User:Awoika3/sandbox/Sensory-motor coupling

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Overview[edit]

Sensory-motor coupling is the coupling or integration of the sensory system and motor system. The integration of the sensory and motor systems allows an animal to take sensory information and use it to make useful motor actions. Additionally, outputs from the motor system can be used to modify the sensory system's response to future stimili. [1] [2] Sensorimotor integration is not a static process. For a given stimulus, there is no one single motor command. "Neural responses at almost every stage of a sensorimotor pathway are modified at short and long timescales by biophysical and synaptic processes, recurrent and feedback connections, and learning, as well as many other internal and external variables". [1]

Basics[edit]

Sensory Stimuli to Movement[edit]

Prior to movement, an animal's current sensory state is used to generate a motor command. To generate a motor command, first, the current sensory state is compared to the desired or target state. Then, the nervous system transforms the sensory coordinates into the motor system's coordinates and generates the necessary commands to move the muscles so that the target state is reached. [2]

Efference Copy[edit]

An important aspect of sensorimotor integration is the efference copy. The efference copy is a copy of a motor command that is used to predict what the new sensory state will be after the motor command has been completed. The efference copy can be used by the nervous system to distinguish self-generated environmental changes, compare an expected response to what actually occurs in the environment, and to increase the rate at which a command can be issued by predicting an organism's state prior to receiving sensory input. [2] [3]

Internal Model[edit]

An internal model is a theoretical model used by a nervous system to predict the environmental changes that result from a motor action. The internal model assumes that the nervous system has an internal representation of how a motor apparatus, the part of the body that will be moved, behaves in an environment.[4] [5] Internal models can be classified as either a forward model or an inverse model.

Foward Model[edit]

A forward model is a model used by the nervous system to predict the new state of the motor apparatus and the sensory stimuli that result from a motion. The forward model takes the efference copy as an input and outputs the expected sensory changes. The use of a forward model

Advantages[edit]
  • Allows modulation of sensory input that results from self-generated movement
  • Allows for correction of errors in future actions

Inverse Model[edit]

Examples[edit]

Gaze Stabilization[edit]

During flight, it is important for a fly to maintain a level gaze; however, it is possible for a fly to rotate. The rotation is detected as visually as a rotation of environment termed optical flow. The input of the optical flow is then converted into a motor command to the fly's neck muscles so that the fly will maintain a level gaze. This reflex is diminished in a stationary fly compared to when it is flying or walking. [1]

Singing Crickets[edit]

Male crickets sing by rubbing their forewings together. The sounds produced are loud enough to reduce the cricket's auditory system's response to other sounds. This desensitization is caused by the hyperpolarization of the Omega 1 neuron (ON1), an auditory interneuron [3] To reduce self-desensitization, the cricket's thoracic central pattern generator sends a corollary discharge, an efference copy that is used to inhibit an organism's response to self-generated stimuli, to the auditory system. [1] [3] The corollary discharge is used to inhibit the auditory system's response to cricket's own song and prevent desensitization. This inhibition allows the cricket to remain responsive to external sounds such as a competing male's song.

Speech[edit]

Sensorimotor integration is critical for speech production and acquisition and modulates the perception of speech. The auditory system affects the motor aspects of speech by providing feedback for a given motor command. Based on the feedback a person will modify future commands to produce the desired sound. [6]

Patient R.W.[edit]

Patient R.W. was a man who suffered damage in his parietal and [occipital lobe|occipital lobes]], areas of the brain related to processing visual information, due to a stroke. As a result, he experienced vertigo when he tried to track a moving object with his eyes. The vertigo was caused by his brain interpreting the world as moving. In normal people, the world is not perceived as in motion when tracking an object despite the fact that the world is moved across the retina as the eye moves. The reason for this is that the brain predicts the movement of the world across the retina as a consequence of moving the eyes. R.W., however, was unable to make this prediction. [7]

References[edit]

  1. ^ a b c d Huston, S. J., & Jayaraman, V. (2011). Studying sensorimotor integration in insects. Current Opinion in Neurobiology, 21(4). doi: 10.1016/j.conb.2011.05.030
  2. ^ a b c Flanders, M. (2011). What is the biological basis of sensorimotor integration?. Biological Cybernetics, 104(1/2), 1-8.
  3. ^ a b c Poulet, J. F. A., & Hedwig, B. (2003). A corollary discharge mechanism modulates central auditory processing in singing crickets. JOURNAL OF NEUROPHYSIOLOGY, 89(3), 1528-1540.
  4. ^ Mitsuo, K. Review: Internal models for motor control and trajectory planning. Current Opinion in Neurobiology, 9, 718-727. doi: 10.1016/S0959-4388(99)00028-8)
  5. ^ Tin, C., & Poon, C. S. (2005). Internal models in sensorimotor integration: perspectives from adaptive control theory. Journal of Neural Engineering, 2(3). doi: 10.1088/1741-2560/2/3/s01
  6. ^ Hickok, G., Houde, J., & Rong, F. (2011). Sensorimotor Integration in Speech Processing: Computational Basis and Neural Organization. Neuron, 69(3). doi: 10.1016/j.neuron.2011.01.019
  7. ^ Shadmehr, R., Smith, M. A., & Krakauer, J. W. (2010). Error Correction, Sensory Prediction, and Adaptation in Motor Control. Annual Review of Neuroscience, Vol 33, 33, 89-108. doi: 10.1146/annurev-neuro-060909-153135