We present a mathematical model of interacting neuron-like units that we call Input Feedback Networks (IFN). Our model is motivated by a new approach to biological neural networks, which contrasts with current approaches (e.g. Layered Neural Networks, Perceptron etc.). Classification reasoning in IFN are accomplished by an iterative algorithm, and learning changes only structure. Feature relevance is determined during classification. Thus it emphasizes network structure over edge weights. IFNs are more flexible than previous approaches. In particular, integration of a new node can affect the outcome of existing nodes without modifying their prior structure. IFN can produce informative responses to partial inputs or when the networks are extended to other tasks. It also enables recognition of complex entities (e.g. images) from parts. This new model is promising for future contributions to integrated human-level intelligent applications due to its flexibility, dynamics and structural similarity to natural neuronal networks.
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