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Related Concept Videos

Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Observational Learning01:12

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Associative Learning01:27

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Related Experiment Video

Updated: Jun 4, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Generative models for sequential dynamics in active inference.

Thomas Parr1, Karl Friston1, Giovanni Pezzulo2

  • 1Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London, UK.

Cognitive Neurodynamics
|December 23, 2024
PubMed
Summary
This summary is machine-generated.

The brain processes sequences using active inference and generative models. This approach explains cognitive operations like language and motor control by modeling how discrete sequences emerge from continuous neural activity.

Keywords:
Active inferenceBayesianGenerative modelSequential dynamicsVariational

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Area of Science:

  • Theoretical neurobiology
  • Computational neuroscience
  • Cognitive science

Background:

  • Cognitive operations rely on processing discrete sequences.
  • Neuronal dynamics underlie sequential processing.
  • Active inference offers a framework for understanding predictive brain function.

Purpose of the Study:

  • To explain sequential brain processing using active inference.
  • To demonstrate how generative models account for sequence processing.
  • To link continuous neuronal dynamics to discrete cognitive operations.

Main Methods:

  • Utilizing active inference principles.
  • Developing generative models of the sensed world.
  • Applying models to motor control, perception, and language.

Main Results:

  • Generative models comprising sequences explain sequential brain processing.
  • Active inference accounts for discrete sequences in cognition.
  • Demonstrated applications in handwriting, birdsong recognition, and language.

Conclusions:

  • Active inference provides a unified framework for sequential brain processing.
  • Generative models with sequences are key to understanding cognition.
  • This approach bridges continuous neural dynamics and discrete cognitive functions.