Potential mechanisms for imperfect synchronization in parkinsonian basal ganglia

Neural activity in the brain of parkinsonian patients is characterized by the intermittently synchronized oscillatory dynamics. This imperfect synchronization, observed in the beta frequency band, is believed to be related to the hypokinetic motor symptoms of the disorder. Our study explores potential mechanisms behind this intermittent synchrony. We study the response of a bursting pallidal neuron to different patterns of synaptic input from subthalamic nucleus (STN) neuron. We show how external globus pallidus (GPe) neuron is sensitive to the phase of the input from the STN cell and can exhibit intermittent phase-locking with the input in the beta band. The temporal properties of this intermittent phase-locking show similarities to the intermittent synchronization observed in experiments. We also study the synchronization of GPe cells to synaptic input from the STN cell with dependence on the dopamine-modulated parameters. Dopamine also affects the cellular properties of neurons. We show how the changes in firing patterns of STN neuron due to the lack of dopamine may lead to transition from a lower to a higher coherent state, roughly matching the synchrony levels observed in basal ganglia in normal and parkinsonian states. The intermittent nature of the neural beta band synchrony in Parkinson's disease is achieved in the model due to the interplay of the timing of STN input to pallidum and pallidal neuronal dynamics, resulting in sensitivity of pallidal output to the phase of the arriving STN input. Thus the mechanism considered here (the change in firing pattern of subthalamic neurons through the dopamine-induced change of membrane properties) may be one of the potential mechanisms responsible for the generation of the intermittent synchronization observed in Parkinson's disease.Comment: 27 pages, 9 figure

How Noise and Coupling Induce Bursting Action Potentials in Pancreatic beta-cells

Unlike isolated beta-cells, which usually produce continuous spikes or fast and irregular bursts, electrically coupled beta-cells are apt to exhibit robust bursting action potentials. We consider the noise induced by thermal fluctuations as well as that by channel gating stochasticity and examine its effects on the action potential behavior of the beta-cell model. It is observed numerically that such noise in general helps single cells to produce a variety of electrical activities. In addition, we also probe coupling via gap junctions between neighboring cells,with heterogeneity induced by noise, to find that it enhances regular bursts.Comment: 40 pages, 10 figure

Optimal first-passage time in gene regulatory networks

The inherent probabilistic nature of the biochemical reactions, and low copy number of species can lead to stochasticity in gene expression across identical cells. As a result, after induction of gene expression, the time at which a specific protein count is reached is stochastic as well. Therefore events taking place at a critical protein level will see stochasticity in their timing. First-passage time (FPT), the time at which a stochastic process hits a critical threshold, provides a framework to model such events. Here, we investigate stochasticity in FPT. Particularly, we consider events for which controlling stochasticity is advantageous. As a possible regulatory mechanism, we also investigate effect of auto-regulation, where the transcription rate of gene depends on protein count, on stochasticity of FPT. Specifically, we investigate for an optimal auto-regulation which minimizes stochasticity in FPT, given fixed mean FPT and threshold. For this purpose, we model the gene expression at a single cell level. We find analytic formulas for statistical moments of the FPT in terms of model parameters. Moreover, we examine the gene expression model with auto-regulation. Interestingly, our results show that the stochasticity in FPT, for a fixed mean, is minimized when the transcription rate is independent of protein count. Further, we discuss the results in context of lysis time of an \textit{E. coli} cell infected by a $\lambda$ phage virus. An optimal lysis time provides evolutionary advantage to the $\lambda$ phage, suggesting a possible regulation to minimize its stochasticity. Our results indicate that there is no auto-regulation of the protein responsible for lysis. Moreover, congruent to experimental evidences, our analysis predicts that the expression of the lysis protein should have a small burst size.Comment: 8 pages, 3 figures, Submitted to Conference on Decision and Control 201

An alternative to the plasma emission model: Particle-In-Cell, self-consistent electromagnetic wave emission simulations of solar type III radio bursts

