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The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
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Ampere's law states that for any closed looped path, the line integral of the magnetic field along the path equals the vacuum permeability times the current enclosed in the loop. If the fingers of the right hand curl along the direction of the integration path, the current in the direction of the thumb is considered positive. The current opposite to the thumb direction is considered negative.
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Related Experiment Video

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Simulating Active Inference Processes by Message Passing.

Thijs W van de Laar1, Bert de Vries1,2

  • 1Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.

Frontiers in Robotics and AI
|January 27, 2021
PubMed
Summary
This summary is machine-generated.

Active inference (AI) automates purposeful behavior by minimizing free energy. This study demonstrates AI automation using ForneyLab and a novel experimental protocol for simulated agents, enabling goal-directed actions in dynamic environments.

Keywords:
Forney-style factor graphsactive inferencefree-energy principlemessage passingstate-space models

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Robotics

Background:

  • The Free Energy Principle (FEP) describes biological agents' perseverance via environmental interaction.
  • Active Inference (AI) posits agents act on prior beliefs, minimizing variational free energy for purposeful behavior.
  • Manual derivation of AI algorithms for dynamic models is complex and error-prone.

Purpose of the Study:

  • To automate active inference (AI) for dynamic models using probabilistic programming.
  • To introduce a formal experimental protocol for simulated AI agents.
  • To demonstrate goal-directed behavior in AI agents within simulated environments.

Main Methods:

  • Utilized ForneyLab, a probabilistic programming toolbox for variational inference on dynamic models.
  • Modeled AI agents in dynamic environments as probabilistic state-space models (SSMs).
  • Performed inference for perception and control via message passing on SSM factor graphs.

Main Results:

  • Successfully automated AI using ForneyLab for dynamic models.
  • Developed and applied a formal experimental protocol for simulated AI.
  • Demonstrated goal-directed behavior in classical reinforcement learning examples (Bayesian thermostat, mountain car).

Conclusions:

  • ForneyLab enables automated active inference for dynamic models.
  • The proposed protocol facilitates the simulation of goal-directed behavior in AI agents.
  • This work provides a framework for developing and testing AI in complex environments.