In a biologically plausible but computationally simplified integrate-andfire neuronal population, it is observed that transient synchronized spikes can occur repeatedly. However, groups with different properties exhibit different periods and different patterns of synchrony. We include learning mechanisms in these models. The effects of spike-timing-dependent plasticity have been known to play a distinct role in information processing in the central nervous system for several years. In this letter, neuronal models with dynamical synapses are constructed, and we analyze the effect of STDP on collective network behavior, such as oscillatory activity, weight distribution, and spike timing precision. We comment on how information is encoded by the neuronal signaling, when synchrony groups may appear, and what could contribute to the uncertainty in decision making.
A differential approach is proposed for tomographic rain field reconstruction using the estimated signal-to-noise ratio of microwave signals from low earth orbit satellites at the ground receivers, with the unknown baseline values eliminated before using least squares to reconstruct the attenuation field. Simulations are done when the baseline is modelled by an autoregressive process and when the baseline is assumed fixed. Comparisons between the reconstruction results for the differential and non-differential approaches suggest that the differential approach performs better in both scenarios. For high correlation coefficient and low model noise in the autoregressive process, the differential approach surpasses the non-differential approach significantly.
This contribution considers an adaptive control method based on a cognition-based framework to stabilize unknown nonlinear systems online. This method requires only the system outputs, which are assumed as measurable. The structure of the framework consists of three parts. The first part is based on a dynamic recurrent neural network (DRNN) to be used for local identification, analysis and multi-step-ahead prediction of the system. In the second part, a set of given input values will be calculated numerically with a geometrical criterion based on a suitable definition of quadratic stability. In the third part, the most suitable control input value is chosen for the next predefined time interval according to a suitable cost function. The proposed controller is able to gain useful local knowledge and define autonomously suitable local control input according to the stability criterion. Numerical examples using inverted pendulum system and Lorenz system are shown to demonstrate the successful application and performance of the method.
Traditionally, the radar cross-section is used to characterize the target in a forward scatter radar (FSR) system, the measurement of which requires the availability of the scattered signal. However, the scattered signal is often hard to be extracted, particularly when the illumination signal is opportunistic. In this paper, we introduce the concept of the forward scatter shadow ratio (FSSR) of a target as the ratio of the total received power density to the incident power density for a receiver at a certain location in an FSR system. It is argued that the FSSR can be a useful parameter in the studies of FSR systems as it is relevant to target detection, size estimation, classification and shadow profile imaging. Particularly, using mathematical analysis and numerical results, we demonstrate that the shadow profile of a target can be retrieved with the FSSR. The three sources of error in shadow profile retrieval, i.e., uncentered line of observation, shadow profile discretization and approximation of the imaginary error function are discussed.
We report the first results of simulating the coupling of neuronal, astrocyte, and cerebrovascular activity. It is suggested that the dynamics of the system is different from systems that only include neurons. In the neuron-vascular coupling, distribution of synapse strengths affects neuronal behavior and thus balance of the blood flow; oscillations are induced in the neuron-to-astrocyte coupling.
1Long-term neuronal behavior caused by two synaptic modification mechanisms Recently, more details have emerged of the interaction between neurons, glial cells, and the cerebrovascular network [1,2,3]. Most of this work is on the micro-level, involving only a few cells and capillaries or arterioles. Numerous investigations have confirmed the following discoveries: The modulation of synaptic efficacy will affect emergent behavior of neuronal assemblies [4,5]; Dilation of capillaries is highly related to the activity of nearby neurons [6]; Astrocytes are very sensitive to the level of neuronal activity because of their position and their sensitivity to activity-dependent changes in the chemical environment they share with neurons [7]; Intercellular Calcium waves between astrocytes are the main signalling mechanism within glial cell networks [8,9]. In this letter, we will build numerical models based on those physiological findings, because micro-descriptions using simplified models of firing [10,11] and wave propagation can be inserted into larger scale simulations. Here we show a modification of the synapse strengths that allows the neuronal firing and the cerebrovascular flow to be compatible on a meso-scale; with astrocyte signalling added, limit cycles exist in the coupled networks.Neurons are associated with capillaries in the brain, and according to physiological discoveries, the neuronal activity can dilate the capillaries that supply them. Our first model contains 2400 neurons and 30 branches of capillaries, each of which supplies 80 neurons. Each neuron has two states: '1' indicates that at that time the neuron was firing and '-1' means the neuron was not firing. All the synapses between these neurons are described by a
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