H. Akaike, A new look at the statistical model identification, IEEE Transactions on Automatic Control, vol.19, issue.6, pp.716-723, 1974.
DOI : 10.1109/TAC.1974.1100705

M. Bastian, S. Heymann, and M. Jacomy, Gephi: an open source software for exploring and manipulating networks, International AAAI conference on weblogs and social media, 2009.

C. Bernon, D. Capera, and J. Mano, Engineering Self-modeling Systems: Application to Biology, Lecture notes in computer science, vol.24, issue.2, pp.248-263, 2009.
DOI : 10.1007/s10489-006-6937-9

E. Bonabeau, M. Dorigo, and G. Theraulaz, Swarm intelligence: from natural to artificial systems. Santa Fe Institute Studies in the Sciences of Complexity, 1999.

N. Brax, E. Andonoff, and M. Gleizes, A self-adaptive multi-agent system for abnormal behavior detection in maritime surveillance (regular paper), KES international conference (KES-AMSTA), Dubrovnick, pp.174-185, 2012.

R. S. Brinkworth and K. S. Türker, A method for quantifying reflex responses from intra-muscular and surface electromyogram, Journal of Neuroscience Methods, vol.122, issue.2, pp.179-193, 2003.
DOI : 10.1016/S0165-0270(02)00321-7

M. Buibas and G. A. Silva, A Framework for Simulating and Estimating the State and Functional Topology of Complex Dynamic Geometric Networks, Neural Computation, vol.11, issue.5918, pp.183-214, 2011.
DOI : 10.1016/j.neuron.2008.04.002

W. Calvin and P. Schwindt, Steps in production of motoneuron spikes during rhytmic firing, Journal of Neurophysiology, vol.35, pp.297-310, 1972.

V. Camps, M. P. Gleizes, and P. Glize, A self-organization process based on cooperation theory for adaptive artificial systems, 1st international conference on philosophy and computer science: processes of evolution in real and virtual systems, 1998.

C. Capaday, The special nature of human walking and its neural control, Trends in Neurosciences, vol.25, issue.7, pp.370-376, 2002.
DOI : 10.1016/S0166-2236(02)02173-2

D. Capera, J. George, M. Gleizes, and P. Glize, The AMAS theory for complex problem solving based on self-organizing cooperative agents, WET ICE 2003. Proceedings. Twelfth IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, 2003., pp.383-388, 2003.
DOI : 10.1109/ENABL.2003.1231441

D. Capera, M. Gleizes, and P. Glize, MECHANISM TYPE SYNTHESIS BASED ON SELF-ASSEMBLING AGENTS, Applied Artificial Intelligence, vol.42, issue.9-10, pp.9-10, 2004.
DOI : 10.1080/08839510490509090

K. C. Chatzidimitriou and P. A. Mitkas, Adaptive reservoir computing through evolution and learning, Neurocomputing, vol.103, pp.198-209, 2013.
DOI : 10.1016/j.neucom.2012.09.022

D. Colquhoun and A. G. Hawkes, Stochastic Properties of Ion Channel Openings and Bursts in a Membrane Patch that Contains Two Channels: Evidence Concerning the Number of Channels Present when a Record Containing Only Single Openings is Observed, Proceedings of the Royal Society of London. Series B, Biological Sciences, pp.240-453, 1299.
DOI : 10.1098/rspb.1990.0048

S. Combettes, T. Sontheimer, S. Rougemaille, and P. Glize, Weight optimization of aircraft harnesses (short paper), International conference on practical applications of agents and multiagent systems (PAAMS), pp.229-232, 2012.

O. David, S. J. Kiebel, L. M. Harrison, J. Mattout, J. M. Kilner et al., Dynamic causal modeling of evoked responses in EEG and MEG, NeuroImage, vol.30, issue.4, pp.1255-1272, 2006.
DOI : 10.1016/j.neuroimage.2005.10.045
URL : https://hal.archives-ouvertes.fr/inserm-00388967

P. Dayan and L. Abbott, Theoretical neuroscience: computational and mathematical modeling of neural systems. Computational Neuroscience, 2005.

