la seva potència espectral abans de les crisis epilèptiques. Expliquem aquests efectes en termes de la interacció entre les freqüències específiques del forçament i l'estructura de bifurcació del model. En segon lloc, en el Capítol 4 es mostra com l'acoblament entre mòduls corticals condueix a patrons d'activitat complexes i a l'aparició d'un fenomen que anomenem excitabilitat col∙lectiva. Els patrons temporals generats per aquest model s'assemblen a les observacions clíniques de les convulsions epilèptiques. En aquest capítol també introduïm un mètode d'anàlisi de la pèrdua d'estabilitat causada per l'acoblament inter-modular excessiu en les xarxes neuronals. En tercer lloc, en el Capítol 5 ens centrem en les interaccions inter-freqüència que es produeixen en una xarxa de mòduls corticals en presència de soroll de color. Suggerim un mecanisme subjacent a l'augment de la potència spectral de ritmes ràpids a causa del forçament amb un ritme lent, i veiem quins paràmetres del soroll descriuen millor els espectres de potència experimental. Finalment, en el Capítol 6, estudiem models dels processos hemodinàmics i metabòlics del cervell, els comparem amb dades experimentals i considerem les conseqüències del seu acoblament amb models neuronals mesoscopics. En conjunt, els nostres resultats mostren la influència combinada del soroll i l'acoblament en models computacionals de l'activitat neuronal. D'altra banda, també demostren la importància de les propietats dinàmiques dels sistemes neuronals en fenòmens fisiològics específics com les interaccions inter-frequència i l'epilèpsia. Els resultats d'aquesta Tesi contribueixen a potenciar l’estudi de l'epilèpsia com una malaltia dinàmica, i el desenvolupament de mètodes de tractament aplicables a pacients epilèptics resistents als fàrmacs.Postprint (published version