Skip to main content

Advertisement

SpringerLink
Log in

Mimicking human neuronal pathways in silico: an emergent model on the effective connectivity

  • Published:
Journal of Computational Neuroscience Aims and scope Submit manuscript

Abstract

We present a novel computational model that detects temporal configurations of a given human neuronal pathway and constructs its artificial replication. This poses a great challenge since direct recordings from individual neurons are impossible in the human central nervous system and therefore the underlying neuronal pathway has to be considered as a black box. For tackling this challenge, we used a branch of complex systems modeling called artificial self-organization in which large sets of software entities interacting locally give rise to bottom-up collective behaviors. The result is an emergent model where each software entity represents an integrate-and-fire neuron. We then applied the model to the reflex responses of single motor units obtained from conscious human subjects. Experimental results show that the model recovers functionality of real human neuronal pathways by comparing it to appropriate surrogate data. What makes the model promising is the fact that, to the best of our knowledge, it is the first realistic model to self-wire an artificial neuronal network by efficiently combining neuroscience with artificial self-organization. Although there is no evidence yet of the model’s connectivity mapping onto the human connectivity, we anticipate this model will help neuroscientists to learn much more about human neuronal networks, and could also be used for predicting hypotheses to lead future experiments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8

Similar content being viewed by others

Notes

  1. The actual coupling that we are trying to model is referred to as effective connectivity. In some studies the term functional connectivity is also used but this term more refers to the statistical correlations between nodes (for a review see (Friston 2011)). In this sense, we used the term effective connectivity throughout this article.

  2. Part of this work was previously published in a conference proceeding (Gürcan et al. 2012).

  3. There are various definitions of the concept of self-organization from different perspectives and disciplines. For a list of most common definitions in the literature see Section 3.2.2 in (Serugendo et al. 2011).

  4. However, it is still unknown what level of biological detail is needed in order to mimic the way CNS behaves.

  5. Considering their global scope, emergent (functional) phenomena can generally be identified by some observer located outside the system that produces them (de Haan 2006; Goldstein 1999).

  6. For a detailed discussion on cooperation refer to (Georgé et al. 2011).

  7. This idea is based on physical self-organizing systems where there exists some critical threshold which causes an immediate change to the system state when reached (Nicolis and Prigogine 1977). Thus, adaptive behaviors are typically governed by a power law (Heylighen 1999) which states that large adjustments are possible but they are much less probable than small adjustments.

  8. The term reorganization in terms of self-organization was first used by Koestler in late 1960s (Koestler 1967). In his study, he defines holons and holarchies where order can result from disorder with progressive reorganization of relations between complex structural elements (see (Serugendo et al. 2011) referring to (Koestler 1967)).

  9. Naturally ψ evolution > ψ reorganization > 0.

  10. It is assumed that the post-synaptic potential (PSP) duration d psp = 4.0 ms.

  11. This bandwidth is chosen to make sure that the poststimulus ’event’ is larger than the maximum possible prestimulus variations in both directions (above and below the line of equity). We double the length of the prestimulus time to account for the by chance ’excitation’ and ’inhibition’ as the CUSUM can go both directions.

  12. In other words, if m spiked as soon as the spike coming from n reached its membrane.

  13. The idea here is quite similar to spike-timing-dependent plasticity (Song et al. 2000) since neurons are trying to increase the temporal correlations between their spikes and the spikes of their presynaptic neurons.

  14. http://www.iro.umontreal.ca/~simardr/ssj/indexe.html, last access on 1 April 2013.

  15. The case study given in (Gürcan et al. 2013) is comprehensively describing this test.

  16. http://graphml.graphdrawing.org/, last access on 1 April 2013.

  17. https://gephi.org/, last access on 1 April 2013.

  18. STDP (Song et al. 2000), synaptic scaling of the excitatory-excitatory connections (Turrigiano et al. 1998), and intrinsic plasticity regulating the thresholds of excitatory units.

References

  • Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723.

    Article  Google Scholar 

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

  • Bernon, C., Capera, D., Mano, J.-P. (2009). Engineering self-modeling systems: application to biology. In A. Artikis, G. Picard, L. Vercouter (Eds.), Engineering societies in the agents world IX. Lecture notes in computer science (Vol. 5485, pp. 248–263). Berlin Heidelberg: Springer.

