Raul Cristian Muresan

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A novel method for pattern recognition using Discrete Fourier Transforms on the global pulse signal of a pulse-coupled neural network (PCNN) is presented in this paper. We describe the mathematical model of the PCNN and an original way of analyzing the pulse of the network in order to achieve scale-and translation-independent recognition for isolated(More)
We investigated the problem of automatic depth of anesthesia (DOA) estimation from electroencephalogram (EEG) recordings. We employed Time Encoded Signal Processing And Recognition (TESPAR), a time-domain signal processing technique, in combination with multi-layer perceptrons to identify DOA levels. The presented system learns to discriminate between five(More)
We show that standard, Hebbian spike-timing dependent plasticity (STDP) induces the precession of the firing phase of neurons in oscillatory networks, while anti-Hebbian STDP induces phase recession. In networks that are subject to os-cillatory inhibition, the intensity of excitatory input relative to the inhibitory one determines whether the phase can(More)
We investigated the relevance of single-unit recordings in the context of dynamical neural systems with recurrent synapses. The present study focuses on modeling a relatively small, biologically-plausible network of neurons. In the absence of any input, the network activity is self-sustained due to the resonating properties of the neurons. Recording of(More)
Spike train models are important for the development and calibration of data analysis methods and for the quantification of certain properties of the data. We study here the properties of a spike train model that can produce both oscillatory and non-oscillatory spike trains, faithfully reproducing the firing statistics of the original spiking data being(More)
We present a novel method that can be used to characterize the dynamics of a source neuronal population. A set of readout, regular spiking neurons, is connected to the population in such a way as to facilitate coding of information about the source in the relative firing phase of the readouts. We show that such a strategy is useful in revealing temporally(More)
Sustained activity in prefrontal cortex is associated with the maintenance of information during short-term memory (STM). We have used impurity reduction criteria of classification trees to investigate how the behavioral performance of a monkey during STM is reflected in the information content of three features of recorded signals: rates of individual(More)
RetinotopicNET is an efficient simulator for neural architectures with retinotopic-like receptive fields. The system has two main characteristics: it is event-driven and it takes advantage of the retinotopic arrangement in the receptive fields. The dynamics of the simulator are driven by the spike events of the simple integrate-and-fire neurons. By using an(More)
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