Volterra series

About: Volterra series is a research topic. Over the lifetime, 2731 publications have been published within this topic receiving 46199 citations.

Efficient temporal processing with biologically realistic dynamic synapses.

Abstract: Synapses play a central role in neural computation: the strengths of synaptic connections determine the function of a neural circuit. In conventional models of computation, synaptic strength is assumed to be a static quantity that changes only on the slow timescale of learning. In biological systems, however, synaptic strength undergoes dynamic modulation on rapid timescales through mechanisms such as short term facilitation and depression. Here we describe a general model of computation that exploits dynamic synapses, and use a backpropagation-like algorithm to adjust the synaptic parameters. We show that such gradient descent suffices to approximate a given quadratic filter by a rather small neural system with dynamic synapses. We also compare our network model to artificial neural networks designed for time series processing. Our numerical results are complemented by theoretical analyses which show that even with just a single hidden layer such networks can approximate a surprisingly large class of nonlinear filters: all filters that can be characterized by Volterra series. This result is robust with regard to various changes in the model for synaptic dynamics.

Aeroelastic Response of Nonlinear Wing Sections Using a Functional Series Technique

Abstract: This paper addresses the problem of the determination of the subcritical aeroelastic response and flutter instability of nonlinear two-dimensional lifting surfaces in an incompressible flow-field via indicial functions and Volterra series approach. The related aeroelastic governing equations are based upon the inclusion of structural and damping nonlinearities in plunging and pitching, of the linear unsteady aerodynamics and consideration of an arbitrary time-dependent external pressure pulse. Unsteady aeroelastic nonlinear kernels are determined, and based on these, frequency and time histories of the subcritical aeroelastic response are obtained, and in this context the influence of the considered nonlinearities is emphasized. Conclusions and results displaying the implications of the considered effects are supplied.

Nonlinear Modeling of Causal Interrelationships in Neuronal Ensembles

Abstract: The increasing availability of multiunit recordings gives new urgency to the need for effective analysis of ldquomultidimensionalrdquo time-series data that are derived from the recorded activity of neuronal ensembles in the form of multiple sequences of action potentials-treated mathematically as point-processes and computationally as spike-trains. Whether in conditions of spontaneous activity or under conditions of external stimulation, the objective is the identification and quantification of possible causal links among the neurons generating the observed binary signals. A multiple-input/multiple-output (MIMO) modeling methodology is presented that can be used to quantify the neuronal dynamics of causal interrelationships in neuronal ensembles using spike-train data recorded from individual neurons. These causal interrelationships are modeled as transformations of spike-trains recorded from a set of neurons designated as the ldquoinputsrdquo into spike-trains recorded from another set of neurons designated as the ldquooutputsrdquo. The MIMO model is composed of a set of multiinput/single-output (MISO) modules, one for each output. Each module is the cascade of a MISO Volterra model and a threshold operator generating the output spikes. The Laguerre expansion approach is used to estimate the Volterra kernels of each MISO module from the respective input-output data using the least-squares method. The predictive performance of the model is evaluated with the use of the receiver operating characteristic (ROC) curve, from which the optimum threshold is also selected. The Mann-Whitney statistic is used to select the significant inputs for each output by examining the statistical significance of improvements in the predictive accuracy of the model when the respective inputs is included. Illustrative examples are presented for a simulated system and for an actual application using multiunit data recordings from the hippocampus of a behaving rat.

Digital Postcompensation Using Volterra Series Transfer Function

Abstract: We propose a noniterative digital backward propagation technique, based on an inverse modified Volterra series transfer function to postcompensate transmission linear and nonlinear impairments in the presence of optical noise. Using a single-channel 40-Gb/s nonreturn-to-zero quadrature phase-shift-keying optical signal propagated over 20 × 80 km of standard single-mode fiber, and performing digital postcompensation around the Nyquist rate, our compensation algorithm is able to surpass the maximum accuracy obtained with a symmetric split-step Fourier method, enabling us to increase the nonlinear tolerance by approximately 2 dB.

Modeling Nonlinear Systems by Volterra Series

Abstract: The Volterra-series expansion is widely employed to represent the input-output relationship of nonlinear dynamical systems. This representation is based on the Volterra frequency-response functions (VFRFs), which can either be estimated from observed data or through a nonlinear governing equation, when the Volterra series is used to approximate an analytical model. In the latter case, the VFRFs are usually evaluated by the so-called harmonic probing method. This operation is quite straightforward for simple systems but may reach a level of such complexity, especially when dealing with high-order nonlinear systems or calculating high-order VFRFs, that it may loose its attractiveness. An alternative technique for the evaluation of VFRFs is presented here with the goal of simplifying and possibly automating the evaluation process. This scheme is based on first representing the given system by an assemblage of simple operators for which VFRFs are readily available, and subsequently constructing VFRFs of the t...