Datteri, Edoardo and Laudisa, Federico (2016) Large-scale simulations of brain mechanisms: beyond the synthetic method. [Preprint]
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Abstract
In recent years, a number of research projects have been proposed whose goal is to build large-scale simulations of brain mechanisms at unprecedented levels of biological accuracy. Here it is argued that the roles these simulations are expected to play in neuroscientific research go beyond the “synthetic method” extensively adopted in Artificial Intelligence and biorobotics. In addition we show that, over and above the common goal of simulating brain mechanisms, these projects pursue various modelling ambitions that can be sharply distinguished from one another, and that correspond to conceptually different interpretations of the notion of “biological accuracy”. They include the ambition (i) to reach extremely deep levels in the mechanistic decomposition hierarchy, (ii) to simulate networks composed of extremely large numbers of neural units, (iii) to build systems able to generate rich behavioural repertoires, (iv) to simulate “complex” neuron models, (v) to implement the “best” theories available on brain structure and function. Some questions will be raised concerning the significance of each of these modelling ambitions with respect to the various roles played by simulations in the study of the brain.
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| Item Type: | Preprint | |||||||||
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| Keywords: | large-scale brain simulations; simulation methodologies in neuroscience; synthetic method; biological accuracy; models in neuroscience; computational neuroscience | |||||||||
| Subjects: | Specific Sciences > Cognitive Science Specific Sciences > Artificial Intelligence General Issues > Experimentation General Issues > Explanation Specific Sciences > Neuroscience General Issues > Technology |
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| Depositing User: | Dr. Edoardo Datteri | |||||||||
| Date Deposited: | 23 Jan 2016 22:25 | |||||||||
| Last Modified: | 23 Jan 2016 22:25 | |||||||||
| Item ID: | 11879 | |||||||||
| Subjects: | Specific Sciences > Cognitive Science Specific Sciences > Artificial Intelligence General Issues > Experimentation General Issues > Explanation Specific Sciences > Neuroscience General Issues > Technology |
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| Date: | 2016 | |||||||||
| URI: | https://philsci-archive.pitt.edu/id/eprint/11879 |
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