Cognitive Neurodynamics

, Volume 9, Issue 3, pp 265–277 | Cite as

A neural network model of reliably optimized spike transmission

Review Paper


We studied the detailed structure of a neuronal network model in which the spontaneous spike activity is correctly optimized to match the experimental data and discuss the reliability of the optimized spike transmission. Two stochastic properties of the spontaneous activity were calculated: the spike-count rate and synchrony size. The synchrony size, expected to be an important factor for optimization of spike transmission in the network, represents a percentage of observed coactive neurons within a time bin, whose probability approximately follows a power-law. We systematically investigated how these stochastic properties could matched to those calculated from the experimental data in terms of the log-normally distributed synaptic weights between excitatory and inhibitory neurons and synaptic background activity induced by the input current noise in the network model. To ensure reliably optimized spike transmission, the synchrony size as well as spike-count rate were simultaneously optimized. This required changeably balanced log-normal distributions of synaptic weights between excitatory and inhibitory neurons and appropriately amplified synaptic background activity. Our results suggested that the inhibitory neurons with a hub-like structure driven by intensive feedback from excitatory neurons were a key factor in the simultaneous optimization of the spike-count rate and synchrony size, regardless of different spiking types between excitatory and inhibitory neurons.


Spike transmission Power-law-distributed synchrony  Log-normally distributed synaptic weights Synaptic background activity 


