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Highlights from
Random Boolean Network Toolbox

assocNeighbours(node, connect... ASSOCNEIGHBOURS Set the array "input" in the NODE structure-array according to the
assocRules(node,rulesMatrix) ASSOCRULES Set the array "rule" in the NODE structure-array according to the network's
averageConnectivity(node) AVERAGECONNECTIVITY Calculate average network connectivity.
bnKav(n, kavdesired) BNKAV Generate network with N nodes and at average KAV incoming connections per node.
bsn(n, k, optionString) BSN Build and show network.
connectivityDistribution(node... CONNECTIVITYDISTRIBUTION Calculate and display the network's connectivity distribution.
countTransitionsPerNode(tsm) COUNTTRANSITIONSPERNODE Count number of changes of a node through evolution.
displayEvolution(node, k, mod... DISPLAYEVOLUTION Calculate and visualize evolution of NODE over K discrete time steps according to MODE update scheme
displayNodeStats(node,tsm) DISPLAYNODESTATS Visualize node statistics (number of updates / nb of state-transitions).
displayTimeStateMatrix(tsm,va... DISPLAYTIMESTATEMATRIX Visualize Time-State-Matrix.
displayTopology(node, connect... DISPLAYTOPOLOGY Visualize network topology, set xy-components and "ouput" field in node structure-array.
entropy(rulesMatrix) ENTROPY Calculate the entropy of the system. Indicator for the diversity of the rules in the network.
evolveARBN(node, varargin) EVOLVEARBN Develop network gradually K discrete time-steps according to ARBN (Asynchronous
evolveCRBN(node, varargin) EVOLVECRBN Develop network gradually K discrete time-steps according to CRBN (Classical
evolveDARBN(node, varargin) EVOLVEDARBN Develop network gradually K discrete time-steps according to DARBN (Deterministic
evolveDGARBN(node, varargin) EVOLVEDGARBN Develop network gradually K discrete time steps according to DGARBN (Deterministic
evolveGARBN(node, varargin) EVOLVEGARBN Develop network gradually K discrete time steps according to GARBN (Generalized
evolveTopology(node, mode, tS... EVOLVETOPOLOGY Evolve and display topology according to algorithm described by Christof Teuscher and Eduardo Sanchez
findAttractor(varargin) FINDATTRACTOR Return attractor length and states in attractor.
frozenComp(kMax, n, r, step ,... FROZENCOMP Visualize frozen components graph.
getStateVector(node) GETSTATEVECTOR Return the states of the nodes in NODE as row vector.
initConnections(varargin) INITCONNECTIONS Generate N x N adjacent matrix with K incoming connections per node.
initNodes(n, varargin) INITNODES Generate structure-array containing node-state information.
initRules(varargin) INITRULES Generate a 2^k x n or 2^kMax x n matrix containing logic transition
resetNodeStats(node) RESETNODESTATS Reset all variables of the node structure which are involved in statistical evaluation to zero.
saveFigure(figure, varargin) SAVEFIGURE Save a figure to the current directory in two formats (*.fig and *.eps).
saveMatrix(matrix, varargin) SAVEMATRIX Save a matrix to the current directory.
scalingLaw(n, mode, tSteps, r... SCALINGLAW Visualize Scaling Law.
setLUTLines(node, varargin) SETLUTLINES Updates the "lineNumber" field of the NODE structure-array.
setNodeNextState(node) SETNODENEXTSTATE Look up next state for each node and update "nextState" field of the NODE
x(~out); end;
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Random Boolean Network Toolbox

by Christian Schwarzer

02 Apr 2003 (Updated 09 Apr 2003)

Simulation und visualization of Random Boolean Networks.

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Description

MATLAB Toolbox for Simulation and Visualization of Random Boolean Networks (RBN).

- Different update modes (synchronous/asynchronous
deterministic/non-deterministic)
- Statistical Functions
- Topology Evolution

Complete documentation, function reference, tutorials, gallery and more available at:
http://www.activewebs.ch/schwarzer/rbn/index.htm

http://www.teuscher-research.ch/rbntoolbox/

Acknowledgements

The author wishes to acknowledge the following in the creation of this submission:
arrow.m

MATLAB release

MATLAB 6.1 (R12.1)

Tags for This File
Everyone's Tags	epfl, fuzzy logic, neural networks, random boolean network, rbn, toolbox
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Comments and Ratings (1)
26 Apr 2007	hamed haddadi	this is amazing, thank you

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Updates
09 Apr 2003	Documentation and function reference available at http://www.activewebs.ch/schwarzer/rbn/index.htm

Tag Activity for this File
Tag	Applied By	Date/Time
fuzzy logic	Christian Schwarzer	22 Oct 2008 06:59:28
neural networks	Christian Schwarzer	22 Oct 2008 06:59:28
random boolean network	Christian Schwarzer	22 Oct 2008 06:59:28
rbn	Christian Schwarzer	22 Oct 2008 06:59:28
toolbox	Christian Schwarzer	22 Oct 2008 06:59:28
epfl	Christian Schwarzer	22 Oct 2008 06:59:28

Highlights from Random Boolean Network Toolbox

Random Boolean Network Toolbox

Highlights from
Random Boolean Network Toolbox