ELFT HÍRADÓ

[StatFizSzeminar <statfiz AT glu.elte.hu>]

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                     ELTE TTK  Elméleti Fizikai Tanszék
                      STATISZTIKUS FIZIKAI SZEMINÁRIUM

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                         Új helyszin: 2.54 sz. terem
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                        2009. június 17, szerda 11 óra

                               Toroczkai Zoltán

                 Physics Department, Notre Dame University, USA 

           "Emergence of Functional Modularity in Multitasking Networks"

Abstract:
While many deterministic and stochastic processes have been proposed to 
produce heterogeneous graphs mimicking real-world networks, only a handful of 
studies attempt to connect structure and dynamics with the function(s) 
performed by the network. In this talk I will present an approach built on 
the premise that structure, dynamics, and their observed heterogeneity, are 
implementations of various functions and their compositions.  While proposals 
have been made for the evolutionary emergence of modularity, it is far from 
clear that adaptation on evolutionary timescales is the sole mechanism 
leading to functional specialization. We show that non-evolutionary learning 
can also lead to the formation of functionally specialized modules in a 
system exposed to multiple environmental constraints. A natural example 
suggesting that this is possible is the cerebral cortex, where there are 
clearly delineated functional areas in spite of the largely uniform 
anatomical construction of the 
 cortical tissue. However, as numerous experiments show, when damaged, 
regions specialized for a certain function can be retrained to perform 
functions normally attributed to other regions.
We use the paradigm of neural networks to represent a multitasking system, 
and use several non-evolutionary learning algorithms as mechanisms for 
phenotypic adaptation. We show that for a network learning to perform 
multiple tasks, the degree of independence between the tasks dictates the 
degree of functional specialization emerging in the network. To uncover the 
functional modules, we introduce a method of node knockouts that explicitly 
rates the contribution of each node to different tasks (differential 
robustness). Through a concrete example we also demonstrate the potential 
inability of purely topology-based clustering methods to detect functional 
modules.


            1117 Budapest, Pázmány P. sétány 1A, Északi tömb
                                 2.54 terem

                                   honlap:
                       http://glu.elte.hu/~statfiz

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