Abstract
Brain function remains one of the most elusive and fascinating phenomena challenging modem science (Churchland, 1986). Although a lot is already known about the neuron and its functional characteristics, when we address the information-processing capabilities of a neural assembly, called here the mesoscopic description (Freeman, 1975), more often than not we are unable to quantify function and abstract the computation. The reasons can be found in the distributed nature of the system architecture, the lack of appropriate tools and metaphors to describe the communication among neurons (the neural code) (Rao et al, 2002), and also very often due to the absence of a detailed knowledge of the function being implemented (Nicolelis, 2001). Hence, conducting studies in computational neuroscience requires a carefully planned methodology and experimental design.
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Xu, D. et al. (2005). Computational Neural Networks. In: He, B. (eds) Neural Engineering. Bioelectric Engineering. Springer, Boston, MA. https://doi.org/10.1007/0-306-48610-5_9
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DOI: https://doi.org/10.1007/0-306-48610-5_9
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