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CONTRIBUTORS:
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CONFERENCE NAME:
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CONF. LOCATION:
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Viene, Austria
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CONFERENCE YEAR:
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2006
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PUB TYPE:
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Conference Presentation
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SUBJECT(S):
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neuroscience, neurobiology, behavior, nonlinear forecasting
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DISCIPLINE:
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Biology
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HTTP:
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LANGUAGE:
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English
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PUB ID:
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103-426-004
(Last edited on
2006/04/13 03:45:55 GMT-6)
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SPONSOR(S):
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ABSTRACT:
Ever since the ancient Greeks, animals in general and insects in particular have been considered automata, or robots: if one only knew all their input variables, one could predict the motor output they would produce. However, even under constant environmental conditions, animals generate variable behavioral output. The question of whether such intrinsic behavioral variability reflects random noise (disorder) in otherwise deterministic input/output systems or an intrinsic, adaptive indeterminacy trait (order) is central for the basic understanding of brain function.
Here we show that spontaneously generated search algorithms (Lévy flights), but not random noise can account for the temporal structure in spontaneous yaw torque fluctuations in tethered Drosophila. Lévy flights are evolutionary conserved probabilistic behavior patterns, suggesting a general neural mechanism underlying spontaneous behavior. Further analysis suggests nonlinear mechanisms to be involved in generating these patterns, indicating a built-in, evolutionarily conserved spontaneity generator in the brain. We hypothesize that this generator can function independently of environmental input and that it evolved to generate flexible behavior in a complex world. Indeed, such flexible behavior patterns have been shown to outcompete random and deterministic patterns in a number of ecological situations. In the real world, predator avoidance and prey catching behavior spring to mind as other obvious beneficiaries from indeterminacy. One can easily conceive how “getting out of a rut” would also be difficult with only pre-programmed “responses”. Our findings imply that both general models of brain function and autonomous agents (robots or brain-based devices) must include relevant nonlinear, endogenous mechanisms if they strive to realistically simulate biological brains.
It is a tempting analogy to imagine all spontaneity being based on nonlinear feedback mechanisms in the brain.
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