By L.M. Ricciardi, G. Esposilo, V. Giorno, C. Valerio (auth.), José Mira, José R. Álvarez (eds.)
ISBN-10: 3540402101
ISBN-13: 9783540402107
ISBN-10: 3540448683
ISBN-13: 9783540448686
The two-volume set LNCS 2686 and LNCS 2687 represent the refereed lawsuits of the seventh overseas Work-Conference on synthetic and traditional 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 e-book and tackle the next issues: mathematical and computational tools in neural modelling, neurophysiological information research and modelling, structural and practical types of neurons, studying and different plasticity phenomena, complicated structures dynamics, cognitive tactics and synthetic intelligence, methodologies for internet layout, bio-inspired platforms and engineering, and purposes in a wide number of fields.
Read Online 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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Additional info 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
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Ibarz and O. Herreras extent. Using a scattered input that spanned throughout 300 µ m of the apical dendritic tree (about 70% of the total extent), the AP initiated typically in the axon-soma region. Within scattered input patterns, the latency of the AP decreased with increasing number of activated synapses, and most notably, the apparent speed of backpropagation (axon-soma-to-dendrite conduction) increased (compare 25 nS and 70 nS in Fig. 1A). When the same number of activated synapses were clustered within narrower dendritic bands (with the same average electrical distance to the axonsoma), the initial AP locus shifted from the axon toward dendrites (see Fig.
Moreover, simulations of the complete model show that the sum of the gating variables n and h is approximately constant. 927-n. 927-n)(V-VNa) - gK n4(V-VK) - gL(V-VL) dn/dt=(n∞ - n) / τ n . (2) This model retains many of the dynamical properties characterizing the complete one. For instance, for the complete model, it was shown in [7] that the transition from rest to periodic firing occurs by a saddle-node bifurcation. e. the periodic solution originates with arbitrarily low frequency values). 254 µ A/cm2, above which the model generates periodic firing of arbitrarily low frequency.
1 Introduction Many symposia on neural networks, as IWANN [lo], and plenty of papers in journals on neural computation or artificial neural nets are based on the old dream of neurocybernetics about the usefulness of tlie interplay between two fields: (1) Matliematical modeling of real neurons and neural networks and (2) A model of connectionist computation (an adaptive graph) with a large number of "small gain" elements of local calculus (weighted sum followed by sigmoid), where explicit and declarative external programming is partially substituted by "learning" through supervised andlor nonsupervised algorithms.
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.)
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