J. De-haan, How emergence arises, Ecological Complexity, vol.3, issue.4, pp.293-301, 2006.
DOI : 10.1016/j.ecocom.2007.02.003

S. Eldawlatly, Y. Zhou, R. Jin, and K. G. Oweiss, On the Use of Dynamic Bayesian Networks in Reconstructing Functional Neuronal Networks from Spike Train Ensembles, Neural Computation, vol.541, issue.1, pp.158-189, 2010.
DOI : 10.1162/089976605774320584

P. Ellaway, Cumulative sum technique and its application to the analysis of peristimulus time histograms, Electroencephalography and Clinical Neurophysiology, vol.45, issue.2, pp.302-304, 1978.
DOI : 10.1016/0013-4694(78)90017-2

S. E. Fahlman and C. Lebiere, Advances in neural information processing systems 2. chapter the cascade-correlation learning architecture, pp.524-532, 1990.

T. Fellin, Communication between neurons and astrocytes: relevance to the modulation of synaptic and network activity, Journal of Neurochemistry, vol.6, issue.3, pp.533-544, 2009.
DOI : 10.1111/j.1471-4159.2008.05830.x

D. Floreano, P. Dürr, and C. Mattiussi, Neuroevolution: from architectures to learning, Evolutionary Intelligence, vol.87, issue.9, pp.47-62, 2008.
DOI : 10.1007/s12065-007-0002-4

K. Friston, The free-energy principle: a rough guide to the brain?, Trends in Cognitive Sciences, vol.13, issue.7, pp.293-301, 2009.
DOI : 10.1016/j.tics.2009.04.005

K. J. Friston, Functional and Effective Connectivity: A Review, Brain Connectivity, vol.1, issue.1, pp.13-36, 2011.
DOI : 10.1089/brain.2011.0008

K. Friston, L. Harrison, and W. Penny, Dynamic causal modelling, NeuroImage, vol.19, issue.4, pp.1273-1302, 2003.
DOI : 10.1016/S1053-8119(03)00202-7
URL : https://hal.archives-ouvertes.fr/inserm-00388972

B. Fritzke, A growing neural gas network learns topologies, NIPS, pp.625-632, 1994.

M. I. Garrido, J. M. Kilner, S. J. Kiebel, K. E. Stephan, and K. J. Friston, Dynamic causal modelling of evoked potentials: A reproducibility study, NeuroImage, vol.36, issue.3, pp.36-571, 2007.
DOI : 10.1016/j.neuroimage.2007.03.014

J. P. Georgé, B. Edmonds, and P. Glize, Making self-organizing adaptive multi-agent systems work, pp.16-321, 2004.

J. Georgé, M. Gleizes, and V. Camps, Cooperation, Self-organizing software: from natural to artificial adaptation. Natural computing series, pp.347-377, 2011.
DOI : 10.1007/978-3-642-17348-6_9

W. Gerstner, H. Sprekeler, and G. Deco, Theory and Simulation in Neuroscience, Science, vol.338, issue.6103, pp.60-65, 2012.
DOI : 10.1126/science.1227356

J. Goldstein, Emergence as a construct: history and issues. Emergence : A Journal of Complexity Issues in Organizations and Management. The New England Complex Systems Institute, pp.49-72, 1999.

G. Grande and E. Cafarelli, Ia Afferent input alters the recruitment thresholds and firing rates of single human motor units, Experimental Brain Research, vol.170, issue.4, pp.449-457, 2003.
DOI : 10.1007/s002210050933

V. Guivarch, V. Camps, and A. Péninou, Amadeus: an adaptive multi-agent system to learn a user's recurring actions in ambient systems, Advances in Distributed Computing and Artificial Intelligence Journal, Special Issue, vol.3, 2012.

¨. O. Gürcan, O. Dikenelli, and K. S. Türker, Agent-based exploration of wiring of biological neural networks: position paper Understanding wiring of human cns using agent-based modeling and simulation, 20th european meeting on cybernetics and systems research, pp.509-514, 2010.

