Abstract
With the use of a biologically plausible artificial neural network in which connections are modified through Grossberg’s presynaptic learning rule, it is possible to simulate the spreading depression (SD) cortical phenomenon and analyze its behavior depending on different parameters related to neural plasticity and connectivity. The neural network that simulates a simplified cortex is formed by excitatory and inhibitory locally connected neurons. The conditions for the occurrence of SD are analyzed after an external stimulus is applied to the lattice simulating the cortex.
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Castello Paiva, D., Andina, D., Ropero Peláez, F.J. (2013). A Neural Network Simulation of Spreading Depression. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., de la Paz López, F., Toledo Moreo, F.J. (eds) Natural and Artificial Models in Computation and Biology. IWINAC 2013. Lecture Notes in Computer Science, vol 7930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38637-4_1
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DOI: https://doi.org/10.1007/978-3-642-38637-4_1
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