Self-organization, Computational Maps, and Motor Control by P. G. Morasso, V. Sanguineti
By P. G. Morasso, V. Sanguineti
Within the research of the computational constitution of biological/robotic sensorimotor platforms, allotted types have won middle level lately, with various concerns together with self-organization, non-linear dynamics, box computing and so on. This multidisciplinary study sector is addressed right here by way of a multidisciplinary workforce of participants, who supply a balanced set of articulated shows which come with stories, computational versions, simulation experiences, psychophysical, and neurophysiological experiments.The e-book is split into 3 components, each one characterised through a marginally assorted concentration: partly I, the most important topic issues computational maps which usually version cortical components, in keeping with a view of the sensorimotor cortex as "geometric engine" and the positioning of "internal versions" of exterior areas. half II additionally addresses difficulties of self-organization and box computing, yet in an easier computational structure which, even supposing missing a really expert cortical equipment, can nonetheless behave in a truly adaptive and remarkable means via exploiting the interplay with the true international. ultimately half III is targeted at the motor keep an eye on concerns on the topic of the actual homes of muscular actuators and the dynamic interactions with the world.The reader will locate varied techniques on arguable matters, reminiscent of the function and nature of strength fields, the necessity for inner representations, the character of invariant instructions, the vexing query approximately coordinate modifications, the excellence among hierachiacal and bi-directional modelling, and the effect of muscle stiffness.
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Additional resources for Self-organization, Computational Maps, and Motor Control
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Often we treat the field as varying continuously in time, although this is not necessary. It is sometimes objected that distributions of quantity in the brain are not in fact continuous, since neurons and even synapses are discrete. However, this objection is irrelevant. For the purposes of field computation, it is necessary only that the number of units be sufficiently large t h a t it may be treated as a continuum, specifically, that continuous mathematics can be applied. There is, of course, no specific number at which the ensemble becomes "big enough" to be treated as a continuum; this is an issue t h a t must be resolved by the modeler in the context of the use to which the model will be put.
There is no doubt that the great majority of studies on self-organized maps have been aimed in this direction, somehow mirroring the bias on receptive field properties which has characterized the neurobiological studies about the functions of cortical areas. Only a minority of researchers has investigated the topological consequences of applying the same Hebbian learning paradigms not to the input but to the lateral connections. Martinetz & Schulten (1994) have coined the term topology representing networks for expressing the fact that the lattice developed by the network, as a result of learning, may capture the topological structure of the input space.