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Related Concept Videos

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Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
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The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
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Perspectives on Neuroscience
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Neural Sequences and the Encoding of Time.

Saray Soldado-Magraner1, Dean V Buonomano2,3

  • 1Department of Neurobiology, University of California, Los Angeles, Los Angeles, CA, USA.

Advances in Experimental Medicine and Biology
|June 25, 2024
PubMed
Summary
This summary is machine-generated.

The brain encodes time using changing neural activity patterns, particularly neural sequences. These sequences offer a potential canonical mechanism for temporal computations in biological and artificial systems.

Keywords:
Neural population clocksNeural sequencesRampingTemporal processingTiming

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Experimental and computational evidence suggest the brain encodes time via dynamic neural activity patterns.
  • Two primary neural dynamic regimes observed are neural population clocks and ramping activity.
  • Neural population clocks, like neural sequences, generate complex spatiotemporal patterns with nonlinear temporal profiles.

Purpose of the Study:

  • To examine the role of neural sequences in encoding time.
  • To investigate how neural sequences may emerge in a biologically plausible manner.

Main Methods:

  • Review of experimental observations of neural sequences across species and behaviors.
  • Analysis of neural sequences in artificial neural networks trained on time-dependent tasks.
  • Examination of computational models for biologically plausible emergence of neural sequences.

Main Results:

  • Neural sequences are a prototypical example of neural population clocks.
  • Neural sequences have been observed across diverse species, brain areas, and behavioral paradigms.
  • Neural sequences emerge in artificial neural networks solving time-dependent tasks.

Conclusions:

  • Neural sequences play a significant role in the encoding of time.
  • Neural sequences may represent a canonical computational regime for temporal computations.
  • Understanding neural sequences offers insights into biological and artificial time encoding mechanisms.