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Full-Text Articles in Physical Sciences and Mathematics
On The Effect Of Criticality And Topology On Learning In Random Boolean Networks, Alireza Goudarzi
On The Effect Of Criticality And Topology On Learning In Random Boolean Networks, Alireza Goudarzi
Systems Science Friday Noon Seminar Series
Random Boolean networks (RBN) are discrete dynamical systems composed of N automata with a binary state, each of which interacts with other automata in the network. RBNs were originally introduced as simplified models of gene regulation. In this presentation, I will present recent work done conjointly with Natali Gulbahce (UCSF), Thimo Rohlf (MPI, CNRS), and Christof Teuscher (PSU). We extend the study of learning in feedforward Boolean networks to random Boolean networks (RBNs) and systematically explore the relationship between the learning capability, the network topology, the system size N, the training sample T, and the complexity of the computational task. …
Random Automata Networks: Why Playing Dice Is Not A Vice, Christof Teuscher
Random Automata Networks: Why Playing Dice Is Not A Vice, Christof Teuscher
Systems Science Friday Noon Seminar Series
Random automata networks consist of a set of simple compute nodes interacting with each other. In this generic model, one or multiple model parameters, such as the the node interactions and/or the compute functions, are chosen at random. Random Boolean Networks (RBNs) are a particular case of discrete dynamical automata networks where both time and states are discrete. While traditional RBNs are generally credited to Stuart Kauffman (1969), who introduced them as simplified models of gene regulation, Alan Turing proposed unorganized machines as early as 1948. In this talk I will start with Alan Turing's early work on unorganized machines, …