Abstract
Models of neurons based on iterative maps allows the simulation of big networks of coupled neurons without loss of biophisical properties such as spiking, bursting or tonic bursting and with an affordable computational effort. In this work we explore by means of the use of Lyapunov ”energy” functions the asymptotic behavior of a set of coupled neurons where each neuron is modelled by an iterative map. The method here developed allows to establish conditions on the parameters of the system to achieve asymptotic stability and can be applied to different models both of neurons and network topologies.
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Aguirre, C., Campos, D., Pascual, P., Serrano, E. (2004). Criteria for Stability in Neural Network Models with Iterative Maps. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_10
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DOI: https://doi.org/10.1007/978-3-540-28647-9_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
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