    Google Scholar 

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

  • Brax, N., Andonoff, E., Gleizes, M.-P. (2012). A self-adaptive multi-agent system for abnormal behavior detection in maritime surveillance (regular paper). In KES international conference (KES-AMSTA), Dubrovnick (Vol. 7327, pp. 174–185). New York: Springer.

  • Brinkworth, R.S.A., & Türker, K.S (2003). A method for quantifying reflex responses from intra-muscular and surface electromyogram. Journal of Neuroscience Methods, 122(2), 179–193.

    Article  PubMed  Google Scholar 

  • Buibas, M., & Silva, G.A. (2011). A framework for simulating and estimating the state and functional topology of complex dynamic geometric networks. Neural Computation, 23(1), 183–214.

    Article  PubMed  Google Scholar 

  • Calvin, W., & Schwindt, P. (1972). Steps in production of motoneuron spikes during rhytmic firing. Journal of Neurophysiology, 35, 297–310.

    CAS  PubMed  Google Scholar 

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

  • Capaday, C. (2002). The special nature of human walking and its neural control. Trends in Neurosciences, 25(7), 370–376.

    Article  CAS  PubMed  Google Scholar 

  • Capera, D., George, J.-P., Gleizes, M.-P., Glize, P. (2003). The amas theory for complex problem solving based on self-organizing cooperative agents. In Proceedings twelfth IEEE international workshops on enabling technologies: infrastructure for collaborative enterprises (WETICE 2003) (pp. 383–388).

  • Capera, D., Gleizes, M.-P., Glize, P. (2004). Mechanism type synthesis based on self-assembling agents. Journal of Applied Artificial Intelligence, 18(9–10), 921–936.

    Article  Google Scholar 

  • Chatzidimitriou, K.C., & Mitkas, P.A. (2013). Adaptive reservoir computing through evolution and learning. Neurocomputing, 103, 198–209.

    Article  Google Scholar 

  • Colquhoun, D., & Hawkes, A.G. (1990). 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, 240(1299), 453–477.

    Article  CAS  PubMed  Google Scholar 

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

  • David, O., Kiebel, S.J., Harrison, L.M., Mattout, J., Kilner, J.M., Friston, K.J. (2006). Dynamic causal modeling of evoked responses in eeg and meg. NeuroImage, 30(4), 1255–1272.

    Article  PubMed  Google Scholar 

  • Dayan, P., & Abbott, L. (2005). Theoretical neuroscience: computational and mathematical modeling of neural systems. Computational Neuroscience. MIT Press.

  • de Haan, J. (2006). How emergence arises. Ecological Complexity, 3(4), 293–301.

    Article  Google Scholar 

  • Eldawlatly, S., Zhou, Y., Jin, R., Oweiss, K.G. (2010). On the use of dynamic bayesian networks in reconstructing functional neuronal networks from spike train ensembles. Neural Computation, 22(1), 158–189.

    Article  PubMed  PubMed Central  Google Scholar 

  • Ellaway, P. (1978). Cumulative sum technique and its application to the analysis of peristimulus time histograms. Electroencephalography and Clinical Neurophysiology, 45(2), 302–304.

    Article  CAS  PubMed  Google Scholar 

  • Fahlman, S.E., & Lebiere, C. (1990). Advances in neural information processing systems 2. chapter the cascade-correlation learning architecture (pp. 524–532). San Francisco: Morgan Kaufmann Publishers Inc.

    Google Scholar 

  • Fellin, T. (2009). Communication between neurons and astrocytes: relevance to the modulation of synaptic and network activity. Journal of Neurochemistry, 108(3), 533–544.

    Article  CAS  PubMed  Google Scholar 

  • Floreano, D., Dürr, P., Mattiussi, C. (2008). Neuroevolution: from architectures to learning. Evolutionary Intelligence, 1(1), 47–62.

    Article  Google Scholar 

  • Friston, K. (2009). The free-energy principle: a rough guide to the brain?Trends in Cognitive Sciences, 13(7), 293–301.

