By L.M. Ricciardi, G. Esposilo, V. Giorno, C. Valerio (auth.), José Mira, José R. Álvarez (eds.)
The two-volume set LNCS 2686 and LNCS 2687 represent the refereed complaints of the seventh foreign Work-Conference on synthetic and usual Neural Networks, IWANN 2003, held in Ma?, Menorca, Spain in June 2003.
The 197 revised papers offered have been conscientiously reviewed and chosen for inclusion within the ebook and tackle the next themes: mathematical and computational equipment in neural modelling, neurophysiological info research and modelling, structural and practical types of neurons, studying and different plasticity phenomena, advanced structures dynamics, cognitive techniques and synthetic intelligence, methodologies for internet layout, bio-inspired platforms and engineering, and purposes in a huge number of fields.
Read or Download Computational Methods in Neural Modeling: 7th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2003 Maó, Menorca, Spain, June 3–6, 2003 Proceedings, Part I PDF
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Extra resources for Computational Methods in Neural Modeling: 7th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2003 Maó, Menorca, Spain, June 3–6, 2003 Proceedings, Part I
0 0 8 16 24 32 40 0 8 τ (msec) 16 24 32 40 Figure 3. Stationary values of the phase difference against the time constant of the inhibitory synapses. 8 µ A/cm2, c) IE =1 µ A/cm2. 8 µ A/cm2. For both panels the open (solid) circles denote unstable (stable) states. For a fixed IE let τ *(IE) be the highest τ value where bistability occurs. We can see from figure 3 that for τ 1 < τ < τ *(IE) there are unstable phase locking states that Synchronous Firing in a Population of Inhibitory Interneurons 29 bifurcate from τ *(IE) (through a subcritical pitchfork bifurcation).
8 µ A/cm2. For both panels it is τ = 8 msec and the open (solid) circles denote unstable (stable) states. Finally, the right panel of figure 4 shows that, for a fixed value of gSy, synchrony is promoted as gEl increases. 30 Santi Chillemi et al. The last issue addressed concerns the effect of the electrical coupling on the synchronization properties of an heterogeneous network of larger size (N=50). The investigation is done numerically, as the theoretical approach should now become very hard. It is assumed that the coupling is all to all and the heterogeneities are introduced by using different stimulation currents: IE(j) = I0 + ξ (j), ξ being a gaussian random variable of zero mean and standard deviation σ .
Wang, J. , vol. 4, pp. 84-97, 1992. 4. X. Wang and G. Buzsaki, “Gamma oscillations by synaptic inhibition in an interneuronal network model”, J. , vol. 16, 6402-6413, 1996. 5. C. van Vreeswijk, L. F. Abbott, B. Ermentrout, “When inhibition not excitation synchronizes neural firing”, J. Comput. 1, 313-321, 1994. 6. M. A. Whittington, R. D. Traub, N. Kopell, B. Ermentrout, E. H. Buhl, “Inhibition-based rhythms: experimental and mathematical observations on network dynamics”, International J. , vol.
Computational Methods in Neural Modeling: 7th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2003 Maó, Menorca, Spain, June 3–6, 2003 Proceedings, Part I by L.M. Ricciardi, G. Esposilo, V. Giorno, C. Valerio (auth.), José Mira, José R. Álvarez (eds.)