The critical role of locomotion mechanics in decoding sensory systems

Abstract:

How do neural systems process sensory information to control locomotion? The weakly electric knifefish Eigenmannia, an ideal model for studying sensorimotor control, swims to stabilize the sensory image of a sinusoidally moving refuge. Tracking performance is best at stimulus frequencies less than ∼1 Hz. Kinematic analysis, which is widely used in the study of neural control of movement, pbkp_redicts commensurately low-pass sensory processing for control. The inclusion of Newtonian mechanics in the analysis of the behavior, however, categorically shifts the pbkp_rediction: this analysis pbkp_redicts that sensory processing is high pass. The counterintuitive pbkp_rediction that a low-pass behavior is controlled by a high-pass neural filter nevertheless matches previously reported but poorly understood high-pass filtering seen in electrosensory afferents and downstream neurons. Furthermore, a model incorporating the high-pass controller matches animal behavior, whereas the model with the low-pass controller does not and is unstable. Because locomotor mechanics are similar in a wide array of animals, these data suggest that such high-pass sensory filters may be a general mechanism used for task-level locomotion control. Furthermore, these data highlight the critical role of mechanical analyses in addition to widely used kinematic analyses in the study of neural control systems. Copyright © 2007 Society for Neuroscience.

Año de publicación:

2007

Keywords:

• Ribbon fin
• Electroreception
• Eigenmannia
• gymnotiformes
• Closed-loop model
• Untethered
• Sensorimotor control

Fuente:

scopus