1.5D PIC, relativistic, fully electromagnetic (EM) simulations are used to model EM wave emission generation in the context of solar type III radio bursts. The model studies generation of EM waves by a super-thermal, hot beam of electrons injected into a plasma thread that contains uniform longitudinal magnetic field and a parabolic density gradient. In effect, a single magnetic line connecting Sun to earth is considered, for which several cases are studied. (i) We find that the physical system without a beam is stable and only low amplitude level EM drift waves (noise) are excited. (ii) The beam injection direction is controlled by setting either longitudinal or oblique electron initial drift speed, i.e. by setting the beam pitch angle. In the case of zero pitch angle, the beam excites only electrostatic, standing waves, oscillating at plasma frequency, in the beam injection spatial location, and only low level EM drift wave noise is also generated. (iii) In the case of oblique beam pitch angles, again electrostatic waves with same properties are excited. However, now the beam also generates EM waves with the properties commensurate to type III radio bursts. The latter is evidenced by the wavelet analysis of transverse electric field component, which shows that as the beam moves to the regions of lower density, frequency of the EM waves drops accordingly. (iv) When the density gradient is removed, electron beam with an oblique pitch angle still generates the EM radiation. However, in the latter case no frequency decrease is seen. Within the limitations of the model, the study presents the first attempt to produce simulated dynamical spectrum of type III radio bursts in fully kinetic plasma model. The latter is based on 1.5D non-zero pitch angle (non-gyrotropic) electron beam, that is an alternative to the plasma emission classical mechanism.Comment: Physics of Plasmas, in press, May 2011 issue (final accepted version

Stochasticity from function -- why the Bayesian brain may need no noise

An increasing body of evidence suggests that the trial-to-trial variability of spiking activity in the brain is not mere noise, but rather the reflection of a sampling-based encoding scheme for probabilistic computing. Since the precise statistical properties of neural activity are important in this context, many models assume an ad-hoc source of well-behaved, explicit noise, either on the input or on the output side of single neuron dynamics, most often assuming an independent Poisson process in either case. However, these assumptions are somewhat problematic: neighboring neurons tend to share receptive fields, rendering both their input and their output correlated; at the same time, neurons are known to behave largely deterministically, as a function of their membrane potential and conductance. We suggest that spiking neural networks may, in fact, have no need for noise to perform sampling-based Bayesian inference. We study analytically the effect of auto- and cross-correlations in functionally Bayesian spiking networks and demonstrate how their effect translates to synaptic interaction strengths, rendering them controllable through synaptic plasticity. This allows even small ensembles of interconnected deterministic spiking networks to simultaneously and co-dependently shape their output activity through learning, enabling them to perform complex Bayesian computation without any need for noise, which we demonstrate in silico, both in classical simulation and in neuromorphic emulation. These results close a gap between the abstract models and the biology of functionally Bayesian spiking networks, effectively reducing the architectural constraints imposed on physical neural substrates required to perform probabilistic computing, be they biological or artificial

Neurosystems: brain rhythms and cognitive processing

Neuronal rhythms are ubiquitous features of brain dynamics, and are highly correlated with cognitive processing. However, the relationship between the physiological mechanisms producing these rhythms and the functions associated with the rhythms remains mysterious. This article investigates the contributions of rhythms to basic cognitive computations (such as filtering signals by coherence and/or frequency) and to major cognitive functions (such as attention and multi-modal coordination). We offer support to the premise that the physiology underlying brain rhythms plays an essential role in how these rhythms facilitate some cognitive operations.098352 - Wellcome Trust; 5R01NS067199 - NINDS NIH HH

Electrophysiological analysis of transcranial direct current stimulation and its effect on cortical spreading depression