    Article  PubMed  Google Scholar 

  • Friston, K.J. (2011). Functional and effective connectivity: a review. Brain Connectivity, 1(1), 13–36.

    Article  PubMed  Google Scholar 

  • Friston, K., Harrison, L., Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273–1302.

    Article  CAS  PubMed  Google Scholar 

  • Fritzke, B. (1994). A growing neural gas network learns topologies. In NIPS (pp. 625–632).

  • Garrido, M.I., Kilner, J.M., Kiebel, S.J., Stephan, K.E., Friston, K.J. (2007). Dynamic causal modelling of evoked potentials: a reproducibility study. NeuroImage, 36(3), 571–580.

    Article  PubMed  PubMed Central  Google Scholar 

  • Georgé, J.P., Edmonds, B., Glize, P. (2004). Making self-organizing adaptive multi-agent systems work, chapter 16 (pp. 321–340). Kluwer Publishing.

  • Georgé, J.-P., Gleizes, M.-P., Camps, V. (2011) In G. Serugendo, M.-P. Gleizes, A. Karageorgos (Eds.), Self-organizing software: from natural to artificial adaptation, natural computing series,(chap. 9, pp. 347–377). Springer.

  • Gerstner, W., Sprekeler, H., Deco, G. (2012). Theory and simulation in neuroscience. Science, 338(6103), 60–65.

    Article  CAS  PubMed  Google Scholar 

  • Goldstein, J. (1999). Emergence as a construct: history and issues. Emergence : A Journal of Complexity Issues in Organizations and Management. The New England Complex Systems Institute, 1(1), 49–72.

    Article  Google Scholar 

  • Grande, G., & Cafarelli, E. (2003). Ia afferent input alters the recruitment thresholds and firing rates of single human motor units. Experimental Brain Research, 150, 449–457.

    CAS  PubMed  Google Scholar 

  • Guivarch, V., Camps, V., Péninou, A. (2012). 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 no 3.

  • Gürcan, Ö., Dikenelli, O., Türker, K.S. (2010a). In R. Trumph (Ed.), 20th european meeting on cybernetics and systems research (EMCSR 2010) (pp. 509–514). Vienna.

  • Gürcan, Ö., Dikenelli, O., Türker, K.S. (2010b). Understanding wiring of human cns using agent-based modeling and simulation. Turkish Journal of Neuroscience, 16(1), 219–220.

    Google Scholar 

  • Gürcan, Ö., Dikenelli, O., Bernon, C. (2011). Towards a generic testing framework for agent-based simulation models. In 5th international workshop on multi-agent systems and simulation (MAS&S’11) (pp. 635–642). Szczecin.

  • Gürcan, Ö., Bernon, C., Türker, K.S., Mano, J.-P., Glize, P., Dikenelli, O. (2012). Simulating human single motor units using self-organizing agents. In Self-adaptive and self-organizing systems (SASO), 2012 IEEE sixth international conference on (pp. 11–20).

  • Gürcan, Ö., Dikenelli, O., Bernon, C. (2013). A generic testing framework for agent-based simulation models. Journal of Simulation (JOS), 1–19. http://www.palgrave-journals.com/jos/journal/vaop/ncurrent/full/jos201226a.html.

  • Gydikov, A., Tankov, N., Gerilovsky, L., Radicheva, N. (1977). Motor unit activity upon polysynaptic reflex in man. Agressologie, 18(2), 103–108.

    CAS  PubMed  Google Scholar 

  • Hay, E., Hill, S., Schürmann, F., Markram, H., Segev, I. (2011). Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties. PLoS Computational Biology, 7(7), e1002107.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Heylighen, F. (1999). 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). Publishers Co. Ltd.

  • Hodgkin, A.L., & Huxley, A.F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117(4), 500–544.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Iansek, R., & Redman, S. (1973). The amplitude, time course and charge of unitary post-synaptic potentials evoked in spinal motoneurone dendrites. Journal of Neurophysiology, 234, 665–688.

    CAS  Google Scholar 

  • Izhikevich, E.M., & Edelman, G.M. (2008). Large-scale model of mammalian thalamocortical systems. Proceedings of the National Academy of Sciences, 105(9), 3593–3598.

