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Establishing Fungal Entomopathogens as Endophytes: Towards Endophytic Biological Control

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Published on: April 11, 2013

Internal Models in Biological Control.

Daniel McNamee^1,2, Daniel M Wolpert^1,3

¹Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom.

Annual Review of Control, Robotics, and Autonomous Systems

|May 21, 2019

Summary

This summary is machine-generated.

Internal models are crucial for flexible and robust biological sensorimotor control. These models, based on optimal feedback control and Bayesian inference, are reviewed for their computational roles and neural evidence.

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Area of Science:

Computational neuroscience
Motor control
Cognitive science

Background:

Sensorimotor control relies on internal models for flexible and robust behaviors.
Optimal feedback control and Bayesian inference provide a probabilistic framework for understanding biological control.

Purpose of the Study:

To review the computational roles of internal models in sensorimotor control.
To examine the neural and behavioral evidence for internal model implementation in the brain.

Main Methods:

Theoretical review of internal models in sensorimotor control.
Analysis of computational roles within a probabilistic framework.
Synthesis of neural and behavioral evidence.

Main Results:

Internal models are essential for optimizing motor system and environmental dynamics.
These models facilitate flexible and robust behavioral adaptation.
Evidence supports the brain's implementation of internal models for sensorimotor tasks.

Conclusions:

Internal models are a key component of efficient sensorimotor control.
The integration of optimal control and Bayesian inference offers a powerful theoretical lens.
Further research into neural mechanisms of internal models is warranted.