Computational Models And Theory Information Approach To Characterize A Dynamical Neural Network

EMILIA B. DELEGLISE; FERNANDO MONTANI

Huerta Grande, Córdoba, Argentina.

Congreso; XXVIII CONGRESO ANUAL DE LA SOCIEDAD ARGENTINA DE INVESTIGACIÓN EN NEUROCIENCIAS; 2013

SOCIEDAD ARGENTINA DE INVESTIGACIÓN EN NEUROCIENCIAS

We consider a network of cortical neurons with axonal conduction delays and spike-timing-dependent plasticity, which is representative of a cortical hypercolumn. Each neuron is randomly interconnected to other neurons. The network model is based on the Simple Model of Spiking Neurons, by E. Izhikevich. This model reproduces spiking and bursting behavior of known types of cortical neurons and combines the biologically plausibility of Hodgkin-Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons. In our current work we use an Information Theory approach based on causal quantifiers (permutation entropy and statistical complexity) to characterize the dynamics of neural activity of a simulated population of neurons. We investigate the ordinal patterns of complex neural signals to estimate the optimal parameters of the neuronal network using causal Fisher information. This approach might become a useful tool to quantify the causal weight in the processing of information when considering different areas of the cortex.