Causal information quantification of prominent dynamical features of biological neurons.
Neurons tend to fire a spike when they are near a bifurcation from the resting state to spiking activity. It is a delicate balance between noise, dynamic currents and initial condition that determines the phase diagram of neural activity. Many possible ionic mechanisms can be accounted for as the source of spike generation. Moreover, the biophysics and the dynamics behind it can usually be described through a phase diagram that involves membrane voltage versus the activation variable of the ionic channel. In this paper, we present a novel methodology to characterize the dynamics of this system, which takes into account the fine temporal 'structures' of the complex neuronal signals. This allows us to accurately distinguish the most fundamental properties of neurophysiological neurons that were previously described by Izhikevich considering the phase-space trajectory, using a time causal space: statistical complexity versus Fisher information versus Shannon entropy.
['Montani|Fernando|F|', 'Baravalle|Roman|R|', 'Montangie|Lisandro|L|', 'Rosso|Osvaldo A|OA|']
D000818:Animals D001703:Biophysics D001921:Brain D003198:Computer Simulation D004569:Electroencephalography D006801:Humans D008564:Membrane Potentials D008959:Models, Neurological D015233:Models, Statistical D009474:Neurons D009991:Oscillometry D012815:Signal Processing, Computer-Assisted D009435:Synaptic Transmission
neurons statistical complexity entropy Fisher information measure