    Article  CAS  Google Scholar 

  • Jenner, J.R., & Stephens, J.A. (1982). Cutaneous reflex responses and their central nervous pathways studied in man. The Journal of Physiology, 333(1), 405–419.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Kaddoum, E., & Georgé, J.-P. (2012). Collective self-tuning for complex product design (short paper). In IEEE international conference on self-adaptive and self-organizing systems (SASO), Lyon - France, page (electronic medium). CPS (Conference Publishing Services).

  • Kandel, E.R., Schwartz, J., Jessell, T.M. (2000). Principles of neural science, 4th edn. Mc Graw Hill.

  • Kiebel, S.J., David, O., Friston, K.J. (2006). Dynamic causal modelling of evoked responses in eeg/meg with lead field parameterization. NeuroImage, 30(4), 1273–1284.

    Article  PubMed  Google Scholar 

  • Koch, C., & Segev, I. (1989). Methods in neuronal modeling: from synapses to networks. Computational Neuroscience Series. Mit Press.

  • Koestler, A. (1967). The ghost in the machine. London, the danube ed. edition: Hutchinson and Co.

    Google Scholar 

  • Kuraszkiewicz, B., Mlozniak, D., Piotrkiewicz, M. (2012) Threshold-crossing model of human motoneuron. In E. Pitka, & J. Kawa (Eds.), Information technologies in biomedicine, lecture notes in computer science (Vol. 7339, pp. 209–218). Berlin Heidelberg: Springer.

    Google Scholar 

  • Lacouture, J., Rodriguez, I., Arcangeli, J.-P., Chassot, C., Desprats, T., Drira, K., Garijo, F., Noel, V., Sibilla, M., Tessier, C. (2011) Mission-aware adaptive communication for collaborative mobile entities. In M.M. Cruz-Cunha, & F. Moreira (Eds.), Handbook of research on mobility and computing: evolving technologies and ubiquitous impacts, (chap. 64, pp. 1056–1076). IGI Global.

  • Lam, T., & Pearson, K. (2002). The role of proprioceptive feedback in the regulation and adaptation of locomotor activity. Advances in Experimental and Medical Biology, 508, 343–355.

    Article  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  • Lemouzy, S., Camps, V., Glize, P. (2010). Real time learning of behaviour features for personalised interest assessment. In Y. Demazeau, F. Dignum, J. Corchado, J. Pérez (Eds.), Advances in practical applications of agents and multiagent systems. Advances in soft computing (Vol. 70, pp. 5–14). Berlin / Heidelberg: Springer.

    Chapter  Google Scholar 

  • Lemouzy, S., Camps, V., Glize, P. (2011). Principles and properties of a mas learning algorithm: A comparison with standard learning algorithms applied to implicit feedback assessment. In Proceedings of the 2011 IEEE/WIC/ACM international conferences on web intelligence and intelligent agent technology - volume 02, WI-IAT ’11 (pp. 228–235). Washington: IEEE Computer Society.

  • Mainen, Z.F., Joerges, J., Huguenard, J.R., Sejnowski, T.J. (1995). A model of spike initiation in neocortical pyramidal neurons. Neuron, 15(6), 1427–39.

    Article  CAS  PubMed  Google Scholar 

  • Makarov, V.A., Panetsos, F., de Feo, O. (2005). A method for determining neural connectivity and inferring the underlying network dynamics using extracellular spike recordings. Journal of Neuroscience Methods, 144(2), 265–279.

    Article  PubMed  Google Scholar 

  • Marsland, S., Shapiro, J., Nehmzow, U. (2002). A self-organising network that grows when required. Neural Networks, 15(8–9), 1041–1058.

    Article  PubMed  Google Scholar 

  • Martin, C., & Reggia, J. (2010). Self-assembly of neural networks viewed as swarm intelligence. Swarm Intelligence, 4, 1–36.

    Article  Google Scholar 

  • Misiaszek, J. (2006). Neural control of walking balance: if falling then react else continue. Exercise & Sport Sciences Reviews, 34(3), 128–134.

    Article  Google Scholar 

  • Misiaszek, J.E. (2003). The h-reflex as a tool in neurophysiology: Its limitations and uses in understanding nervous system function. Muscle & Nerve, 28(2), 144–160.

