More broadly, the neuronal migration model presented here highlights the potential for integrating theory, computational approaches and experimental data in neuroscience and in biology more generally [ 43- 45].
This model presents a reasonable compromise between dissociated cell cultures and models using intact living animals as most neuronal and glial cells survive [ 27] and their cytoarchitecture and connective organisation are well preserved [ 18, 23, 28- 32].
Here, we present a neuronal network model that convenes both neuronal and network representations with the goal to describe brain dynamics involved in the development of epilepsy.
The MAM model presents reductions in neuronal soma size within the entorhinal cortex, CA3 and CA4 that resemble post-mortem findings from schizophrenic patients (Table 1).
In addition to the aforementioned Kuramoto model of coupled oscillators and Ising model of magnetization, Honey et al. presented a neuronal mass model based analysis that could predict the functional connectivity from the human anatomical structure [ 21].
The mouse neuroblastoma-derived Neuro2A cell line, a widely used model of neuronal physiology, presents simpler morphology than primary hippocampal neurons, enabling high-resolution quantitative measure of cellular structure and biomechanical characteristics.
MCN focuses on several seminal mathematical models used in neuronal coding, neuronal and network dynamics, and learning in neuronal networks.
Here we present a computational model of neuronal migration, in which four key molecular entities, Lis1, DCX, Reelin and GABA, form a molecular program that mediates the migration process.
More importantly, the ability to present remarkable inter-neuronal and intra-neuronal randomness using the devised neuron was also verified.
The ability of the model to evolve various biological plasticity regimes, together with its ability to present genomic and neuronal phenomena suggest that this simulation approach can serve as a tool for investigating evolutionary aspects of synaptic plasticity.
