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![Two-parameter diagrams show the global dynamics of the proposed neuron model when two parameters simultaneously vary. (a) The plane (k0, f) is obtained for m = 0.2, k1 = 0.01, and k2 = 0.5. (b) The plane (k0, m) is obtained for f = 1.0, k1 = 0.01, and k2 = 0.5. (c) The plane (k0, k1) is obtained for m = 0.2, f = 1.0, and k2 = 0.5, and (d) the plane (k0, k2) is obtained for m = 0.2, k1 = 0.01, and f = 1.0. Initial conditions are [0;0;0].](publication/365317239/figure/fig1/AS:11431281096649103@1668197955922/Two-parameter-diagrams-show-the-global-dynamics-of-the-proposed-neuron-model-when-two.jpg)
Two-parameter diagrams show the global dynamics of the proposed neuron model when two parameters simultaneously vary. (a) The plane (k0, f) is obtained for m = 0.2, k1 = 0.01, and k2 = 0.5. (b) The plane (k0, m) is obtained for f = 1.0, k1 = 0.01, and k2 = 0.5. (c) The plane (k0, k1) is obtained for m = 0.2, f = 1.0, and k2 = 0.5, and (d) the plane (k0, k2) is obtained for m = 0.2, k1 = 0.01, and f = 1.0. Initial conditions are [0;0;0].
Source publication
Understanding neuron function may aid in determining the complex collective behavior of brain systems. To delineate the collective behavior of the neural network, we consider modified tabu learning neurons (MTLN) with magnetic flux. Primarily, we explore the rest points and stability of the isolated MTLN, as well as its dynamical characteristics us...
Contexts in source publication
Context 1
... diagrams have been obtained by simultaneously varying two parameters of the proposed model and recording at each iteration the value of the maximum Lyapunov exponent of the model. From the value of the Lyapunov exponent of the considered neuron, two dominant behaviors have been recorded, as can be seen in Figure 2. On one hand, we have chaotic behavior supported by > 0. On the other hand, we have periodic behavior supported by < 0. For instance, in Figure 2a, the two-parameter diagram is depicted in (k0, f) parametric space, demonstrating that chaotic behavior can exist at sufficient magnitudes of f and k0, while the rest of the parameters exhibit periodic behavior. ...
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... diagrams have been obtained by simultaneously varying two parameters of the proposed model and recording at each iteration the value of the maximum Lyapunov exponent of the model. From the value of the Lyapunov exponent of the considered neuron, two dominant behaviors have been recorded, as can be seen in Figure 2. On one hand, we have chaotic behavior supported by > 0. On the other hand, we have periodic behavior supported by < 0. For instance, in Figure 2a, the two-parameter diagram is depicted in (k0, f) parametric space, demonstrating that chaotic behavior can exist at sufficient magnitudes of f and k0, while the rest of the parameters exhibit periodic behavior. Furthermore, in (k0, m) parametric space, chaotic behavior is observed only at lower magnitudes of excitation amplitude when k0 is varied (see Figure 2b). ...
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... the value of the Lyapunov exponent of the considered neuron, two dominant behaviors have been recorded, as can be seen in Figure 2. On one hand, we have chaotic behavior supported by > 0. On the other hand, we have periodic behavior supported by < 0. For instance, in Figure 2a, the two-parameter diagram is depicted in (k0, f) parametric space, demonstrating that chaotic behavior can exist at sufficient magnitudes of f and k0, while the rest of the parameters exhibit periodic behavior. Furthermore, in (k0, m) parametric space, chaotic behavior is observed only at lower magnitudes of excitation amplitude when k0 is varied (see Figure 2b). Following that, Figure 2c,d depict the dynamical behavior of TLN in (k0, k1) and (k0, k2) parametric spaces, respectively. ...
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... in (k0, m) parametric space, chaotic behavior is observed only at lower magnitudes of excitation amplitude when k0 is varied (see Figure 2b). Following that, Figure 2c,d depict the dynamical behavior of TLN in (k0, k1) and (k0, k2) parametric spaces, respectively. In both cases, chaotic attractors exist at lower k0 values with all values of k1. ...
