1,224 research outputs found
Corticothalamic projections control synchronization in locally coupled bistable thalamic oscillators
Thalamic circuits are able to generate state-dependent oscillations of
different frequencies and degrees of synchronization. However, only little is
known how synchronous oscillations, like spindle oscillations in the thalamus,
are organized in the intact brain. Experimental findings suggest that the
simultaneous occurrence of spindle oscillations over widespread territories of
the thalamus is due to the corticothalamic projections, as the synchrony is
lost in the decorticated thalamus. Here we study the influence of
corticothalamic projections on the synchrony in a thalamic network, and uncover
the underlying control mechanism, leading to a control method which is
applicable in wide range of stochastic driven excitable units.Comment: 4 pages with 4 figures (Color online on p.3-4) include
Periodicity in wide-band time series
Summary: To test the hypotheses that (i) electroencephalograms (EEGs) are largely made up of oscillations at many frequencies and (ii) that the peaks in the power spectra represent oscillations, we applied a new method, called the Period Specific Average (PSA) to a wide sample of EEGs. Both hypotheses can be rejected
Random dynamical systems modeling for brain wave synchrony
A random dynamical systems model is studied\ud
to understand coupled dynamics of auditory area and\ud
motor area modulated by external force. We measure transfer\ud
entropy of coupled oscillators with the presence of noise\ud
to explain results of human brain wave experiments
Modeling Brain Resonance Phenomena Using a Neural Mass Model
Stimulation with rhythmic light flicker (photic driving) plays an important role in the diagnosis of schizophrenia, mood disorder, migraine, and epilepsy. In particular, the adjustment of spontaneous brain rhythms to the stimulus frequency (entrainment) is used to assess the functional flexibility of the brain. We aim to gain deeper understanding of the mechanisms underlying this technique and to predict the effects of stimulus frequency and intensity. For this purpose, a modified Jansen and Rit neural mass model (NMM) of a cortical circuit is used. This mean field model has been designed to strike a balance between mathematical simplicity and biological plausibility. We reproduced the entrainment phenomenon observed in EEG during a photic driving experiment. More generally, we demonstrate that such a single area model can already yield very complex dynamics, including chaos, for biologically plausible parameter ranges. We chart the entire parameter space by means of characteristic Lyapunov spectra and Kaplan-Yorke dimension as well as time series and power spectra. Rhythmic and chaotic brain states were found virtually next to each other, such that small parameter changes can give rise to switching from one to another. Strikingly, this characteristic pattern of unpredictability generated by the model was matched to the experimental data with reasonable accuracy. These findings confirm that the NMM is a useful model of brain dynamics during photic driving. In this context, it can be used to study the mechanisms of, for example, perception and epileptic seizure generation. In particular, it enabled us to make predictions regarding the stimulus amplitude in further experiments for improving the entrainment effect
The type II phase resetting curve is optimal for stochastic synchrony
The phase-resetting curve (PRC) describes the response of a neural oscillator
to small perturbations in membrane potential. Its usefulness for predicting the
dynamics of weakly coupled deterministic networks has been well characterized.
However, the inputs to real neurons may often be more accurately described as
barrages of synaptic noise. Effective connectivity between cells may thus arise
in the form of correlations between the noisy input streams. We use constrained
optimization and perturbation methods to prove that PRC shape determines
susceptibility to synchrony among otherwise uncoupled noise-driven neural
oscillators. PRCs can be placed into two general categories: Type I PRCs are
non-negative while Type II PRCs have a large negative region. Here we show that
oscillators with Type II PRCs receiving common noisy input sychronize more
readily than those with Type I PRCs.Comment: 10 pages, 4 figures, submitted to Physical Review
Chaotic exploration and learning of locomotion behaviours
We present a general and fully dynamic neural system, which exploits intrinsic chaotic dynamics, for the real-time goal-directed exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modeled as a network of neural oscillators that are initially coupled only through physical embodiment, and goal-directed exploration of coordinated motor patterns is achieved by chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organized dynamics, each of which is a candidate for a locomotion behavior. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states, using its intrinsic chaotic dynamics as a driving force, and stabilizes on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced, which results in an increased diversity of motor outputs, thus achieving multiscale exploration. A rhythmic pattern discovered by this process is memorized and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronization method. Our results show that the novel neurorobotic system is able to create and learn multiple locomotion behaviors for a wide range of body configurations and physical environments and can readapt in realtime after sustaining damage
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