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Persuading Computers to Act More Like Brains

  • Heather Ames
  • Massimiliano Versace
  • Anatoli Gorchetchnikov
  • Benjamin Chandler
  • Gennady Livitz
  • Jasmin Léveillé
  • Ennio Mingolla
  • Dick Carter
  • Hisham Abdalla
  • Greg Snider
Chapter
First Online:
Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS, volume 4)

Abstract

Convergent advances in neural modeling, neuroinformatics, neuromorphic engineering, materials science, and computer science will soon enable the development and manufacture of novel computer architectures, including those based on memristive technologies that seek to emulate biological brain structures. A new computational platform, Cog Ex Machina, is a flexible modeling tool that enables a variety of biological-scale neuromorphic algorithms to be implemented on heterogeneous processors, including both conventional and neuromorphic hardware. Cog Ex Machina is specifically designed to leverage the upcoming introduction of dense memristive memories close to computing cores. The MoNETA (Modular Neural Exploring Traveling Agent) model is comprised of such algorithms to generate complex behaviors based on functionalities that include perception, motivation, decision-making, and navigation. MoNETA is being developed with Cog Ex Machina to exploit new hardware devices and their capabilities as well as to demonstrate intelligent, autonomous behaviors in both virtual animats and robots. These innovations in hardware, software, and brain modeling will not only advance our understanding of how to build adaptive, simulated, or robotic agents, but will also create innovative technological applications with major impacts on general-purpose and high-performance computing.

Keywords

Hewlett Packard Chromatic Feature Biological Brain Information Information Silicon Neuron 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Acknowledgments

The work was supported in part by the Center of Excellence for Learning in Education, Science and Technology (CELEST), a National Science Foundation Science of Learning Center (NSF SBE-0354378 and NSF OMA-0835976). This work was also partially funded by the DARPA SyNAPSE program, contract HR0011-09-3-0001. The views, opinions, and/or findings contained in this chapter are those of the authors and should not be interpreted as representing the official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency, the Department of Defense, or the National Science Foundation.

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Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Heather Ames
    • 1
  • Massimiliano Versace
    • 1
  • Anatoli Gorchetchnikov
    • 1
  • Benjamin Chandler
    • 2
  • Gennady Livitz
    • 1
  • Jasmin Léveillé
    • 1
  • Ennio Mingolla
    • 1
  • Dick Carter
    • 2
  • Hisham Abdalla
    • 2
  • Greg Snider
    • 2
  1. 1.Neuromorphics Lab, Center of Excellence for Learning in Education, Science, and Technology (CELEST)Boston UniversityBostonUSA
  2. 2.HP LabsPalo AltoUSA

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