Modular neural network: Difference between revisions

Revision as of 15:17, 27 May 2018

Latest revision as of 01:56, 23 June 2025

(15 intermediate revisions by 10 users not shown)

Line 1:

A '''modular neural network''' is an [[artificial neural network]] characterized by a series of independent neural networks moderated by some intermediary, such as a cohomological structure of Cohomology Theory. Each independent neural network serves as a module and operates on separate inputs to accomplish some subtask of the task the network hopes to perform.{{sfn|Azam|2000}} The intermediary takes the outputs of each module and processes them to produce the output of the network as a whole. The intermediary only accepts the modules' outputs—it does not respond to, nor otherwise signal, the modules. As well, the modules do not interact with each other.

{{Underlinked|date=December 2012}}

A '''modular neural network''' is an [[artificial neural network]] characterized by a series of independent neural networks moderated by some intermediary. Each independent neural network serves as a module and operates on separate inputs to accomplish some subtask of the task the network hopes to perform.{{sfn|Azam|2000}} The intermediary takes the outputs of each module and processes them to produce the output of the network as a whole. The intermediary only accepts the modules' outputs—it does not respond to, nor otherwise signal, the modules. As well, the modules do not interact with each other.

==Biological basis==

As [[artificial neural network]] research progresses, it is appropriate that artificial neural networks continue to draw on their biological inspiration and emulate the segmentation and modularization found in the brain. The brain, for example, divides the complex task of visual perception into many subtasks.{{sfn|Happel|Murre|1994}} Within a part of the [[brain]], called the [[thalamus]], lies the [[lateral geniculate nucleus]] (LGN), which is divided into layers that separately processprocesses color and contrast: both major components of [[Visual perception|vision]].{{sfn|Hubel|Livingstone|1990}} After the LGN processes each component in parallel, it passes the result to another region to compile the results.

Some tasks that the brain handles, like vision, employ a hierarchy of sub-networks. However, it is not clear whether some intermediary ties these separate processes together. Rather, as the tasks grow more abstract, the modules communicate with each other, unlike the modular neural network model.

Line 15 ⟶ 13:

===Efficiency===

The possible [[neuron]] (node) connections increase exponentiallyquadratically as nodes are added to a network. Computation time depends on the number of nodes and their connections, any increase has drastic consequences for processing time. Assigning specific subtasks to individual modules reduce the number of necessary connections.

===Training===

Line 28 ⟶ 26:

== References ==

*{{cite web|last=Azam |first=Farooq |title=Biologically Inspired Modular Neural Networks. PhD Dissertation |publisher=Virginia Tech |year=2000 |hdl=10919/27998 |url=http://scholarhdl.libhandle.vt.edunet/theses10919/available/etd-06092000-12150028/unrestricted/etd.pdf |ref=harv27998}}

*{{cite journal | last1 = Happel | first1 = Bart | last2 = Murre | first2 = Jacob | year = 1994 | title = The Design and Evolution of Modular Neural Network Architectures | url = http://citeseer.comp.nus.edu.sg/cache/papers/cs/3480/ftp:zSzzSzftp.mrc-apu.cam.ac.ukzSzpubzSznnzSzmurrezSznnga1.pdf/the-design-and-evolution.pdf | format = PDF | journal = Neural Networks | volume = 7 | issue = 6–7 | pages = 985–1004 | doi = 10.1016/s0893-6080(05)80155-8 }}{{Dead link|refdate=harvApril 2020 |bot=InternetArchiveBot |fix-attempted=yes }}

*{{cite journal | last1 = Hubel | first1 = DH | last2 = Livingstone | first2 = MS | year = 1990 | title = Color and contrast sensitivity in the lateral geniculate body and primary visual cortex of the macaque monkey | url = http://www.jneurosci.org/cgi/content/abstract/10/7/2223 | journal = Journal of Neuroscience | volume = 10 | issue = 7| pages = 2223–2237|ref doi =harv 10.1523/JNEUROSCI.10-07-02223.1990 | pmid = 2198331 | pmc = 6570379 | doi-access = free }}

* {{cite journal | last1 = Tahmasebi | first1 = P. | last2 = Hezarkhani | first2 = A. | year = 2011 | title = Application of a Modular Feedforward for Grade Estimation | url = | journal = Natural Resources Research | volume = 20 | issue = 1| pages = 25–32 | doi = 10.1007/s11053-011-9135-3 | s2cid = 45997840 }}

* {{Cite journal|lastlast1=Clune|firstfirst1=Jeff|last2=Mouret|first2=Jean-Baptiste|last3=Lipson|first3=Hod|date=2013-01-30|title=The evolutionary origins of modularity|journal=Proceedings of the Royal Society B: Biological Sciences|volume=280|issue=1755|pages=20122863–2012286320122863|doi=10.1098/rspb.2012.2863|pmid=23363632|pmc=3574393|issn=0962-8452|arxiv=1207.2743}}

* {{cite journal|last1 = Tahmasebi|first1 = Pejman|last2 = Hezarkhani|first2 = Ardeshir|date=|year = 2012|title = A fast and independent architecture of artificial neural network for permeability prediction|url =|journal = Journal of Petroleum Science and Engineering|volume = 86|issue =|pages = 118–126|doi=10.1016/j.petrol.2012.03.019|via bibcode=2012JPSE...86..118T }}

[[Category:Computational neuroscience]]

[[Category:ArtificialNeural neuralnetwork networksarchitectures]]

[[Category:Modularity|Neural network]]