|InterJournal Complex Systems, 2086
|Manuscript Number: |
Submission Date: 20080226
|Modularity and Self-Organized Functional Architectures in the Brain|
Modularity and Self-Organized Functional Architectures in the Brain Laxmi Iyer and Ali A. Minai (University of Cincinnati) Simona Doboli and Vincent R. Brown (Hofstra University) Recent work in neuroscience and cognitive psychology is gradually leading to a consensus about the nature of cognition and its biological basis. It is generally believed that cognition involves the self-organization of coherent dynamic functional networks across several brain regions in response to incoming stimulus and internal modulation, These context-dependent networks arise continually from the spatiotemporally multi-scale structural substrate of the brain configured by evolution, development and previous experience, persisting for 100-200 ms and generating responses such as imagery, recall and motor action. Three major requirements for such a system are: 1) It should be capable of supporting a large number of diverse functional networks; 2) The functional networks should be robust against irrelevant variations; and 3) The functional networks should be responsive to relevant variations. In the current paper, we show that a system of interacting modular attractor networks can instantiate this functionality using a selective mechanism for assembling functional networks from the modular substrate. We use the approach to develop a model of idea-generation in the brain. Ideas are modeled as combinations of concepts organized in a recurrent network that reflects previous associations between them. The dynamics of this network, resulting in the transient co-activation of concept groups, is seen as a search through the space of ideas, and attractor dynamics is used to “shape” this search. The process is required to encompass both rapid retrieval of old ideas in familiar contexts and efficient search for novel ones in unfamiliar situations or during brainstorming. We show that the inclusion of an adaptive modulatory mechanism allows the network to balance the competing requirements of exploiting previous learning and exploring new possibilities as needed in different contexts.
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