Random Boolean Network: Difference between revisions
No edit summary |
|||
| Line 13: | Line 13: | ||
model by Stuart Kauffman, see his book "The Origins of Order". | model by Stuart Kauffman, see his book "The Origins of Order". | ||
== NK Network == | |||
An NK-Boolean network is defined as a network of N nodes with connectivity K, | An NK-Boolean network is defined as a network of N nodes with connectivity K, | ||
| Line 23: | Line 23: | ||
* [http://www-users.cs.york.ac.uk/susan/cyc/n/nk.htm Applet for NK networks] | * [http://www-users.cs.york.ac.uk/susan/cyc/n/nk.htm Applet for NK networks] | ||
== Links == | == Links == | ||
Revision as of 16:45, 11 February 2011
Random Boolean Networks are networks of boolean nodes that can be in one of two possible states, 0 or 1, and whose evolution from one time point to another is governed by simple boolean transition rules. The nodes change their states according to this boolean transition rules that depend on their current states and those of their neighbors. RBNs are closely related to Cellular Automata (CA) and are used to study complex systems. Both are usually based on local boolean transition functions. In RBNs we have nodes instead of Cells in CA, and connections to remote neighbors instead of a local neighborhood grid as in CA. RBNs and NK Networks have been proposed as a biological model by Stuart Kauffman, see his book "The Origins of Order".
NK Network
An NK-Boolean network is defined as a network of N nodes with connectivity K, where K refers to the maximum number of nodes that regulate some othernode, i.e. each of the N nodes has K inputs and one output. It can be considered as a network of N light bulbs. At every timestep, each bulb changes the state: the light bulbs can only be on or off, and each of the bulbs influences K other bulbs in the network.
Links
Tutorials
- RBN Tutorial from Kai Willadsen
- RBN Tutorial from Carlos Gershenson
arXiv papers
- Introduction to Random Boolean Networks from Carlos Gershenson
- Boolean Dynamics with Random Couplings from Leo Kadanoff et al.
Examples
The Finite State Machine (FSM) for the whole boolean network reveals the attractor structures and the basins of attraction. The attractor - if one exists - is a fixed point or a discrete limit cycle. The limit cycle is of course shorter than the total number of states, which is 2^N for N nodes (2^3=8 for 3 nodes).
