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Extra resources for Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part I
Pi = vi + ζ Δt (3) where vi denotes the membrane potential of ith neuron. ζ denotes bias of the membrane potential, whose value is common to all neurons in the network. The state value of the neuron is given by a sigmoid function of pi as follows. ai = 1 1 + e pi +α β (4) where α and β denote the parameters for adjusting center and steepness of the sigmoid function, respectively. 3 Classification of the Morphogenetic State of Neurons As mentioned earlier, we use the morphogenesis model proposed by Butza et al.
Therefore, in this morphogenetic algorithm, the activity of an individual neuron plays an important role . 4 Method of Numerical Experiments In our hippocampal model, we used 100 Izhikevich-type neurons in each region. 5. 21. 05, respectively. 1 Construction of Learning Patterns Each training pattern is formed in excitable neurons equivalent to 10% of the number of the neurons ﬁring in ECin. The spike strength is set to s = 1, and the intervals of spikes are set with pseudo-random numbers between 42 and 44.
Matteo Cacciola, Maurizio Fiasch´e, Giuseppe Megali, Francesco C. Morabito, and Mario Versaci Caller Interaction Classiﬁcation: A Comparison of Real and Binary Coded GA-MLP Techniques . . . . . . . . . . . . . . . . . . . . Pretesh B. Patel and Tshilidzi Marwala A Robust Technique for Background Subtraction in Traﬃc Video . . . Tao Gao, Zheng-guang Liu, Wen-chun Gao, and Jun Zhang 703 711 720 728 736 Gabor Filters as Feature Images for Covariance Matrix on Texture Classiﬁcation Problem .