3 pages.-- PMID: 18982097 [PubMed].-- PMCID: PMC2570081.The principal goal in neuroanatomy is to define the detailed structural design of the nervous system. This challenge is one of the first steps towards understanding how neural circuits contribute to the functional organization of the nervous system, both in health and disease. The main difficulties involve unraveling the extraordinary complexity of the nervous system and to define how information flows through this finely organized synaptic network. Over the years, neuroanatomy has evolved considerably thanks to the use of classical techniques and the introduction of new procedures (DeFelipe, 2002), including: axonal transport methods to trace connections; electron microscopy to better understand synaptic connectivity; immunocytochemistry to map protein expression and the distribution of particular types of neurons; in situ hybridization to map gene expression; intracellular labeling of physiologically characterized neurons to visualize their morphology; and other powerful techniques to examine the organization of the nervous system. Among the most appealing new tools are the molecular/genetic approaches to activate, inactivate and label active neurons and synapses (Marek and Davis, 2003; Dymecki and Kim, 2007; Kuhlman and Huang, 2008). In turn, as more detailed circuit diagrams become available, the more we will learn about the role of each element in the circuit through computer simulations, and this will enable the response properties of individual neurons to be correlated with the activity in cortical circuits, an attractive bridge between anatomy, physiology and computation (Segev and London, 2000; Markram, 2006). Additionally, the magnitude of morphological, molecular and physiological data collectively generated is now so colossal that even at the cellular level it is necessary to develop neuronal classification systems, in order to help the scientific community in each field to establish and maintain an effective communication and interchange of information (Ascoli et al., 2008). Thus, we have to learn not only how to manage the growing amount of multidisciplinary data but also, how to communicate this knowledge.Peer reviewe
