Truccolo, Wilson A and Rangarajan, Govindan and Chen, Yonghong and Ding, Mingzhou (2003) Analyzing stability of equilibrium points in neural networks: a general approach. In: Neural Networks, 16 (10). pp. 1453-1460.
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Networks of coupled neural systems represent an important class of models in computational neuroscience. In some applications it is required that equilibrium points in these networks remain stable under parameter variations. Here we present a general methodology to yield explicit constraints on the coupling strengths to ensure the stability of the equilibrium point. Two models of coupled excitatory–inhibitory oscillators are used to illustrate the approach.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to Elsevier Science|
|Keywords:||Neural networks;Excitatory–inhibitory unit;Equilibrium point;Stability constraints;Jordan canonical form;Gershgo rin disc theorem|
|Department/Centre:||Division of Physical & Mathematical Sciences > Centre for Theoretical Studies
Division of Physical & Mathematical Sciences > Mathematics
|Date Deposited:||04 Dec 2008 06:45|
|Last Modified:||19 Sep 2010 04:52|
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