• Corpus ID: 151323179

Task-level models for image-stabilization behaviors in animals

  title={Task-level models for image-stabilization behaviors in animals},
  author={Eatai Roth},
  • E. Roth
  • Published 2012
  • Psychology, Biology
This research addresses a fundamental question in biology and neuroscience: how do animals process sensory information for the control of locomotor behaviors? Behaviors can be described as a sensorimotor loop: sensing (sensori-) governs action (-motor), action changes the environment, and these changes are perceived via sensing. Animal behavior arises from a concert of sensory, computational, and mechanical systems. Often, these mechanisms are studied independently (and often isolated from the… 


The Critical Role of Locomotion Mechanics in Decoding Sensory Systems
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It is found that both having an internal model of theVisual environment and its type make a significant difference in how the postural system responds to motion of the visual scene.
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The Dynamics of Legged Locomotion: Models, Analyses, and Challenges
This review describes mathematical models for legged animal locomotion, focusing on rapidly running insects and highlighting past achievements and challenges that remain.
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Weakly electric fish dramatically adjust their locomotor behavior in relation to changes of modality-specific information in a task in which increasing sensory volume is irrelevant, and data show that both categories of movements – whole-body oscillations and tail bends – actively shape the spatiotemporal dynamics of electrosensory feedback.
Synaptic Plasticity Can Produce and Enhance Direction Selectivity
A parsimonious model of motion processing that generates direction selective responses using short-term synaptic depression and can reproduce salient features of direction selectivity found in a population of neurons in the midbrain of the weakly electric fish Eigenmannia virescens is proposed.
Active flight increases the gain of visual motion processing in Drosophila
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An optimal state estimation model of sensory integration in human postural balance.
  • A. Kuo
  • Biology
    Journal of neural engineering
  • 2005
A model for human postural balance, combining state feedback control with optimal state estimation, which reproduces the general nature of postural motion as a function of sensory environment and is useful for predicting which sensors are most critical for balance, and how much they can deteriorate before posture becomes unstable.