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Cognitive Neurodynamics

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01-06-2015 | Review Paper

A neural network model of reliably optimized spike transmission

Authors: Toshikazu Samura, Yuji Ikegaya, Yasuomi D. Sato

Published in: Cognitive Neurodynamics | Issue 3/2015

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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.

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

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

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):1419–1424CrossRefPubMed

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

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

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):485–502CrossRefPubMedCentralPubMed

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):639–654CrossRefPubMed

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):1729–1751CrossRefPubMed

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):3184–3189CrossRefPubMedCentralPubMed

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):066013CrossRefPubMedCentralPubMed

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

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

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

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):293–304CrossRefPubMed

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

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

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

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):5690–5701CrossRefPubMed

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

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

Klaus A, Yu S, Plenz D (2011) Statistical analyses support power law distributions found in neuronal avalanches. PLoS one 6(5):e19779CrossRefPubMedCentralPubMed

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):39–51CrossRefPubMedCentralPubMed

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

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):301–316CrossRefPubMed

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

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

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

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):16353–16358CrossRefPubMedCentralPubMed

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

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):15595–15600CrossRefPubMedCentralPubMed

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):55–63CrossRefPubMedCentralPubMed

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):2078–2118CrossRefPubMed

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

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):650–672PubMed

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):e68CrossRefPubMedCentralPubMed

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

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 107:10244–10249CrossRefPubMedCentralPubMed

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):985–1003CrossRefPubMed

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):995–1017CrossRefPubMed

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 7:3CrossRefPubMedCentralPubMed

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

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

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

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

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

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

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):1171–1183CrossRefPubMed

Title: A neural network model of reliably optimized spike transmission
Authors: Toshikazu Samura
Yuji Ikegaya
Yasuomi D. Sato
Publication date: 01-06-2015
Publisher: Springer Netherlands
Published in: Cognitive Neurodynamics / Issue 3/2015
Print ISSN: 1871-4080
Electronic ISSN: 1871-4099
DOI: https://doi.org/10.1007/s11571-015-9329-1

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