Transcranial direct current stimulation (TDCS) allows for the noninvasive modulation of cortical activity. In this study, the effects of cathodal and anodal TDCS treatment on baseline activity in the motor cortex of rats were investigated via translaminar electroencephalogram (EEG) recording and power spectral density analysis. Treatment with low intensity anodal TDCS for five minutes was found to increase delta and theta frequency cortical activity during and for up to five minutes following treatment. This study also assessed the interaction of TDCS with the phenomenon of cortical spreading depression (CoSD), which has been implicated in numerous disease states, including migraine and stroke. TDCS treatment was given concurrently with induction of CoSD via administration of potassium chloride to the surface of the dura. The presence of the spreading depression event, a characteristic low frequency wave observed to travel outwards from the point of CoSD induction and downwards through the cortex, was used as a proxy measure for the occurrence of CoSD. It was observed that animals treated with cathodal TDCS exhibited fewer spreading depression events relative to those treated with anodal TDCS or those receiving sham treatment. In this study, animals were segregated into groups that exhibited stimulus artifact during TDCS treatment and those that did not. Stimulus artifact was defined as a characteristic alpha and/or beta frequency activity spike lasting throughout and not longer than the period of stimulation. Those animals receiving TDCS without exhibiting stimulus artifact were considered for the purposes of this study to not have received proper TDCS treatment, and acted as a sham treatment group. Because salient differences emerged between the stimulus artifact positive and stimulus artifact negative groups, this study suggests that the presence of stimulus artifact could be used as a proxy measure for successful TDCS dosage

The design of a turboshaft speed governor using modern control techniques

The objectives of this program were: to verify the model of off schedule compressor variable geometry in the T700 turboshaft engine nonlinear model; to evaluate the use of the pseudo-random binary noise (PRBN) technique for obtaining engine frequency response data; and to design a high performance power turbine speed governor using modern control methods. Reduction of T700 engine test data generated at NASA-Lewis indicated that the off schedule variable geometry effects were accurate as modeled. Analysis also showed that the PRBN technique combined with the maximum likelihood model identification method produced a Bode frequency response that was as accurate as the response obtained from standard sinewave testing methods. The frequency response verified the accuracy of linear models consisting of engine partial derivatives and used for design. A power turbine governor was designed using the Linear Quadratic Regulator (LQR) method of full state feedback control. A Kalman filter observer was used to estimate helicopter main rotor blade velocity. Compared to the baseline T700 power turbine speed governor, the LQR governor reduced droop up to 25 percent for a 490 shaft horsepower transient in 0.1 sec simulating a wind gust, and up to 85 percent for a 700 shaft horsepower transient in 0.5 sec simulating a large collective pitch angle transient

Shaping bursting by electrical coupling and noise

Gap-junctional coupling is an important way of communication between neurons and other excitable cells. Strong electrical coupling synchronizes activity across cell ensembles. Surprisingly, in the presence of noise synchronous oscillations generated by an electrically coupled network may differ qualitatively from the oscillations produced by uncoupled individual cells forming the network. A prominent example of such behavior is the synchronized bursting in islets of Langerhans formed by pancreatic \beta-cells, which in isolation are known to exhibit irregular spiking. At the heart of this intriguing phenomenon lies denoising, a remarkable ability of electrical coupling to diminish the effects of noise acting on individual cells. In this paper, we derive quantitative estimates characterizing denoising in electrically coupled networks of conductance-based models of square wave bursting cells. Our analysis reveals the interplay of the intrinsic properties of the individual cells and network topology and their respective contributions to this important effect. In particular, we show that networks on graphs with large algebraic connectivity or small total effective resistance are better equipped for implementing denoising. As a by-product of the analysis of denoising, we analytically estimate the rate with which trajectories converge to the synchronization subspace and the stability of the latter to random perturbations. These estimates reveal the role of the network topology in synchronization. The analysis is complemented by numerical simulations of electrically coupled conductance-based networks. Taken together, these results explain the mechanisms underlying synchronization and denoising in an important class of biological models