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Newton's first law states that a net external force causes a change in motion. External forces act on an object or system, originating outside of the object or system. In contrast, internal forces originate inside the system of interest and do not lead to any acceleration. In simpler words, internal forces are forces that act on one part of an object and are exerted by another part of the same object. External forces are forces that act on an object due to some other object. Therefore, when...
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Switching behavior in Bipolar Junction Transistors (BJTs) is a fundamental aspect utilized in various electronic circuits, particularly for digital logic applications like switches and amplifiers. In a typical switching circuit, a BJT alternates between cut-off and saturation modes, corresponding to the "off" and "on" states, respectively, thus behaving like an ideal switch.
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What is a Mode?01:07

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The mode is one of the commonly used measures of a central tendency. It is defined as the most frequent value in a data set.
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Switching between internal and external modes: A multiscale learning principle.

Christopher J Honey1, Ehren L Newman2, Anna C Schapiro3

  • 1Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA.

Network Neuroscience (Cambridge, Mass.)
|August 10, 2018
PubMed
Summary
This summary is machine-generated.

Brains learn by switching between internal and external processing modes. This neural switching, observed across various brain scales, drives learning and integrates new information with existing knowledge.

Keywords:
AcetylcholineContrastive learningDefault modeHippocampusLearningSleepSwitchingTimescale

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Brains build internal models for perception, prediction, and action.
  • Neural circuits learn local models for efficient representation.
  • The mechanism of internal model learning remains an open question.

Purpose of the Study:

  • To propose and review evidence for a mode-switching mechanism in neural learning.
  • To explore how internal/external processing shifts facilitate learning at multiple scales.
  • To hypothesize predictions about mode-switching and information integration.

Main Methods:

  • Review of computational modeling evidence for mode-switching as a learning driver.
  • Examination of empirical findings on neural mode-switching across different brain systems and timescales.
  • Formulation of testable predictions based on the proposed framework.

Main Results:

  • Computational models demonstrate mode-switching generates error signals for learning.
  • Empirical evidence shows mode-switching occurs from subsecond hippocampal fluctuations to whole-brain wake-sleep cycles.
  • The proposed framework unifies learning mechanisms across diverse neural scales.

Conclusions:

  • Continual switching between internally and externally biased processing modes facilitates neural learning.
  • This mode-switching mechanism operates across multiple temporal and spatial scales within the brain.
  • Disrupting mode-switching impairs the integration of new information with prior knowledge, supporting the framework's predictions.