¨. O. Gürcan, O. Dikenelli, C. Bernon, . Szczecin, ¨. O. Gürcan et al., Towards a generic testing framework for agent-based simulation models In 5th international workshop on multi-agent systems and simulation (MAS&S'11) (pp Simulating human single motor units using selforganizing agents, Self-adaptive and self-organizing systems (SASO), 2012 IEEE sixth international conference on, pp.635-642, 2011.

¨. O. Gürcan, O. Dikenelli, and C. Bernon, A generic testing framework for agent-based simulation models, Journal of Simulation (JOS), pp.1-19, 2013.

A. Gydikov, N. Tankov, L. Gerilovsky, and N. Radicheva, Motor unit activity upon polysynaptic reflex in man, Agressologie, vol.18, issue.2, pp.103-108, 1977.

E. Hay, S. Hill, F. Schürmann, H. Markram, and I. Segev, Models of Neocortical Layer 5b Pyramidal Cells Capturing a??Wide Range of Dendritic and Perisomatic Active Properties, PLoS Computational Biology, vol.273, issue.3, p.1002107, 2011.
DOI : 10.1371/journal.pcbi.1002107.s010

F. Heylighen, The science of self-organization and adaptivity . In Knowledge management, organizational intelligence and learning, and complexity, in: the encyclopedia of life support systems, EOLSS, pp.253-280, 1999.

A. L. Hodgkin and A. F. Huxley, A quantitative description of membrane current and its application to conduction and excitation in nerve, The Journal of Physiology, vol.117, issue.4, pp.500-544, 1952.
DOI : 10.1113/jphysiol.1952.sp004764

R. Iansek and S. Redman, The amplitude, time course and charge of unitary excitatory post-synaptic potentials evoked in spinal motoneurone dendrites, The Journal of Physiology, vol.234, issue.3, pp.665-688, 1973.
DOI : 10.1113/jphysiol.1973.sp010366

E. M. Izhikevich and G. M. Edelman, Large-scale model of mammalian thalamocortical systems, Proceedings of the National Academy of Sciences, vol.105, issue.9, pp.3593-3598, 2008.
DOI : 10.1073/pnas.0712231105

J. R. Jenner and J. A. Stephens, Cutaneous reflex responses and their central nervous pathways studied in man, The Journal of Physiology, vol.333, issue.1, pp.405-419, 1982.
DOI : 10.1113/jphysiol.1982.sp014461

E. Kaddoum and J. Georgé, Collective self-tuning for complex product design (short paper), IEEE international conference on self-adaptive and self-organizing systems (SASO), p.page (electronic medium, 2012.

E. R. Kandel, J. Schwartz, and T. M. Jessell, Principles of neural science, 2000.

S. J. Kiebel, O. David, and K. J. Friston, Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization, NeuroImage, vol.30, issue.4, pp.1273-1284, 2006.
DOI : 10.1016/j.neuroimage.2005.12.055
URL : https://hal.archives-ouvertes.fr/inserm-00388972

C. Koch and I. Segev, Methods in neuronal modeling: from synapses to networks, Computational Neuroscience Series, 1989.

A. Koestler, The ghost in the machine, 1967.

B. Kuraszkiewicz, D. Mlozniak, and M. Piotrkiewicz, Thresholdcrossing model of human motoneuron, Information technologies in biomedicine. lecture notes in computer science, pp.209-218, 2012.

J. Lacouture, I. Rodriguez, J. Arcangeli, C. Chassot, T. Desprats et al., Mission-Aware Adaptive Communication for Collaborative Mobile Entities, Handbook of research on mobility and computing: evolving technologies and ubiquitous impacts, pp.1056-1076, 2011.
DOI : 10.4018/978-1-60960-042-6.ch064

T. Lam and K. Pearson, The Role of Proprioceptive Feedback in the Regulation and Adaptation of Locomotor Activity, Advances in Experimental and Medical Biology, vol.508, pp.343-355, 2002.
DOI : 10.1007/978-1-4615-0713-0_40

A. Lazar, G. Pipa, and J. Triesch, SORN: a self-organizing recurrent neural network, Frontiers in Computational Neuroscience, vol.3, p.23, 2009.

