Neuromorphic technologies are among the most actual interdisciplinary fields of science. Neuromorphic devices are developing to implement principles and algorithms of brain information processing in computational systems. Neuromorphic devices require development of electronic components: neurons and synapses. The excitability of the neuron-like generator based on phase-locked loop is studied under external pulse stimulation. Imitational modelling study of electronic neuron-like generator model with excitable and self-oscillating modes have conducted in Simulink environment. Spiking and bursting self-oscillating modes known from numerical simulations were observed. Novel excitable dynamics was studied in presence of external pulse stimulation.
This work aims to show that the radioengineering generators able to demonstrate neural like activity can switch to the epileptiform behavior due to short time external driving if coupled using sigmoid function. The effect takes place for various number of generators in ensemble. The particular coupling architecture and initial phase of external driving are of importance.
Absence epilepsy is a widespread among children and adolescents form of disease, manifestating itself on EEG with spike-wave discharges (SWDs). It was shown, just as a proof of concept, that SWDs can be considered as a long transient process rather than attractor in a phase space. In this work, we investigate this hypothesis in detail, studying a small class of simple mathematical models consisting of 14 identical FitzHugh–Nagumo neurons, organized in accordance with modern concepts of the thalamo-cortical network of the brain. Some models belonging to the class studied demonstrate rather long transient processes in response to a short in time external driving from an individual neuron modeling the trigeminal nerve.
Purpose. Optimal value of the embedding lag calculation is made. Lag is one of empirical parameters of mathematical models, used in autoregressive models for prediction, coupling analysis, signal classification etc. Methods. The first minimum in the dependence of the mutual information function on the time lag was detected. Results. The calculation showed that the optimal lag is about 8 sampling intervals (1/64 s or 1/8 of the characteristic oscillation period for the absence seizures). Discussion. The optimal lag is about 1/8 of the characteristic oscillation period was obtained for both epileptiform and background activity, including preictal and different stages of ictal activity, i. e. this time scale is present in the signal throughout the observation time.
KEYWORDS: Epilepsy, Time series analysis, Electroencephalography, Electrodes, Brain, Signal processing, Genetics, Animal model studies, Error analysis, Signal detection, Electronics
Absence seizures are known to be highly non-linear large amplitude oscillations with a well pronounced main time scale. Whilst the appearance of the main frequency is usually considered as a transition from noisy complex dynamics of baseline EEG to more regular absence activity, the dynamical properties of this type of epileptiformic activity in genetic absence models was not studied precisely.
Here, the estimation of the largest Lyapunov exponent from intracranial EEGs of 10 WAG/Rij rats (genetic model of absence epilepsy) was performed. Fragments of 10 seizures and 10 episodes of on-going EEG each of 4 s length were used for each animal, 3 cortical and 2 thalamic channels were analysed. The method adapted for short noisy data was implemented. The positive values of the largest Lyapunov exponent were found as for baseline as for spike wave discharges (SWDs), with values for SWDs being significantly less than for on-going activity.
Current findings may indicate that SWD is a chaotic process with a well pronounced main timescale rather than a periodic regime. Also, the absence activity was shown to be less chaotic than the baseline one.
The detection of coupling presence and direction between cortical areas from the EEG is a popular approach in neuroscience. Granger causality method is promising for this task, since it allows to operate with short time series and to detect nonlinear coupling or coupling between nonlinear systems.
In this study EEG multichannel data from adolescent children, suffering from unilateral cerebral palsy were investigated. Signals, obtained in rest and during motor activity of affected and less affected hand, were analysed. The changes in inter-hemispheric and intra-hemispheric interactions were studied over time with an interval of two months. The obtained results of coupling were tested for significance using surrogate times series. In the present proceeding paper we report the data of one patient. The modified nonlinear Granger causality is indeed able to reveal couplings within the human brain.
WAG/Rij rats are well known genetic model of absence epilepsy, which is traditionally considered as a nonconvulsive generalised epilepsy of unknown aetiology. In current study the effect of (R)-(+)-WIN 55,212-2 (cannabis agonist) injection on the coupling between different parts of cortex was studied on 27 male 8 month old rats using local field potentials. Recently developed non-linear adapted Granger causality approach was used as a primary method.
It was shown that first 2 hours after the injection the coupling between most channel pairs rises in comparison with the spontaneous activity, whilst long after the injection (2-6 hours) it drops down. The coupling increase corresponds to the mentioned before treatment effect, when the number and the longitude of seizures significantly decreases. However the subsequent decrease of the coupling in the cortex is accompanied by the dramatic increase of the longitude and the number of seizures. This assumes the hypothesis that a relatively higher coupling in the cortical network can prevent the seizure propagation and generalisation.
KEYWORDS: Data modeling, Visualization, Statistical modeling, Statistical analysis, Signal processing, Signal detection, Brain, Algorithm development, Electrodes, Data processing
This work proposes a new method for automatic marking epileptic spike-wave discharges in local field potential (LFP) signals. The method is based on empirical modelling using radial basis functions to approximate dependency of a further state on the current one. Number and type of radial basis functions used are adjusted to data based on statistical criteria. Due to this the method needs only a few manual efforts for its application to new data. The time resolution of the method is close to the sampling interval of the original data, and real time detection is possible. Detection accuracy of the proposed approach is validated analysing the LFP signals obtained using WAG/Rij rats.
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