Biomimetic Neural Learning for Intelligent Robots: by Stefan Wermter, Günther Palm, Mark Elshaw

By Stefan Wermter, Günther Palm, Mark Elshaw

This cutting-edge survey comprises chosen papers contributed via researchers in clever platforms, cognitive robotics, and neuroscience together with contributions from the MirrorBot undertaking and from the NeuroBotics Workshop 2004. The examine paintings awarded demonstrates major novel advancements in biologically encouraged neural types to be used in clever robotic environments and biomimetic cognitive behavior.

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Extra info for Biomimetic Neural Learning for Intelligent Robots: Intelligent Systems, Cognitive Robotics, and Neuroscience

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A connectionist perspective on development. MIT Press, Cambridge, MA (1996) 5. : The neuroscience of language: on brain circuits of words and serial order. Cambridge University Press, Cambridge, UK (2003) 6. : Sequence detectors as a basis of grammar in the brain.

For example, the replay of sequence (1, 3) will activate a whole assembly of seven SDs 13, 14, 23, 11, 22, 33, and 44. The result is that the connections from WWs 1 and 3 to all seven SDs of the assembly get strengthened heteroassociatively. Additionally also the autoassociative connections between the seven SDs get strengthened. A neuronal assembly forms which is no longer selective for one sequence of WW activations, but rather for a sequence of any member of a set of WWs followed by the activation of any member of a second set.

2 Perspectives on Improving the Linear SD Model The analysis of the previous section reveals some limitations of the simple linear SD model: It is not very robust because the maximal peak or amplitude difference between critical and inverse SD cannot exceed 25 percent (eq. 12), and for reasonable noise parameter η there can be severe limitations on minimal and maximal possible word web delays ∆min and ∆max . One particularity of the model is the assumption that the word web activity decays exponentially with exp(−t), based on some experimental findings of sustained neural activity related to working memory [11].

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