S. Lemouzy, V. Camps, and P. Glize, Real Time Learning of Behaviour Features for Personalised Interest Assessment, Advances in soft computing, vol.70, pp.5-14, 2010.
DOI : 10.1007/978-3-642-12384-9_2

S. Lemouzy, V. Camps, and P. Glize, Principles and Properties of a MAS Learning Algorithm: A Comparison with Standard Learning Algorithms Applied to Implicit Feedback Assessment, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, pp.228-235, 2011.
DOI : 10.1109/WI-IAT.2011.190

Z. F. Mainen, J. Joerges, J. R. Huguenard, and T. J. Sejnowski, A model of spike initiation in neocortical pyramidal neurons, Neuron, vol.15, issue.6, pp.15-1427, 1995.
DOI : 10.1016/0896-6273(95)90020-9

V. A. Makarov, F. Panetsos, and O. De-feo, A method for determining neural connectivity and inferring the underlying network dynamics using extracellular spike recordings, Journal of Neuroscience Methods, vol.144, issue.2, pp.265-279, 2005.
DOI : 10.1016/j.jneumeth.2004.11.013

S. Marsland, J. Shapiro, and U. Nehmzow, A self-organising network that grows when required, Neural Networks, vol.15, issue.8-9, pp.8-9, 2002.
DOI : 10.1016/S0893-6080(02)00078-3

C. Martin and J. Reggia, Self-assembly of neural networks viewed as swarm intelligence, Swarm Intelligence, vol.59, issue.3, pp.1-36, 2010.
DOI : 10.1007/s11721-009-0035-7

J. Misiaszek, Neural Control of Walking Balance, Exercise and Sport Sciences Reviews, vol.34, issue.3, pp.128-134, 2006.
DOI : 10.1249/00003677-200607000-00007

J. E. Misiaszek, The H-reflex as a tool in neurophysiology: Its limitations and uses in understanding nervous system function, Muscle & Nerve, vol.124, issue.2, pp.144-160, 2003.
DOI : 10.1002/mus.10372

G. Nicolis and I. Prigogine, Self-organization in nonequilibrium systems: from dissipative structures to order through fluctuations, 1977.

M. North, N. Collier, and J. Vos, Experiences creating three implementations of the repast agent modeling toolkit, ACM Transactions on Modeling and Computer Simulation, vol.16, issue.1, pp.1-25, 2006.
DOI : 10.1145/1122012.1122013

K. Ottens, M. Gleizes, and P. Glize, A multi-agent system for building dynamic ontologies, Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems , AAMAS '07, pp.1278-1284, 2007.
DOI : 10.1145/1329125.1329399

G. Perea and A. Araque, GLIA modulates synaptic transmission, Brain Research Reviews, vol.63, issue.1-2, pp.93-102, 2010.
DOI : 10.1016/j.brainresrev.2009.10.005

G. Picard and M. P. Gleizes, Cooperative self-organization to design robust and adaptive collectives, 2nd international conference on informatics in control, automation and robotics (ICINCO'05, pp.14-17, 2005.
URL : https://hal.archives-ouvertes.fr/hal-01205860

R. K. Powers, F. R. Robinson, M. A. Konodi, D. , and B. M. , Effective synaptic current can be estimated from measurements of neuronal discharge, Journal of Neurophysiology, vol.68, issue.3, pp.964-968, 1992.

B. Roeschies and C. Igel, Structure optimization of reservoir networks, Logic Journal of IGPL, vol.18, issue.5, pp.635-669, 2010.
DOI : 10.1093/jigpal/jzp043

P. Rossi, F. Pierelli, L. Parisi, A. Perrotta, M. Bartolo et al., Effect of painful heterotopic stimulation on the cutaneous silent period in the upper limbs, Clinical Neurophysiology, vol.114, issue.1, pp.1-6, 2003.
DOI : 10.1016/S1388-2457(02)00321-8

P. Schwindt and W. Crill, Factors influencing motoneuron rhytmic firing: results from a voltage-clamp study, Journal of Neurophysiology, vol.48, pp.875-890, 1982.

G. Serugendo, M. Gleizes, and A. Karageorgos, Natural Computing Series, 2011.

S. Song, K. D. Miller, and L. F. Abbott, Competitive hebbian learning through spike-timing-dependent synaptic plasticity, Nature Neuroscience, vol.3, issue.9, pp.919-926, 2000.

