Nonlinear dynamical multi-scale model of associative memory

Alexander M. Duda, Stephen E Levinson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

How can we get such reliable behavior from the mind when the brain is made up of such unreliable elements as neurons? We propose that the answer is related to the emergence of stable brain states and we offer a model that illustrates how such states could arise. We discuss a new ab initio nonlinear dynamical multi-scale model that will serve as the foundation for an associative memory. Scale 0 consists of spiking Hodgkin-Huxley (HH) neurons. Scale 1 consists of components that are made up of large populations of HH neurons whose topological structure evolves according to a Hebbian-plasticity rule based on synchronous firing. The component's state is captured by the variance of phase synchrony for the population. Many such components are sparsely connected to form a large network, whose state can be captured by the n-tuple consisting of the individual states of each member component. Scale 2 takes the state of the overall network and upon examining the particular interrelationships of each component (determining how the state of one component affects the state of others) is able to generate a class of trajectories that is multistationary and stable periodic. Such a class we consider a memory; the encoding of many such memories leads to the creation of a robust associative memory. The details of the different scales are examined.

Original languageEnglish (US)
Title of host publicationProceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010
Pages867-872
Number of pages6
DOIs
StatePublished - Dec 1 2010
Event9th International Conference on Machine Learning and Applications, ICMLA 2010 - Washington, DC, United States
Duration: Dec 12 2010Dec 14 2010

Publication series

NameProceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010

Other

Other9th International Conference on Machine Learning and Applications, ICMLA 2010
CountryUnited States
CityWashington, DC
Period12/12/1012/14/10

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Keywords

  • Associative memory
  • Attractors
  • Hodgkin-Huxley neurons
  • Machine learning
  • Multi-scale modeling
  • Nonlinear dynamics

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction

Cite this

Duda, A. M., & Levinson, S. E. (2010). Nonlinear dynamical multi-scale model of associative memory. In Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010 (pp. 867-872). [5708958] (Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010). https://doi.org/10.1109/ICMLA.2010.135