Abstract
Studying and modeling the brain as a whole is a real challenge. For such systemic models (in contrast to models of one brain area or aspect), there is a real need for new tools designed to perform complex numerical experiments, beyond usual tools distributed in the computer science and neuroscience communities. Here, we describe an effective solution, freely available on line and already in use, to validate such models of the brain functions. We explain why this is the best choice, as a complement to robotic setup, and what are the general requirements for such a benchmarking platform. In this experimental setup, the brainy-bot implementing the model to study is embedded in a simplified but realistic controlled environment. From visual, tactile and olfactory input, to body, arm and eye motor command, in addition to vital interoceptive cues, complex survival behaviors can be experimented. We also discuss here algorithmic high-level cognitive modules, making the job of building biologically plausible bots easier. The key point is to possibly alternate the use of symbolic representation and of complementary and usual neural coding. As a consequence, algorithmic principles have to be considered at higher abstract level, beyond a given data representation, which is an interesting challenge.
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Notes
- 1.
Such a structure is of common use in computer science: It corresponds to, e.g., a XML logical-structure, or the Json syntax underlying data model.
- 2.
The following scalar values are used: bool for an either true or false Boolean value, percent for a proportion value between 0 and 1, degree for an angle in degree, index stands for a non-negative integer index, count stands for a non-negative integer count value, color stands for a RGB color value, real stands for a unbounded decimal value.
- 3.
Sets are unordered lists without repetition, Sequences are sequential lists, and t-uples map strings, or names, to their corresponding value.
- 4.
Definition available on https://en.wikipedia.org .
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Huge thanks to Nicolas Rougier for precious advice.
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Denoyelle, N., Carrere, M., Pouget, F., Viéville, T., Alexandre, F. (2016). From Biological to Numerical Experiments in Systemic Neuroscience: A Simulation Platform. In: Londral, A., Encarnação, P. (eds) Advances in Neurotechnology, Electronics and Informatics. Biosystems & Biorobotics, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-319-26242-0_1
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