K. O. Stanley, Efficient evolution of neural networks through complexification, 2004.

K. O. Stanley and R. Miikkulainen, Evolving Neural Networks through Augmenting Topologies, Evolutionary Computation, vol.7, issue.2, pp.99-127, 2002.
DOI : 10.1016/S0096-3003(97)10005-4

J. Stephens, T. Usherwood, and R. Garnett, Technique for studying synaptic connections of single motoneurones in man, Nature, vol.238, issue.5575, pp.343-344, 1976.
DOI : 10.1038/263343a0

N. Takata and H. Hirase, Cortical Layer 1 and Layer 2/3 Astrocytes Exhibit Distinct Calcium Dynamics In Vivo, PLoS ONE, vol.19, issue.6, p.2525, 2008.
DOI : 10.1371/journal.pone.0002525.s005

K. S. Türker and T. S. Miles, Threshold depolarization measurements in resting human motoneurones, Journal of Neuroscience Methods, vol.39, issue.1, pp.103-107, 1991.
DOI : 10.1016/0165-0270(91)90097-J

K. S. Türker and H. B. Cheng, Motor-unit firing frequency can be used for the estimation of synaptic potentials in human motoneurones, Journal of Neuroscience Methods, vol.53, issue.2, pp.225-234, 1994.
DOI : 10.1016/0165-0270(94)90181-3

K. S. Türker and R. K. Powers, Effects of large excitatory and inhibitory inputs on motoneuron discharge rate and probability, Journal of Neurophysiology, vol.82, issue.2, pp.829-840, 1999.

K. S. Türker and R. K. Powers, Estimation of postsynaptic potentials in rat hypoglossal motoneurones: insights for human work, The Journal of Physiology, vol.29, issue.2, pp.419-431, 2003.
DOI : 10.1113/jphysiol.2003.044982

K. S. Türker and R. K. Powers, Black box revisited: a technique for estimating postsynaptic potentials in neurons, Trends in Neurosciences, vol.28, issue.7, pp.379-386, 2005.
DOI : 10.1016/j.tins.2005.05.007

K. S. Türker, J. Yang, and P. Brodin, Conditions for excitatory or inhibitory masseteric reflexes elicited by tooth pressure in man, Archives of Oral Biology, vol.42, issue.2, pp.121-128, 1997.
DOI : 10.1016/S0003-9969(96)00112-4

G. G. Turrigiano, K. R. Leslie, N. S. Desai, L. C. Rutherford, and S. B. Nelson, Activity-dependent scaling of quantal amplitude in neocortical neurons, Nature, issue.6670, pp.391-892, 1998.

S. Videau, C. Bernon, P. Glize, and J. Uribelarrea, Controlling Bioprocesses Using Cooperative Self-organizing Agents, PAAMS, pp.141-150, 2011.
DOI : 10.1007/978-3-642-19875-5_19

T. Villmann, R. Der, M. Herrmann, and T. Martinetz, Topology preservation in self-organizing feature maps: exact definition and measurement, IEEE Transactions on Neural Networks, vol.8, issue.2, pp.256-266, 1997.
DOI : 10.1109/72.557663

J. D. Warren, T. S. Miles, and K. S. Türker, Properties of synaptic noise in tonically active human motoneurons, Journal of Electromyography and Kinesiology, vol.2, issue.4, pp.189-202, 1992.
DOI : 10.1016/1050-6411(92)90023-C

J. Welcomme, M. Gleizes, and R. Redon, A self-organising multi-agent system managing complex system design application to conceptual aircraft design, International Transactions on Systems Science and Applications Self-organized Networked Systems, vol.5, issue.3, pp.208-221, 2009.

S. Whiteson and P. Stone, Evolutionary function approximation for reinforcement learning, Journal of Machine Learning Research, vol.7, pp.877-917, 2006.

M. Wooldridge, An introduction to multiagent systems, 2002.

X. Yao, Evolving artificial neural networks, Proceedings of the IEEE, pp.1423-1447, 1999.