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Evolving complete cognitive architectures: The role of neural competition and diffusive emotional control for learning and emergent cognitive capabilities Claudius Gros, Gregor Kaczor Institute for Theoretical Physics Goethe University


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Evolving complete cognitive architectures: The role of neural competition and diffusive emotional control for learning and emergent cognitive capabilities

Claudius Gros, Gregor Kaczor Institute for Theoretical Physics Goethe University Frankfurt/Germany

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Assumption

 A complete cognitive agent is not just a

complex input-output device, driven by the sensory input, but characterized by autonomously generated internal activities.

 If so, how and when do these internal states

and activity patterns build up the correlation with information it receives via the sensory data input stream?

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Assumption

 We think, that correlations between internal and

external states are generated whenever the later are able to influence and modify the internal thought processes.

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Cognitive System with autonomous dynamics

Dhan Layer Input Layer Backlinks

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Dhan Layer Input Layer Backlinks

Dense Homogeneous Associative Network Model

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Autonomous dynamics layer

Inhibitory background Excitatory links

  • Dashed lines
  • reservoir
  • Continuous lines- activities
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Autonomous internal activity and clique encoding

  • Dashed lines
  • reservoir
  • Continuous lines- activities
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Fully connected layers

Dhan Layer Input Layer Backlinks

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Dense Homogeneous Associative Network Model

Dhan Layer Input Layer Backlinks Regular Stimuli 90% Noise Stimuli 10%

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Dhan Layer Input Layer Backlinks Regular Stimuli 90% Noise Stimuli 10%

Dense Homogeneous Associative Network Model

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Lerning between Layers

Dhan Layer Input Layer Backlinks

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Dhan Layer Input Layer Backlinks Regular Stimuli 90% Noise Stimuli 10% Receptive Field

Receptive Field

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Receptive Field

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Model

ri= f w

post∑ [wij wij] f w pre j x j−z tanh ri bare

 ri

bare 

˙ x=1−xiririxi−riri ri

bare=∑ j=1 N

zij f z j g zx j ˙ i=

positive1−x ina−xi− negativeixi−x act

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References

Gros, C., and Kaczor, G. 2008. Learning in cognitive systems with autonomous dynamics. Arxiv preprint arXiv:0804.1306 Gros, C. 2005. Self-sustained thought processes in a dense associative network. In Lecture notes in computer science, volume 3698, 366-379. Springer Gros, C. 2007. Neural Networks with transient state dynamics. New Journal of Physics 9:109 Gros, C. 2008. Complex and Adaptive Dynamical Systems. Springer. Gros, C. 2009. Emotions, diffusive emotional control and the motivational problem for autonomous cognitive systems. In Handbook of Research on Synthetic Emotions and Sociable Robotics: New Applications in Affective Computing and Artificial Intelligence.

  • J. Vallverdu, D. Casacuberta (Eds.), IGI-Global. (in press)