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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze each...
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Related Experiment Video

Updated: Jun 5, 2026

Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales
11:41

Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales

Published on: November 14, 2010

A compositionality machine realized by a hierarchic architecture of synfire chains.

Sven Schrader1, Markus Diesmann, Abigail Morrison

  • 1Honda Research Institute Offenbach, Germany.

Frontiers in Computational Neuroscience
|January 25, 2011
PubMed
Summary

This study introduces a hierarchical synfire chain model that generates complex behaviors by combining simultaneous and sequential action primitives. The model successfully produces drawing strokes, offering insights into proactive computation and neural activity signatures.

Keywords:
compositionalitymovement primitivessimulationsynfire chains

Related Experiment Videos

Last Updated: Jun 5, 2026

Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales
11:41

Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales

Published on: November 14, 2010

Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Robotics and Control

Background:

  • Complex behaviors are composed of simpler action primitives activated concurrently and sequentially.
  • Synfire chains model either simultaneous or sequential aspects of compositionality, but their compatibility was unaddressed.
  • Previous studies on simultaneous primitive activation were limited to reactive computations (e.g., perception).

Purpose of the Study:

  • To demonstrate a hierarchical synfire chain model capable of generating both simultaneous and sequential compositionality for proactive computations.
  • To investigate the generation of complex and ongoing actions using a novel network architecture.
  • To propose candidate signatures of synfire chain computation in neural activity during action execution.

Main Methods:

  • Developed a two-layer network model of synfire chains.
  • Used simple drawing strokes as a visualization for abstract action primitives.
  • Mapped feed-forward activity of upper-level chains to 2D motion and utilized closed-loop configurations in the lower layer for sequential generation.

Main Results:

  • The model generated drawing strokes representing combinations of primitive strokes by binding corresponding synfire chains.
  • Sequential drawing strokes were produced when the lower layer network was configured in a closed-loop manner.
  • Quantitative measures confirmed a wide parameter range where both simultaneity and sequentiality were achieved; random or deterministic patterns emerged based on connection patterns.

Conclusions:

  • A hierarchical synfire chain organization can account for both simultaneous and sequential aspects of action compositionality in proactive behaviors.
  • The model provides a framework for understanding how complex actions are generated from simpler components.
  • Investigated neural spiking activity to identify potential signatures of synfire chain computations during action execution.