It is shown how, through the use of a single layer of spiking neurons, even more so with a single neuron, it is possible to make the classification of patterns, either a binary function as the XOR function or a database with tens of characteristics. Izhikevich model is used for modeling the behavior of the used spiking neurons. Mainly intends to exploit the use of a single neuron to perform cla...

In stochastic resonance (SR), the presence of noise helps a nonlinear system amplify a weak (sub-threshold) signal. Chaotic resonance (CR) is a phenomenon similar to SR but without stochastic noise, which has been observed in neural systems. However, no study to date has investigated and compared the characteristics and performance of the signal responses of a spiking neural system in some chao...

To understand the behavior of a neural circuit it is a presupposition that we have a model of the dynamical system describing this circuit. This model is determined by several parameters, including not only the synaptic weights, but also the parameters of each neuron. Existing works mainly concentrate on either the synaptic weights or the neural parameters. In this paper we present an algorithm...

In this paper, we propose a simple supervised associative learning approach for spiking neural networks. In an excitatory-inhibitory network paradigm with Izhikevich spiking neurons, synaptic plasticity is implemented on excitatory to excitatory synapses dependent on both spike emission rates and spike timings. As results of learning, the network is able to associate not just familiar stimuli b...

We study a modification of the Hoppensteadt-Izhikevich canonical model for networks of class I neurons, in which the ‘pulse’ emitted by a neuron is smooth rather than a δ-function. We prove two types of results about synchronization and desynchronization of such networks, the first type pertaining to ‘pulse’ functions which are symmetric, and the other type in the regime in which each neuron is...

To understand neural activity, two broad categories of models exist: statistical and dynamical. While statistical models possess rigorous methods for parameter estimation and goodness-of-fit assessment, dynamical models provide mechanistic insight. In general, these two categories of models are separately applied; understanding the relationships between these modeling approaches remains an area...