    Article  Google Scholar 

  • Nicolis, G., & Prigogine, I. (1977). Self-organization in nonequilibrium systems: from dissipative structures to order through fluctuations. Hoboken: Wiley (Wiley-Interscience Publication).

    Google Scholar 

  • North, M., Collier, N., Vos, J. (2006). Experiences creating three implementations of the repast agent modeling toolkit. ACM Transactions Modelling Computing Simulation, 16(1), 1–25.

    Article  Google Scholar 

  • Ottens, K., Gleizes, M.-P., Glize, P. (2007). A multi-agent system for building dynamic ontologies. In International joint conference on autonomous agents and multiagent systems (AAMAS), Hawaii (pp. 1278–1284). ACM Press.

  • Perea, G., & Araque, A. (2010). Glia modulates synaptic transmission. Brain Research Reviews, 63(1–2), 93–102.

    Article  CAS  PubMed  Google Scholar 

  • Picard, G., & Gleizes, M.P. (2005). Cooperative self-organization to design robust and adaptive collectives. In 2nd International conference on informatics in control, automation and robotics (ICINCO’05), 14-17 september 2005, Barcelona, Spain, volume I (pp. 236–241). INSTICC Press.

  • Powers, R.K., Robinson, F.R., Konodi, M.A., D., B.M. (1992). Effective synaptic current can be estimated from measurements of neuronal discharge. Journal of Neurophysiology, 68(3), 964–968.

    CAS  PubMed  Google Scholar 

  • Roeschies, B., & Igel, C. (2010). Structure optimization of reservoir networks. Logic Journal of the IGPL, 18(5), 635–669.

    Article  Google Scholar 

  • Rossi, P., Pierelli, F., Parisi, L., Perrotta, A., Bartolo, M., Amabile, G., Serrao, M. (2003). Effect of painful heterotopic stimulation on the cutaneous silent period in the upper limbs. Clinical Neurophysiology, 114(1), 1–6.

    PubMed  Google Scholar 

  • Schwindt, P., & Crill, W. (1982). Factors influencing motoneuron rhytmic firing: results from a voltage-clamp study. Journal of Neurophysiology, 48, 875–890.

    CAS  PubMed  Google Scholar 

  • Serugendo, G., Gleizes, M.-P., Karageorgos, A. (Eds.) (2011). Natural Computing Series. Springer.

  • Song, S., Miller, K.D., Abbott, L.F. (2000). Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neuroscience, 3(9), 919–926.

    Article  CAS  PubMed  Google Scholar 

  • Stanley, K.O. (2004). Efficient evolution of neural networks through complexification. Ph.D. thesis, Department of Computer Sciences, The University of Texas at Austin.

  • Stanley, K.O., & Miikkulainen, R. (2002). Evolving neural networks through augmenting topologies. Evolutionary Computation, 10(2), 99–127.

    Article  PubMed  Google Scholar 

  • Stephens, J., Usherwood, T., Garnett, R. (1976). Technique for studying synaptic connections of single motoneurones in man. Nature, 263, 343–344.

    Article  CAS  PubMed  Google Scholar 

  • Takata, N., & Hirase, H. (2008). Cortical layer 1 and layer 2/3 astrocytes exhibit distinct calcium dynamics in vivo. PLoS ONE, 3(6), e2525.

    Article  PubMed  PubMed Central  Google Scholar 

  • Türker, K.S., & Miles, T.S. (1991). Threshold depolarization measurements in resting human motoneurones. Journal of Neuroscience Methods, 39(1), 103–107.

    Article  PubMed  Google Scholar 

  • Türker, K.S., & Cheng, H.B. (1994). Motor-unit firing frequency can be used for the estimation of synaptic potentials in human motoneurones. Journal of Neuroscience Methods, 53(2), 225–234.

    Article  PubMed  Google Scholar 

  • Türker, K.S., & Powers, R.K. (1999). Effects of large excitatory and inhibitory inputs on motoneuron discharge rate and probability. Journal of Neurophysiology, 82(2), 829–840.

