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Non-Markovian state quantum state diffusion (35–37) also makes use of a Noise: A Handbook of Markovian and Non-Markovian Quantum Stochastic. Quantum optics is becoming increasingly important in fields such as quantum Noise: A Handbook of Markovian and Non-Markovian Quantum Stochastic. Non-classical light generated by quantum-noise-driven cavity(More)
Models of neurons based on iterative maps allows the simulation of big networks of coupled neurons without loss of biophysical properties such as spiking, bursting or tonic bursting and with an affordable computational effort. These models are built over a phenomenological basis and are mainly implemented by the use of iterative two-dimensional maps that(More)
Tomographic transforms [1] refers to a new kind of linear transforms that use a different approach than traditional transforms such as the Cohen's Class or the Wigner distribution to obtain a representation of a signal in the time-frequency plane. The idea of tomography is to decompose the signal by using the eigenfunctions of linear combinations of(More)
Time-frequency tomograms have been used for denois-ing and component separation of neuronal signals [1]. Time-frequency tomograms are particularly appropriate to identify the time unfolding of the frequency features of the signals. However there are components of neuro-nal signals, as the neural signatures, that are not well represented by a clear spectral(More)
In this work we study the formation of patterns of neuronal activity when some input are presented to the network. For this task a recently developed model of neuron is utilized. This model requires a very low computational effort but presents many characteristics of more complex models such as, spiking, bursting and sub-threshold oscillations, and(More)