Some Insights Into Computational Models of (Patho)physiological Brain Activity

Abstract

-The amount of experimental data concerning physiology and anatomy of the nervous system is growing very fast, challenging our capacity to make comprehensive syntheses of the plethora of data available. Computer models of neuronal networks provide useful tools to construct such syntheses. They can be used to interpret experimental data, generate experimentally testable predictions and formulate new hypotheses regarding the function of the neural systems. Models can also act as a bridge between different levels of neuronal organization. The ultimate aim of computational neuroscience is to provide a link between behavior and underlying neural mechanisms. Depending on the specific aim of the model, there are different levels of neuronal organization at which the model can be set. Models are constructed at the microscopic (molecular and cellular), macroscopic level (local populations or systems) or dynamical systems level. Apart from purely computational models, hybrid networks are being developed in which biological neurons are connected in vitro to computer simulated neurons. Also, neuromorphic systems are recently being created using silicon chips that mimic computational operations in the brain. This article reviews various computational models of the brain and insights obtained through their simulations.Index Terms-Brain modeling, computational neuroscience, nervous system, simulation

Epilepsy and Nonlinear Dynamics

“…Last but not least, it is of potential importance to develop neurocomputational models for the dynamics of neuronal networks underlying the epileptic process [152,153]. Using concepts from network theory, recent modeling studies already indicate the importance of network topology in epileptogenesis and seizure generation [154][155][156][157][158], which may help in interpreting the complex phenomena seen on the EEG during the interictal-to-preictal transition [159].…”

Go with the flow: Use of a directed phase lag index (dPLI) to characterize patterns of phase relations in a large-scale model of brain dynamics

“…Development of models of brain networks in the context of computational neuroscience can play an important role in addressing these problems (Coombes, 2010;Deco et al, 2008Deco et al, , 2011Suffczynski et al, 2006). The models of random, small-world and scale-free networks that initially inspired a graph theoretical approach to brain network studies are simple and mathematically NeuroImage 62 (2012) [1415][1416][1417][1418][1419][1420][1421][1422][1423][1424][1425][1426][1427][1428] elegant, but lack adequate detail to explain all observations, and do not fit within a single framework (Stam and van Straaten, 2012).…”

The neuronal sources of EEG: Modeling of simultaneous scalp and intracerebral recordings in epilepsy

“…On the other hand, biologically-inspired neuron models can provide a more accurate description of the temporal activity of neuronal sources (see (Suffczynski et al, 2006) for a detailed review).…”