    PubMed  Google Scholar 

  • Türker, K.S., & Powers, R.K. (2003). Estimation of postsynaptic potentials in rat hypoglossal motoneurones: insights for human work. Journal of Physiology, 551, 419–431.

    Article  PubMed  PubMed Central  Google Scholar 

  • Türker, K.S., & Powers, R.K. (2005). Black box revisited: a technique for estimating postsynaptic potentials in neurons. Trends in Neurosciences, 28(7), 379–386.

    Article  PubMed  Google Scholar 

  • Türker, K.S., Yang, J., Brodin, P. (1997). Conditions for excitatory or inhibitory masseteric reflexes elicited by tooth pressure in man. Archives of Oral Biology, 42(2), 121–128.

    Article  PubMed  Google Scholar 

  • Turrigiano, G.G., Leslie, K.R., Desai, N.S., Rutherford, L.C., Nelson, S.B. (1998). Activity-dependent scaling of quantal amplitude in neocortical neurons. Nature, 391(6670), 892–896.

    Article  CAS  PubMed  Google Scholar 

  • Videau, S., Bernon, C., Glize, P., Uribelarrea, J.-L. (2011). Controlling bioprocesses using cooperative self-organizing agents. In PAAMS, 141–150.

  • Villmann, T., Der, R., Herrmann, M., Martinetz, T. (1997). Topology preservation in self-organizing feature maps: exact definition and measurement. IEEE Transactions on Neural Networks, 8(2), 256–266.

    Article  CAS  PubMed  Google Scholar 

  • Warren, J.D, Miles, T.S., Türker, K.S. (1992). Properties of synaptic noise in tonically active human motoneurons. Journal of Electromyography and Kinesiology, 2(4), 189–202.

    Article  CAS  PubMed  Google Scholar 

  • Welcomme, J.-B., Gleizes, M.-P., Redon, R. (2009). 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, 5(3), 208–221.

    Google Scholar 

  • Whiteson, S., & Stone, P. (2006). Evolutionary function approximation for reinforcement learning. Journal of Machine Learning Research, 7, 877–917.

    Google Scholar 

  • Wooldridge, M. (2002). An introduction to multiagent systems. Hoboken: Wiley.

    Google Scholar 

  • Yao, X. (1999). Evolving artificial neural networks. Proceedings of the IEEE, 87(9), 1423–1447.

    Article  Google Scholar 

Download references

Acknowledgments

Önder Gürcan is supported by the Turkish Scientific and Technological Research Council (TÜBİTAK) through a domestic PhD scholarship program (BAYG-2211) and the French Government through the cotutelle scholarship program. In addition, the authors would like to sincerely thank Ş. Utku Yavuz from Bernstein Center for Computational Neuroscience (BCCN) in Georg-August University for his technical support on the scientific data about the activity of human motoneurons and Serdar Korukoğlu from Ege University Computer Engineering Department for his technical support on statistical analysis. Lastly, we would like to thank the reviewers for their constructive feedback and advice.

Conflict of Interest

The authors declare that they have no conflict of interest.

Author information

Authors and Affiliations

  1. Computer Engineering Department, Ege University, Universite cad., 35100, Bornova, Izmir, Turkey

    Önder Gürcan & Oğuz Dikenelli

  2. School of Medicine, Koc University, Rumelifeneri Yolu, 34450, Sariyer, Istanbul, Turkey

    Kemal S. Türker

  3. IRIT Laboratory, Universite Paul Sabatier, 118 Route de Narbonne, Toulouse, France

    Önder Gürcan, Carole Bernon & Pierre Glize

  4. Upetec, 10 Avenue de l’Europe, 31520, Ramonville-Saint-Agne, France

    Jean-Pierre Mano

Authors
  1. Önder Gürcan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kemal S. Türker
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jean-Pierre Mano
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carole Bernon
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Oğuz Dikenelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Pierre Glize
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Önder Gürcan.

Additional information

Action Editor: Mark Goldman

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gürcan, Ö., Türker, K.S., Mano, JP. et al. Mimicking human neuronal pathways in silico: an emergent model on the effective connectivity. J Comput Neurosci 36, 235–257 (2014). https://doi.org/10.1007/s10827-013-0467-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10827-013-0467-3

Keywords

Search

Navigation