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2008), synchronized inputs received by selected cell assemblies are able to generate waves of depolarization following the complex dynamics (Makarenko and Llinás 1998 Gollo et al. 2008), even in the presence of noise (Asai and Villa 2008), thanks to their sensitivity to the temporal precision in sequences of spikes.įollowing the general encoding principle that neurons that are more strongly depolarized are going to fire earlier than the neurons that are less optimally stimulated (von der Malsburg and Schneider 1986 Singer 1993 Fries et al. At the scale of neuronal dynamics, it has been hypothesized that complex information can be transmitted through neural networks (Asai et al. The relationship between subsequent action potentials forms complex patterns typically associated with nontrivial dynamics and fractal dimensionality (Longtin 1993 Iglesias et al. Single neuron experimental recordings show that precise neuronal discharges can be arranged in sequences of spikes that appear much more often than expected by chance (Abeles and Gerstein 1988 Tetko and Villa 2001 Reinoso et al. 1999 Segundo 2003) to the mesoscale of large neuronal populations within, e.g., cortical columns (Stam 2005 Myers and Kozma 2018) has reinforced the hypothesis of a nonlinear source of complexity in brain dynamics (Korn and Faure 2003). The analysis of many brain signals ranging from the microscopic scale of single neurons (Celletti et al. Finally, we discuss the effect of local excitatory/inhibitory balance on these results and how excitability in cortical columns, controlled by neuromodulatory innervation of the cerebral cortex, may contribute to set a fine tuning and gating of the information fed to the cortex.
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Our analysis shows that the neural mass model has the capacity for gating the input signal and propagate selected temporal features of that signal. After converting phase and amplitude changes obtained into point processes, we quantify input–output similarity following a threshold-filtering algorithm onto the amplitude wave peaks.
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The phase shift that we observe, when we drive the neural mass model with specific chaotic inputs, shows that the local field potential amplitude peak appears in less than one full cycle, thus allowing traveling waves to encode temporal information. Here, we use a neural mass model to investigate to what extent precise temporal information, carried by deterministic nonlinear attractor mappings, is filtered and transformed into fluctuations in phase, frequency and amplitude of oscillatory brain activity. Despite these experimental evidences, the link between highly temporally organized input patterns and EEG waves has not been studied in detail. At the same time, the synchronized activity of large ensembles produces local field potentials that propagate through highly dynamic oscillatory waves, such that, at the whole brain scale, complex spatiotemporal dynamics of electroencephalographic (EEG) signals may be associated to sensorimotor decision making processes. It is well known that neuronal networks are capable of transmitting complex spatiotemporal information in the form of precise sequences of neuronal discharges characterized by recurrent patterns.