We study associative memory of an oscillator neural network with distributed native frequencies. The model is based on the use of the Hebb learning rule with random patterns (ξ i = ±1), and the distribution function of native frequencies is assumed to be symmetric with respect to its average. Although the system with an extensive number of stored patterns is not allowed to get entirely synchron...

Current studies in neurophysiology award a key role to collective behaviors in both neural information and image processing. This fact suggests to exploit phase locking and frequency entrainment in oscillatory neural networks for computational purposes. In the practical implementation of artificial neural networks delays are always present due to the non null processing time and the finite sign...

In this paper we propose a hybrid model of a neural oscillator, obtained by partially discretizing a well-known continuous model. Our construction points out that in this case the standard techniques, based on replacing sigmoids with step functions, is not satisfactory. Then, we study the hybrid model through both symbolic methods and approximation techniques. This last analysis, in particular,...

Networks of coupled oscillators are a form of dynamical network originally inspired by various biological and physical systems. Much existing research focuses on simple, restricted models which are analytically tractable. In order to explore networks which do not fit into the existing models, we have programmed a flexible numerical simulation of oscillator networks. We have used the simulated n...

We consider a coupled, heterogeneous population of relaxation oscillators used to model rhythmic oscillations in the pre-Bötzinger complex. By choosing specific values of the parameter used to describe the heterogeneity, sampled from the probability distribution of the values of that parameter, we show how the effects of heterogeneity can be studied in a computationally efficient manner. When m...

This paper introduces the Neural Oscillator Model of Speech Timing and Rhythm (NOMSTR), which is designed to be a flexible tool for investigating the systems which affect speech rhythm and timing through simulation. NOMSTR is an artificial neural network (ANN) model which incorporates oscillators, inspired by central pattern generators (CPGs), a type of neural circuit which underlies other type...

We analyze the response of the Morris-Lecar model to a periodic train of short current pulses in the period-amplitude plane. For a wide parameter range encompassing both class 2 and class 3 behavior in Hodgkin’s classification there is a multimodal transition between the set of odd modes and the set of all modes. It is located between the 2:1 and 3:1 locked-in regions. It is the same dynamic in...

A general analysis of an inserted long-period grating in an air-clad photonic crystal fiber for temperature and strain measurement is presented. The temperature and strain can be detected simultaneously by using an artificial neural network. A simulation study was carried out with the data set generated by using theoretical strain and temperature sensitivities of the long-period gratings. It in...

Recent experimental findings (Gray et al. 1989; Eckhorn et al. 1988) seem to indicate that rapid oscillations and phase-lockings of different populations of cortical neurons play an important role in neural computations. In particular, global stimulus properties could be reflected in the correlated firing of spatially distant cells. Here we describe how simple coupled oscillator networks can be...

The tools of weakly coupled phase oscillator theory have had a profound impact on the neuroscience community, providing insight into a variety of network behaviours ranging from central pattern generation to synchronisation, as well as predicting novel network states such as chimeras. However, there are many instances where this theory is expected to break down, say in the presence of strong co...