From spiking neuron models to the neural dynamics of a population of neurons

FERNANDO MONTANI; EMILIA B. DELEGLISE; OSVALDO A ROSSO

Maceio, Brasil

Conferencia; XVIII Conference on Nonequilibrium Statistical Mechanics and Nonlinear Physics; 2014

We consider a network of cortical neurons with axonal conduction delays and time-dependent plasticity, which is representative of a cortical column. Each neuron is triggered following the dynamic of the Hodgkin-Huxley equations, and is randomly interconnected to other neurons. In our current work we use an information theory approach based on causal quanti_ers (entropy and statistical complexity evaluated PDF with Bandt-Pompe) to characterize the dynamics of neural activity of a population of neurons. This allows us to investigate the temporal structure of complex neural signals not only by using probability distributions associated with the inter-spikes intervals (PDF histograms), but considering instead much more subtle measures accounting for its causal structure.