Effect of the Background Activity on the Reconstruction of Spike Train by Spike Pattern Detection
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Nonlinear dynamics emerging in large scale neural networks with ontogenetic and epigenetic processes
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Deterministic nonlinear spike train filtered by spiking neuron model
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Distributed deterministic temporal information propagated by feedforward neural networks
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
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Dynamics of the activity of neuronal networks have been intensively studied from the view point of the nonlinear dynamical system. The neuronal activities are recorded as multivariate time series of the epochs of spike occurrences–the spike trains–which are often effected by intrinsic and measuring noise. The spike trains can be considered as a mixture of a realization of deterministic and stochastic processes. Within this framework we considered several simulated spike trains derived from the Zaslavskii map with additive noise. The ensemble of all preferred firing sequences detected by the pattern grouping algorithm (PGA) in the noisy spike trains form a new time series that retains the dynamics of the original mapping.