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Towards cortex sized artificial neural systems.

Christopher Johansson1, Anders Lansner

  • 1Department of Numerical Analysis and Computer Science, Royal Institute of Technology, Stockholm, Sweden. cjo@nada.kth.se

Neural Networks : the Official Journal of the International Neural Network Society
|July 25, 2006
PubMed
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We developed a computational model of the mammalian neocortex using spiking neural networks and Hebbian learning. This large-scale simulation demonstrates efficient noise reduction and pattern completion, advancing abstract neural network capabilities.

Area of Science:

  • Computational neuroscience
  • Artificial neural networks
  • Mammalian neocortex modeling

Background:

  • The mammalian neocortex exhibits complex modular structures like hypercolumns and minicolumns.
  • Understanding neocortical computation requires large-scale, biologically plausible models.

Purpose of the Study:

  • To propose and implement an abstract computational model of the mammalian neocortex.
  • To investigate the structure, modularization, and size of the neocortex through simulation.

Main Methods:

  • Utilized a sparse, recurrently connected neural network with spiking leaky integrator units.
  • Incorporated continuous Hebbian learning for synaptic plasticity.
  • Implemented the model using both floating-point and fixed-point arithmetic.

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Main Results:

  • Simulations of mouse and rat cortex-sized networks ran in 44% and 23% of real-time, respectively.
  • A large-scale instance (1.6 x 10^6 units, 2 x 10^11 connections) demonstrated noise reduction and pattern completion.
  • Performance was found to be computation-bounded on cluster computers.

Conclusions:

  • The proposed abstract model effectively simulates neocortical circuitry and function.
  • Large-scale neural network simulations are feasible and can achieve complex cognitive tasks.
  • This work represents a frontier in simulating large-scale abstract neural networks regarding size and speed.