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Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or playing an...
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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

Learning shapes spontaneous activity itinerating over memorized states.

Tomoki Kurikawa1, Kunihiko Kaneko

  • 1Department of Basic Science, University of Tokyo, Tokyo, Japan. kurikawa@complex.c.u-tokyo.ac.jp

Plos One
|March 17, 2011
PubMed
Summary

This study proposes memory recall involves spontaneous neural activity shifting to output activity upon input, a process termed bifurcation. A novel neural network model demonstrates this, showing learned patterns lead to itinerant spontaneous activity.

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Area of Science:

  • Computational Neuroscience
  • Dynamical Systems Theory
  • Machine Learning

Background:

  • Neural dynamical systems generate outputs from inputs, with memory often residing in attractors.
  • Spontaneous neural activity and its input-dependent changes were understudied.
  • Recent experiments show structured spontaneous activity and its modulation by inputs.

Purpose of the Study:

  • To propose memory recall as a bifurcation of spontaneous neural activity upon input.
  • To introduce a novel neural network model for studying this phenomenon.
  • To investigate the role of learning and spontaneous activity in memory recall.

Main Methods:

  • Developed a reinforcement-learning-based layered neural network with two synaptic time scales.
  • Investigated input-output (I/O) relation memorization based on time scale differences.
  • Analyzed neural dynamics and spontaneous activity patterns post-learning.

Main Results:

  • The model successfully memorized I/O relations when synaptic time scales differed appropriately.
  • Learned neural dynamics adapted to generate appropriate outputs for given inputs.
  • Increased memorized patterns resulted in spontaneous activity itinerating over learned patterns.
  • Theoretical findings align with experimental observations of itinerant activity in the visual cortex.

Conclusions:

  • Memory recall can be understood as a bifurcation process driven by spontaneous neural activity.
  • Itinerant spontaneous activity is a natural consequence of learning multiple patterns.
  • This itinerant activity facilitates network bifurcation, aiding memory recall.