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The direct motor pathways, also known as the pyramidal tracts, are a group of neural pathways that originate in the brain and descend through the spinal cord. They control the voluntary movement of the body. There are two major direct motor pathways: the corticospinal and the corticobulbar tracts.
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The indirect motor or extrapyramidal pathways originate in the brainstem, the lower portion of the brain that connects it to the spinal cord. They consist of several distinct tracts, each with specialized functions. The four main tracts of the indirect motor pathways are the vestibulospinal tract, the reticulospinal tract, the tectospinal tract, and the rubrospinal tract.
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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Related Experiment Video

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Molecular Autonomous Pathfinder Using Deep Reinforcement Learning.

Ken-Ichi Nomura1, Ankit Mishra1, Tian Sang1

  • 1Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089, United States.

The Journal of Physical Chemistry Letters
|May 9, 2024
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Summary
This summary is machine-generated.

We developed an AI framework using deep reinforcement learning to discover energy-efficient diffusion pathways in amorphous materials. This approach reveals atomistic diffusion mechanisms, overcoming challenges in glassy solids.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Artificial Intelligence

Background:

  • Diffusion in solids is crucial for chemical reactions but poorly understood in amorphous materials due to lack of defined pathways.
  • Material failures are often linked to slow diffusion processes in glassy solids.
  • Predicting diffusion mechanisms in amorphous materials presents significant computational challenges.

Purpose of the Study:

  • To develop an AI-guided simulation approach for uncovering energy-efficient diffusion pathways in amorphous solids.
  • To enable long-term atomistic simulations of diffusion processes.
  • To bridge the gap between slow diffusion and material failures in glassy materials.

Main Methods:

  • Proposed the molecular autonomous pathfinder (MAP) framework, utilizing deep reinforcement learning (DRL).
  • Employed a Deep Q-Network architecture with a distributed prioritized replay buffer for efficient agent training.
  • Used asynchronous agents for accelerated experience sampling and a piecewise nudged elastic band method for energy profile refinement.

Main Results:

  • The MAP framework successfully identified atomistic diffusion pathways and their energy profiles in amorphous silica.
  • Achieved simulation time scales comparable to experimental observations.
  • Demonstrated the capability of AI to uncover complex diffusion mechanisms in disordered materials.

Conclusions:

  • The AI-guided MAP framework provides an effective method for studying diffusion in amorphous materials.
  • This approach can accelerate the understanding of diffusion-controlled processes and material degradation.
  • Enables accurate prediction of diffusion mechanisms and timescales in challenging glassy systems.