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Related Experiment Video

Updated: Mar 24, 2026

Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm
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Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm

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Recurrent Network Models of Sequence Generation and Memory.

Kanaka Rajan1, Christopher D Harvey2, David W Tank3

  • 1Joseph Henry Laboratories of Physics and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.

Neuron
|March 15, 2016
PubMed
Summary
This summary is machine-generated.

Neural networks can learn to perform complex tasks like working memory and decision-making, even with random initial connections. Partial In-Network Training (PINning) modifies a few connections to enable sequence generation and efficient memory implementation.

Related Experiment Videos

Last Updated: Mar 24, 2026

Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm
06:35

Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm

Published on: April 28, 2016

35.7K

Area of Science:

  • Computational neuroscience
  • Systems neuroscience
  • Neural network modeling

Background:

  • Sequential neural activation is crucial for behaviors like working memory and decision-making.
  • Existing models often rely on pre-wired specialized architectures for sequence and memory functions.

Purpose of the Study:

  • To investigate if disordered recurrent neural networks can generate sequences and implement working memory efficiently.
  • To model and match experimental data using a novel learning process.

Main Methods:

  • Developed Partial In-Network Training (PINning) to modify a small fraction of connections in a disordered recurrent network.
  • Applied PINning to model cellular resolution imaging data from the posterior parietal cortex during a memory-guided task.

Main Results:

  • Demonstrated that a largely disordered network, after partial training, can efficiently produce neural sequences and implement working memory.
  • Showed that sequences propagate through the cooperation of recurrent synaptic interactions and external inputs, not feedforward or asymmetric connections.

Conclusions:

  • Neural sequences and working memory can emerge from learning in largely unstructured network architectures.
  • PINning offers an efficient method for training disordered networks to perform complex cognitive functions.