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 by way of researchers in clever structures, cognitive robotics, and neuroscience together with contributions from the MirrorBot undertaking and from the NeuroBotics Workshop 2004. The examine paintings offered demonstrates major novel advancements in biologically encouraged neural types to be used in clever robotic environments and biomimetic cognitive behavior.

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Example text

The Intentional Attunement Hypothesis 27 can be characterized at the functional level as embodied simulation, a specific mechanism, likely constituting a basic functional feature by means of which our brain/body system models its interactions with the world. The mirror neuron matching systems and the other non-motor mirroring neural clusters represent one particular sub-personal instantiation of embodied simulation. With this mechanism we do not just “see” an action, an emotion, or a sensation.

If this happens repeatedly, it is possible to define classes of string elements that are frequently being substituted with each other, and these can be considered the basis of so-called lexical categories or grammatical word classes. Think, for example of nouns such as boys, whales, fish, lobsters and verbs such as swim, sleep, jump, talk etc. Each member of one of these lexical or lexico-semantic categories can, in principle, co-occur with any member of the respective other category. The child learns this, although in real life it usually does not encounter all possible pairings.

For t ∈ [0; ∆] we use c = s + w · e−∆/τ and ν = xα,β (∆) = w · ατ,τx (∆)/(1 − τx /τ ). This and a similar procedure for the inverse detector’s potential xβ,α finally yields xα,β (t) = 1 · 1 − ττx w · α(∆) · e xβ,α (t) = 1 · 1 − ττx s · α(∆) · e− − t−∆ τx t−∆ τx w · α(t) ,0 ≤ t ≤ ∆ −∆ + (s + we τ ) · α(t − ∆),t ≥ ∆ (39) s · α(t) ,0 ≤ t ≤ ∆ ∆ + (w + se− τ ) · α(t − ∆),t ≥ ∆ (40) 40 A. Knoblauch and F. 2 0 0 20 40 time t 60 80 0 0 20 40 time t 60 80 Fig. 3. Membrane potential of the critical SD (left) and the inverse SD (right) for the “leaky-integrate” model as computed from eqs.

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