Bio-inspired Memory Generation by Recurrent Neural Networks

  • Manuel G. Bedia
  • Juan M. Corchado
  • Luis F. Castillo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4507)


The knowledge about higher brain centres in insects and how they affect the insect’s behaviour has increased significantly in recent years by experimental investigations. A large body of evidence suggests that higher brain centres of insects are important for learning, short-term and long-term memory and play an important role for context generalisation. In this paper, we focus on artificial recurrent neural networks that model non-linear systems, in particular, Lotka-Volterra systems. After studying the typical behavior and processes that emerge in appropiate Lotka-Volterra systems, we analyze the relationship between sequential memory encoding processes and the higher brain centres in insects in order to propose a way to develop a general ’insect-brain’ control architecture to be implemented on simple robots.


Recurrent Neural Network Mushroom Body Control Architecture Sequential Memory Interior Equilibrium Point 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Manuel G. Bedia
    • 1
  • Juan M. Corchado
    • 2
  • Luis F. Castillo
    • 3
  1. 1.Dpto. de Informática, Universidad Carlos III, Av. de la Universidad, s/n, 28911 - MadridSpain
  2. 2.Dpto. Informática y Automática, Universidad de Salamanca. Pl. de la Merced, s/n, 37008 - SalamancaSpain
  3. 3.Dpto. de Ciencias Computacionales, Universidad Autónoma de Manizales, ManizalesColombia

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