Recurrent fractal neural networks: A strategy for the exchange of local and global information processing in the brain

Erhard Bieberich

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

The regulation of biological networks relies significantly on convergent feedback signaling loops that render a global output locally accessible. Ideally, the recurrent connectivity within these systems is self-organized by a time-dependent phase-locking mechanism. This study analyzes recurrent fractal neural networks (RFNNs), which utilize a self-similar or fractal branching structure of dendrites and downstream networks for phase-locking of reciprocal feedback loops: output from outer branch nodes of the network tree enters inner branch nodes of the dendritic tree in single neurons. This structural organization enables RFNNs to amplify re-entrant input by over-the-threshold signal summation from feedback loops with equivalent signal traveling times. The columnar organization of pyramidal neurons in the neocortical layers V and III is discussed as the structural substrate for this network architecture. RFNNs self-organize spike trains and render the entire neural network output accessible to the dendritic tree of each neuron within this network. As the result of a contraction mapping operation, the local dendritic input pattern contains a downscaled version of the network output coding structure. RFNNs perform robust, fractal data compression, thus coping with a limited number of feedback loops for signal transport in convergent neural networks. This property is discussed as a significant step toward the solution of a fundamental problem in neuroscience: how is neuronal computation in separate neurons and remote brain areas unified as an instance of experience in consciousness? RFNNs are promising candidates for engaging neural networks into a coherent activity and provide a strategy for the exchange of global and local information processing in the human brain, thereby ensuring the completeness of a transformation from neuronal computation into conscious experience.

Original languageEnglish (US)
Pages (from-to)145-164
Number of pages20
JournalBioSystems
Volume66
Issue number3
DOIs
StatePublished - Aug 1 2002

Fingerprint

Fractals
Information Processing
Automatic Data Processing
Fractal
Brain
Neural Networks
Neural networks
Neurons
Neuron
Feedback Loop
Feedback
Phase Locking
Output
Data Compression
Branch
Mental Processes
Pyramidal Cells
Tree Networks
Contraction Mapping
Neurosciences

Keywords

  • Brain
  • Consciousness
  • Fractal
  • Mind
  • Networks
  • Neuron

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Applied Mathematics

Cite this

Recurrent fractal neural networks : A strategy for the exchange of local and global information processing in the brain. / Bieberich, Erhard.

In: BioSystems, Vol. 66, No. 3, 01.08.2002, p. 145-164.

Research output: Contribution to journalArticle

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