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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.