Titlebook: Artificial Neural Networks: Biological Inspirations – ICANN 2005; 15th International C W?odzis?aw Duch,Janusz Kacprzyk,S?awomir Zadro?ny Co

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Mikhail N. Zervas,Gerlas van den Hovenevolution in time of the population density is determined by a partial differential equation. Now, the discussion of most researchers is based on the population density function. In this paper, we propose a new function to characterize the population of excitatory and inhibitory spiking neurons and

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Waveguide Properties of Optical Fibres, assume inhibitory coupling between minicolumn activities and Hebbian type synaptic plasticity of afferents to the minicolumns. If input in the form of activity patterns is presented, self-organization of receptive fields (RFs) of the minicolumns is induced. Self-organization is shown to appropriate

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Alejandro Giraldo Soto,Walter Kaufmannf neuronal population activity and information theoretic tools to investigate whether this arrangement of synapses allows efficient information transmission. We find that efficient information transmission requires that the tuning curve of the afferent neurons is approximately as wide as the spread

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Namkon Lee,Sungwook Kim,Gijoon Parkmethod to cope with these variabilities by considering a single learner for each acquisition session. Each learner consists of a channel selection procedure and a classifier. Our algorithm has been benchmarked with the data and the results of the BCI 2003 competition dataset and we clearly show that

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Sébastien Wolf,Simon Cleven,Oldrich Vlasákp to and including V1 (as an orientation-filtering system). We then develop architectures for afferents to V2 and thence to V4, both of which are trained by a causal Hebbian law. We use an incremental approach, using sequences of increasingly complex stimuli at an increasing level of the hierarchy.

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Salah Altoubat,Nadia Nassif,Mohamed Maalejarning processes, which change a neuron’s excitability. Here, we study a single, continuous activation model neuron and derive a gradient rule for the intrinsic plasticity based on information theory that allows the neuron to bring its firing rate distribution into an approximately exponential regim

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Katharina Look,Peter Heek,Peter Markral types of neurons, which are modeled as conductancebased leaky integrate-and-.re units with realistic values of parameters adopted from known anatomical and physiological data. We demonstrate that the recurrent inhibitory circuit composed of granule and Golgi cells can represent a time passage by

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