Skip to main content
SpringerLink
Log in

A neural network model of reliably optimized spike transmission

  • Review Paper
  • Published:
Cognitive Neurodynamics Aims and scope Submit manuscript

Abstract

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.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Baker JL, Olds JL (2007) Theta phase precession emerges from a hybrid computational model of a CA3 place cell. Cogn Neurodyn 1(3):237–248

    Article  PubMed Central  PubMed  Google Scholar 

  • Beggs JM, Plenz D (2003) Neuronal avalanches in neocortical circuits. J Neurosci 23(35):11167–11177

    CAS  PubMed  Google Scholar 

  • 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):1419–1424

    Article  CAS  PubMed  Google Scholar 

  • Brown EN, Kass RE, Mitra PP (2004) Multiple neural spike train data analysis: state-of-the-art and future challenges. Nat Neurosci 7(5):456–461

    Article  CAS  PubMed  Google Scholar 

  • Brown E, Moehlis J, Holmes P (2004) On the phase reduction and response dynamics of neural oscillator populations. Neural Comput 16:673–715

    Article  PubMed  Google Scholar 

  • 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):485–502

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • 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):639–654

    Article  PubMed  Google Scholar 

  • 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):1729–1751

    Article  PubMed  Google Scholar 

  • 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):3184–3189

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • 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):066013

    Article  PubMed Central  PubMed  Google Scholar 

  • Heinzle J, König P, Salazar RF (2007) Modulation of synchrony without changes in firing rates. Cog Neurodyn 1:225–235

    Article  Google Scholar 

  • Helmchen F, Svoboda K, Denk W, Tank DW (1999) In vivo dendritic calcium dynamics in deep-layer cortical pyramidal neurons. Nat Neurosci 2(11):989–996

    Article  CAS  PubMed  Google Scholar 

  • Hiratani N, Teramae JN, Fukai T (2013) Associative memory model with long-tail-distributed Hebbian synaptic connections. Front Comput Neurosci 6:102

    Article  PubMed Central  PubMed  Google Scholar 

  • 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):293–304

    Article  PubMed  Google Scholar 

  • Izhikevich EM (2003) Simple model of spiking neurons. IEEE Trans Neural Netw 14:1569–1572

    Article  CAS  PubMed  Google Scholar 

  • Izhikevich EM (2007) Dynamical systems in neuroscience: the geometry of excitability and bursting. MIT Press, Cambridge, MA

    Google Scholar 

  • Jensen MS, Azouz R, Yaari Y (1996) Spike after-depolarization and burst generation in adult rat hippocampal CA1 pyramidal cells. J Physiol 492:199–210

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • 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):5690–5701

    Article  CAS  PubMed  Google Scholar 

  • Kesner RP (2007) Behavioral functions of the CA3 subregion of the hippocampus. Learn Mem 14(11):771–781

    Article  PubMed  Google Scholar 

  • Kinouchi O, Copelli M (2006) Optimal dynamical range of excitable networks at criticality. Nat Phys 2:348–351

    Article  CAS  Google Scholar 

  • Klaus A, Yu S, Plenz D (2011) Statistical analyses support power law distributions found in neuronal avalanches. PLoS one 6(5):e19779

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • 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):39–51

    Article  PubMed Central  PubMed  Google Scholar 

  • Larremore DB, Shew WL, Restrepo JG (2011) Predicting criticality and dynamic range in complex networks: effects of topology. Phys Rev Lett 106(5):058101

    Article  PubMed  Google Scholar 

  • 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):301–316

    Article  CAS  PubMed  Google Scholar 

  • Li XG, Somogyi P, Ylinen A, Buzsáki G (1994) The hippocampal CA3 network: an in vivo intracellular labeling study. J Comp Neurol 339(2):181–208

    Article  CAS  PubMed  Google Scholar 

  • Li S, Wu S (2007) Robustness of neural codes and its implication on natural image processing. Cogn Neurodyn 1(3):261–272

    Article  PubMed Central  PubMed  Google Scholar 

  • Miles R, Wong RKS (1983) Single neurones can initiate synchronized population discharge in the hippocampus. Nature 306:371–373

    Article  CAS  PubMed  Google Scholar 

  • 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):16353–16358

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Sasaki T, Matsuki N, Ikegaya Y (2007) Metastability of active CA3 networks. J Neurosci 27(3):517–528

    Article  CAS  PubMed  Google Scholar 

  • 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):15595–15600

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • 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):55–63

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • 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):2078–2118

    Article  PubMed  Google Scholar 

  • Singer W (2009) Distributed processing and temporal codes in neuronal networks. Cogn Neurodyn 3(3):189–196

    Article  PubMed Central  PubMed  Google Scholar 

  • 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):650–672

    CAS  PubMed  Google Scholar 

  • 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):e68

    Article  PubMed Central  PubMed  Google Scholar 

  • Steyn-Ross DA, Steyn-Ross M (2010) Modeling phase transitions in the brain. Springer, New York

    Book  Google Scholar 

  • 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 107:10244–10249

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • 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):985–1003

    Article  PubMed  Google Scholar 

  • 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):995–1017

    Article  CAS  PubMed  Google Scholar 

  • 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 7:3

    Article  PubMed Central  PubMed  Google Scholar 

  • Teramae JN, Tsubo Y, Fukai T (2013) Optimal spike-based communication in excitable networks with strong-sparse and weak-dense links. Sci Rep 2:485

    Google Scholar 

  • Touboul J, Brette R (2009) Spiking dynamics of bidimensional integrate-and-fire neurons. SIAM J Appl Dyn Syst 8(4):1462–1506

    Article  Google Scholar 

  • Traub RD, Miles R (1991) Neuronal networks of the hippocampus. Cambridge Univ Press, Cambridge

    Book  Google Scholar 

  • Wagatsuma H, Yamaguchi Y (2007) Neural dynamics of the cognitive map in the hippocampus. Cogn Neurodyn 1(2):119–141

    Article  PubMed Central  PubMed  Google Scholar 

  • Wang XJ, Buzsáki G (1996) Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model. J Neurosci 16(20):6402–6413

    CAS  PubMed  Google Scholar 

  • Yang H, Shew WL, Roy R, Plenz D (2012) Maximal variability of phase synchrony in cortical networks with neuronal avalanches. J Neurosci 32(3):1061–1072

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • 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):1171–1183

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

Y. D. S. was partially supported by a Grant-in-Aid for Challenging Exploring Research No. 25540110. Y. I. was partially supported by the Funding Program for Next Generation World-Leading Researchers (No. LS023).

Author information

Authors and Affiliations

  1. Department of Applied Molecular Bioscience, Graduate School of Medicine, Yamaguchi University, 1-1-1, Minamikogushi, Ube, Yamaguchi, 755-8508, Japan

    Toshikazu Samura

  2. Laboratory of Chemical Pharmacology, Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan

    Yuji Ikegaya

  3. Frankfurt Institute for Advanced Studies (FIAS), Goethe University Frankfurt, Ruth-Moufang-Strasse. 1, Frankfurt am Main, 60438, Germany

    Yasuomi D. Sato

  4. Institute of Industrial Science, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo, 153-8505, Japan

    Yasuomi D. Sato

Authors
  1. Toshikazu Samura
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuji Ikegaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yasuomi D. Sato
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yasuomi D. Sato.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Samura, T., Ikegaya, Y. & Sato, Y.D. A neural network model of reliably optimized spike transmission. Cogn Neurodyn 9, 265–277 (2015). https://doi.org/10.1007/s11571-015-9329-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11571-015-9329-1

Keywords

Search

Navigation