Holistically convergent modular networks: a biological principle for recurrent network architecture
© Cook et al; licensee BioMed Central Ltd. 2010
Published: 20 July 2010
The general principle of combining information processing modules in a feed-forward manner, to sequentially process input until the desired output has been generated, is well understood and widely used. In contrast, biological neural circuits often consist of recurrently connected modules, with each module’s units encoding a different aspect of the modeled world [1, 3]. No engineering principles for constructing circuits in this biological pattern are available.
Since different consistency constraints are located in different parts of the network, information must propagate from the input to the ends of the network and back repeatedly as the network converges to a globally consistent state, a process we refer to as holistic convergence. Because the formula involving the input E leaves F and G underconstrained, one cannot directly infer R and I from it. Only by combining local constraints from all parts of the network can a globally coherent interpretation be obtained.
This work demonstrates how a biologically inspired recurrent architecture of modules and local relationships can be designed to interpret noisy or ambiguous data even when there is not a clear feed-forward method to generate the desired interpretation. This provides a new paradigm for the design of information processing systems, based on principles found in biological systems.
The authors would like to thank ETH Research Grant ETH-23 08-1 and EU Project Grant FET-IP-216593.
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