  1. Baker JL, Olds JL (2007) Theta phase precession emerges from a hybrid computational model of a CA3 place cell. Cogn Neurodyn 1(3):237–248CrossRefPubMedCentralPubMedGoogle Scholar
  2. Beggs JM, Plenz D (2003) Neuronal avalanches in neocortical circuits. J Neurosci 23(35):11167–11177PubMedGoogle Scholar
  3. Bonifazi P, Goldin M, Picardo MA, Jorquera I, Cattani A, Bianconi G, Represa A, Ben-Ari Y, Cossart R (2009) GABAergic hub neurons orchestrate synchrony in developing hippocampal networks. Science 326(5958):1419–1424CrossRefPubMedGoogle Scholar
  4. Brown EN, Kass RE, Mitra PP (2004) Multiple neural spike train data analysis: state-of-the-art and future challenges. Nat Neurosci 7(5):456–461CrossRefPubMedGoogle Scholar
  5. Brown E, Moehlis J, Holmes P (2004) On the phase reduction and response dynamics of neural oscillator populations. Neural Comput 16:673–715CrossRefPubMedGoogle Scholar
  6. Cohen I, Miles R (2000) Contributions of intrinsic and synaptic activities to the generation of neuronal discharges in in vitro hippocampus. J Physiol 524(Pt 2):485–502CrossRefPubMedCentralPubMedGoogle Scholar
  7. Fujisawa S, Matsuki N, Ikegaya Y (2006) Single neurons can induce phase transitions of cortical recurrent networks with multiple internal states. Cereb Cortex 16(5):639–654CrossRefPubMedGoogle Scholar
  8. Gulyás AI, Miles R, Hájos N, Freund TF (1993) Precision and variability in postsynaptic target selection of inhibitory cells in the hippocampal CA3 region. Eur J Neurosci 5(12):1729–1751CrossRefPubMedGoogle Scholar
  9. Hampson RE, Pons TP, Stanford TR, Deadwyler SA (2004) Categorization in the monkey hippocampus: a possible mechanism for encoding information into memory. Proc Natl Acad Sci USA 101(9):3184–3189CrossRefPubMedCentralPubMedGoogle Scholar
  10. Hampson RE, Song D, Opris I, Santos LM, Shin DC, Gerhardt GA, Marmarelis VZ, Berger TW, Deadwyler SA (2013) Facilitation of memory encoding in primate hippocampus by a neuroprosthesis that promotes task-specific neural firing. J Neural Eng 10(6):066013CrossRefPubMedCentralPubMedGoogle Scholar
  11. Heinzle J, König P, Salazar RF (2007) Modulation of synchrony without changes in firing rates. Cog Neurodyn 1:225–235CrossRefGoogle Scholar
  12. Helmchen F, Svoboda K, Denk W, Tank DW (1999) In vivo dendritic calcium dynamics in deep-layer cortical pyramidal neurons. Nat Neurosci 2(11):989–996CrossRefPubMedGoogle Scholar
  13. Hiratani N, Teramae JN, Fukai T (2013) Associative memory model with long-tail-distributed Hebbian synaptic connections. Front Comput Neurosci 6:102CrossRefPubMedCentralPubMedGoogle Scholar
  14. Ikegaya Y, Sasaki T, Ishikawa D, Honma N, Tao K, Takahashi N, Minamisawa G, Ujita S, Matsuki N (2013) Interpyramid spike transmission stabilizes the sparseness of recurrent network activity. Cereb Cortex 23(2):293–304CrossRefPubMedGoogle Scholar
  15. Izhikevich EM (2003) Simple model of spiking neurons. IEEE Trans Neural Netw 14:1569–1572CrossRefPubMedGoogle Scholar
  16. Izhikevich EM (2007) Dynamical systems in neuroscience: the geometry of excitability and bursting. MIT Press, Cambridge, MAGoogle Scholar
  17. Jensen MS, Azouz R, Yaari Y (1996) Spike after-depolarization and burst generation in adult rat hippocampal CA1 pyramidal cells. J Physiol 492:199–210CrossRefPubMedCentralPubMedGoogle Scholar
  18. Jiruska P, Csicsvari J, Powell AD, Fox JE, Chang WC, Vreugdenhil M, Li X, Palus M, Bujan AF, Dearden RW, Jefferys JG (2010) High-frequency network activity, global increase in neuronal activity, and synchrony expansion precede epileptic seizures in vitro. J Neurosci 30(16):5690–5701CrossRefPubMedGoogle Scholar
  19. Kesner RP (2007) Behavioral functions of the CA3 subregion of the hippocampus. Learn Mem 14(11):771–781CrossRefPubMedGoogle Scholar
  20. Kinouchi O, Copelli M (2006) Optimal dynamical range of excitable networks at criticality. Nat Phys 2:348–351CrossRefGoogle Scholar
  21. Klaus A, Yu S, Plenz D (2011) Statistical analyses support power law distributions found in neuronal avalanches. PLoS one 6(5):e19779CrossRefPubMedCentralPubMedGoogle Scholar
  22. Kwok HF, Jurica P, Raffone A, van Leeuwen C (2007) Robust emergence of small-world structure in networks of spiking neurons. Cogn Neurodyn 1(1):39–51CrossRefPubMedCentralPubMedGoogle Scholar
  23. Larremore DB, Shew WL, Restrepo JG (2011) Predicting criticality and dynamic range in complex networks: effects of topology. Phys Rev Lett 106(5):058101CrossRefPubMedGoogle Scholar
  24. Lefort S, Tomm C, Floyd Sarria JC, Petersen CC (2009) The excitatory neuronal network of the C2 barrel column in mouse primary somatosensory cortex. Neuron 61(2):301–316CrossRefPubMedGoogle Scholar
  25. Li XG, Somogyi P, Ylinen A, Buzsáki G (1994) The hippocampal CA3 network: an in vivo intracellular labeling study. J Comp Neurol 339(2):181–208CrossRefPubMedGoogle Scholar
