By Gaëlle Spielmann Moura, Yolanda Triñanes-Pego, Maria T. Carrillo-de-la-Peña (auth.), Amir Hussain, Igor Aleksander, Leslie S. Smith, Allan Kardec Barros, Ron Chrisley, Vassilis Cutsuridis (eds.)
Brain encouraged Cognitive structures 2008 (June 24-27, 2008; São Luís, Brazil) introduced jointly top scientists and engineers who use analytic, syntactic and computational tools either to appreciate the prodigious processing homes of organic platforms and, particularly, of the mind, and to take advantage of such wisdom to boost computational tools in the direction of ever better degrees of cognitive competence.
This publication contains the papers offered at 4 significant symposia:
Part I - Cognitive Neuroscience
Part II - Biologically encouraged Systems
Part III - Neural Computation
Part IV - types of Consciousness.
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Extra resources for Brain Inspired Cognitive Systems 2008
Then we discuss the issues concerning the minimum requirements that an artificial neural network (ANN) should fulfill in order that it would be capable of expressing the categories and mappings between them, underlying the MES. We conclude that a pulsed ANN based on Izhikevich’s formal neuron with STDP (spike time-dependent plasticity) has sufficient dynamical properties to achieve these requirements, provided it can cope with the topological requirements. Finally, we present some perspectives of future research concerning the proposed ANN topology.
Elegans is known and if we choose a neuron model that reflects a realistic biological-like dynamic behavior for each neuron, the computational costs and long-term numerical accuracy are difficult challenges to deal with. However, even if one could run a simulation that would be very accurate from the neurobiological point of view, the interpretation of the results in terms of information and knowledge processing would still be difficult, without an adequate framework for treating the cognitive processes at neural level.
This model allows the correct representation of time, enabling neurons to pass information by temporal spike coding, also being able to approximate continuous functions, using temporal coded inputs and outputs, requiring fewer neurons in some cases [11, 26], and usually with less network iterations. Networks composed of spiking neurons can transmit larger ammounts of data through the relative timing of only a few spikes by having groups neurons activating each other sequentially in a short timespan.