Brain Architecture Management System

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Brain Architecture Management System
Abbreviations: BAMS
Category: Brain Architecture Management System

Brain connectivity database

Papers: DOAJ Google Scholar PubMed
Ontologies: MeSH NeuroLex Wikidata Wikipedia
Other: Google Twitter WolframAlpha

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The Brain Architecture Management System (BAMS) is an Web-based resource with information about neural circuitry, among other functions a brain connectivity database and an ontology of brain regions.

BAMS provides several ways of searching for information within the database. The URL for searching on, e.g., the "cerebellum" is:

The brain connectivity database part of BAMS is based on four different nomenclatures: Swanson-1998 (see e.g.[1]), Paxinos/Watson-1998, Swanson-2004 and Felleman & van Essen.

[edit] Papers

  1. Mihail Bota, Hong-Wei Dong, Larry W. Swanson (2005). "Brain architecture management system". Neuroinformatics 3(1): 15-47. doi: 10.1385/NI:3:1:015.
  2. Mihail Bota, Larry W. Swanson (2008). "BAMS neuroanatomical ontology: design and implementation". Frontiers in Neuroinformatics 2: 2. doi: 10.3389/neuro.11.002.2008.
  3. Mihail Bota, Larry W. Swanson (2010). "Collating and curating neuroanatomical nomenclatures: principles and use of the Brain Architecture Knowledge Management System (BAMS)". Frontiers in Neuroinformatics 4: 3. doi: 10.3389/fninf.2010.00003.
  4. Mihail Bota, H.-W. Dong, Larry W. Swanson (2003). "From gene networks to brain networks". Nature Neuroscience 6(8): 795-799. doi: 10.1038/nn1096. PMID: 12886225.
  5. Mihail Bota, Larry W. Swanson (2006). "A new module for on-line manipulation and display of molecular information in the brain architecture management system". Neuroinformatics 4(4): 275-298.
  6. Mihail Bota, Larry W. Swanson (2007). "Online workbenches for neural network connections". The Journal of Comparative Neurology 500(5): 807-814. doi: 10.1002/cne.21209.

[edit] Applications

  1. Large-scale extraction of brain connectivity from the neuroscientific literature

[edit] See also

  1. Knowledge Engineering from Experimental Design
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