  26. Li S, Wu S (2007) Robustness of neural codes and its implication on natural image processing. Cogn Neurodyn 1(3):261–272CrossRefPubMedCentralPubMedGoogle Scholar
  27. Miles R, Wong RKS (1983) Single neurones can initiate synchronized population discharge in the hippocampus. Nature 306:371–373CrossRefPubMedGoogle Scholar
  28. Sarid L, Bruno R, Sakmann B, Segev I, Feldmeyer D (2007) Modeling a layer 4-to-layer 2/3 module of a single column in rat neocortex: interweaving in vitro and in vivo experimental observations. Proc Natl Acad Sci USA 104(41):16353–16358CrossRefPubMedCentralPubMedGoogle Scholar
  29. Sasaki T, Matsuki N, Ikegaya Y (2007) Metastability of active CA3 networks. J Neurosci 27(3):517–528CrossRefPubMedGoogle Scholar
  30. Shew WL, Yang H, Petermann T, Roy R, Plenz D (2009) Neuronal avalanches imply maximum dynamic range in cortical networks at criticality. J Neurosci 29(49):15595–15600CrossRefPubMedCentralPubMedGoogle Scholar
  31. Shew WL, Yang H, Yu S, Roy R, Plenz D (2011) Information capacity and transmission are maximized in balanced cortical networks with neuronal avalanches. J Neurosci 31(1):55–63CrossRefPubMedCentralPubMedGoogle Scholar
  32. Shlizerman E, Holmes P (2012) Neural dynamics, bifurcations, and firing rates in a quadratic integrate-and-fire model with a recovery variable. I: deterministic behavior. Neural Comput 24(8):2078–2118CrossRefPubMedGoogle Scholar
  33. Singer W (2009) Distributed processing and temporal codes in neuronal networks. Cogn Neurodyn 3(3):189–196CrossRefPubMedCentralPubMedGoogle Scholar
  34. Smith KL, Szarowski DH, Turner JN, Swann JW (1995) Diverse neuronal populations mediate local circuit excitation in area CA3 of developing hippocampus. J Neurophysiol 74(2):650–672PubMedGoogle Scholar
  35. Song S, Sjöström PJ, Reigl M, Nelson S, Chklovskii DB (2005) Highly nonrandom features of synaptic connectivity in local cortical circuits. PLoS Biol 3(3):e68CrossRefPubMedCentralPubMedGoogle Scholar
  36. Steyn-Ross DA, Steyn-Ross M (2010) Modeling phase transitions in the brain. Springer, New YorkCrossRefGoogle Scholar
  37. Takahashi N, Sasaki T, Matsumoto W, Matsuki N, Ikegaya Y (2010) Circuit topology for synchronizing neurons in spontaneously active networks. Proc Natl Acad Sci USA 107:10244–10249CrossRefPubMedCentralPubMedGoogle Scholar
  38. Tateno K, Hayashi H, Ishizuka S (1998) Complexity of spatiotemporal activity of a neural network model which depends on the degree of synchronization. Neural Netw 11(6):985–1003CrossRefPubMedGoogle Scholar
  39. Taxidis J, Coombes S, Mason R, Owen MR (2012) Modeling sharp wave-ripple complexes through a CA3-CA1 network model with chemical synapses. Hippocampus 22(5):995–1017CrossRefPubMedGoogle Scholar
  40. Taxidis J, Mizuseki K, Mason R, Owen MR (2013) Influence of slow oscillation on hippocampal activity and ripples through cortico-hippocampal synaptic interactions, analyzed by a cortical-CA3-CA1 network model. Front Comput Neurosci 7:3CrossRefPubMedCentralPubMedGoogle Scholar
  41. Teramae JN, Tsubo Y, Fukai T (2013) Optimal spike-based communication in excitable networks with strong-sparse and weak-dense links. Sci Rep 2:485Google Scholar
  42. Touboul J, Brette R (2009) Spiking dynamics of bidimensional integrate-and-fire neurons. SIAM J Appl Dyn Syst 8(4):1462–1506CrossRefGoogle Scholar
  43. Traub RD, Miles R (1991) Neuronal networks of the hippocampus. Cambridge Univ Press, CambridgeCrossRefGoogle Scholar
  44. Wagatsuma H, Yamaguchi Y (2007) Neural dynamics of the cognitive map in the hippocampus. Cogn Neurodyn 1(2):119–141CrossRefPubMedCentralPubMedGoogle Scholar
  45. Wang XJ, Buzsáki G (1996) Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model. J Neurosci 16(20):6402–6413PubMedGoogle Scholar
  46. Yang H, Shew WL, Roy R, Plenz D (2012) Maximal variability of phase synchrony in cortical networks with neuronal avalanches. J Neurosci 32(3):1061–1072CrossRefPubMedCentralPubMedGoogle Scholar
  47. Yoshida M, Hayashi H, Tateno K, Ishizuka S (2002) Stochastic resonance in the hippocampal CA3-CA1 model: a possible memory recall mechanism. Neural Netw 15(10):1171–1183CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Toshikazu Samura
    • 1
  • Yuji Ikegaya
    • 2
  • Yasuomi D. Sato
    • 3
    • 4
  1. 1.Department of Applied Molecular Bioscience, Graduate School of MedicineYamaguchi UniversityYamaguchiJapan
  2. 2.Laboratory of Chemical Pharmacology, Graduate School of Pharmaceutical SciencesThe University of TokyoTokyoJapan
  3. 3.Frankfurt Institute for Advanced Studies (FIAS)Goethe University FrankfurtFrankfurt am MainGermany
  4. 4.Institute of Industrial ScienceThe University of TokyoTokyoJapan

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