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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
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 .
References
Taouali, W., Viéville, T., Rougier, N.P., Alexandre, F.: No clock to rule them all. J. Physiol. Paris 105, 83–90 (2011)
Uithol, S., van Rooij, I., Bekkering, H., Haselager, P.: Hierarchies in action and motor control. J. Cogn. Neurosci. 24, 1077–1086 (2012)
Hyvärinen, A.: Natural image statistics a probabilistic approach to early computational vision. Hardcover (2009)
Teftef, E., Escobar, M.J., Astudillo, A., Carvajal, C., Cessac, B., Palacios, A., Viéville, T., Alexandre, F.: Modeling non-standard retinal in/out function using computer vision variational methods. Rapport de recherche RR-8217, INRIA (2013)
Friston, K.: A free energy principle for biological systems. Entropy 14, 2100–2121 (2012)
Viéville, T., Crahay, S.: Using an hebbian learning rule for multi-class SVM classifiers. J. Comput. Neurosci. (2004)
Viéville, T., Vadot, C.: An improved biologically plausible trajectory generator. Technical Report 4539-2, INRIA (2006)
Connolly, C.I., Grupen, R.A.: The applications of harmonic functions to robotic. J. Robot. Syst. 10, 931–946 (1993)
Todorov, E.: Optimality principles in sensorimotor control. Nat. Neurosci. 7 (2004)
Cofer, D., Cymbalyuk, G., Reid, J., Zhu, Y., Heitler, W.J., Edwards, D.H.: AnimatLab: a 3D graphics environment for neuromechanical simulations. J. Neurosci. Methods 187, 280–288 (2010)
Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J.M., Diesmann, M., Morrison, A., Goodman, P.H., Harris, F.C., Zirpe, M., Natschläger, T., Pecevski, D., Ermentrout, B., Djurfeldt, M., Lansner, A., Rochel, O., Vieville, T., Muller, E., Davison, A.P., El Boustani, S., Destexhe, A.: Simulation of networks of spiking neurons: a review of tools and strategies. J. Comput. Neurosci. 23, 349–398 (2007)
Davison, A.P., Brüderle, D., Eppler, J., Kremkow, J., Muller, E., Pecevski, D., Perrinet, L., Yger, P.: PyNN: A Common interface for neuronal network simulators. Frontiers Neuroinform. 2 (2008)
Chemla, S., Chavane, F., Vieville, T., Kornprobst, P.: Biophysical cortical column model for optical signal analysis. BMC Neurosci. 8, P140 (2007)
Herry, C., Ciocchi, S., Senn, V., Demmou, L., Muller, C., Luthi, A.: Switching on and off fear by distinct neuronal circuits. Nature 454, 600–606 (2008)
LeDoux, J.: The amygdala. Curr. Biol. 17, R868–R874 (2007)
Eichenbaum, H., Sauvage, M., Fortin, N., Komorowski, R., Lipton, P.: Towards a functional organization of episodic memory in the medial temporal lobe. Neurosci. Biobehav. Rev. 36, 1597–1608 (2012)
Rolls, E.T.: A computational theory of episodic memory formation in the hippocampus. Behav. Brain Res. 215, 180–196 (2010)
Gorojosky, R., Alexandre, F.: Models of Hippocampus for pavlovian learning. Rapport de recherche RR-8377, INRIA (2013)
Beati, T., Carrere, M., Alexandre, F.: Which reinforcing signals in autonomous systems? In: Third International Symposium on Biology of Decision Making, Paris, France (2013)
Carrere, M., Alexandre, F.: Émergence de catégories par interaction entre systèmes d’apprentissage. In: Preux, P., Tommasi, M. (eds.) Conférence Francophone sur l’Apprentissage Automatique (CAP). Lille, France (2013)
Denoyelle, N., Pouget, F., Viéville, T., Alexandre, F.: VirtualEnaction: A platform for systemic neuroscience simulation. In: International Congress on Neurotechnology, Electronics and Informatics, Rome, Italy (2014)
Acknowledgments
Huge thanks to Nicolas Rougier for precious advice.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-26242-0_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26240-6
Online ISBN: 978-3-319-26242-0
eBook Packages: EngineeringEngineering (R0)