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  • From: StatFizSzeminar <statfiz AT>
  • To: fizinfo AT
  • Subject: [Fizinfo] Stat Fiz Szeminarium
  • Date: Sat, 13 Jun 2009 12:04:29 +0200
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ELTE TTK Elméleti Fizikai Tanszék

Új helyszin: 2.54 sz. terem

2009. június 17, szerda 11 óra

Toroczkai Zoltán

Physics Department, Notre Dame University, USA

"Emergence of Functional Modularity in Multitasking Networks"

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

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


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