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Empowering the impaired astrocytes in the tripartite synapses to improve accuracy of pattern recognition

  • Soheila Nazari
  • Karim Faez
Methodologies and Application
  • 14 Downloads

Abstract

Recent finding has demonstrated that glial cells and especially astrocytes are responsible for some important roles in the central nervous system. Laboratory examination of the roles of astrocytes in information processing of neural system is associated with technical difficulties. Therefore, computational modeling provides a suitable approach in describing cognitive phenomena and neuroscience. In this paper, the role of strong and weak astrocytes in pattern recognition has considered and bio-stimulator was used to compensate the malfunction of the weak astrocyte. Therefore, we designed the tripartite synapses network, which astrocytes dominate the synaptic spaces. Then, the network has been trained on MNIST dataset. After that, the control feedback of astrocyte to pre- and postsynaptic neurons has decreased so that the astrocyte dominance on synaptic transitions decreased and impaired tripartite synapses network has been configured. In the next step, due to the destructive effects of impaired astrocyte, based on the dynamical model of the astrocyte biophysical model, a bio-inspired stimulator was designed. Accordingly, a new concept “Stimulating the impaired astrocytes to compensate their malfunction in pattern recognition” was introduced. This new mechanism was proposed to stimulate the impaired astrocytes in a population of tripartite synapses for restoration of the normal neural oscillations. Reported results confirmed that the increased error of “always firing” in pattern recognition caused by astrocytes dysfunction could be compensated by the bio-inspired stimulator.

Keywords

Tripartite synapses network Impaired and strong astrocyte Bio-inspired stimulator Pattern recognition 

Notes

Compliance with ethical standards

Conflict of interest

We do not have any conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. Amiri M, Bahrami F, Janahmadi M (2012a) Functional contributions of astrocytes in synchronization of a neuronal network model. J Theor Biol 292:60–70MathSciNetCrossRefzbMATHGoogle Scholar
  2. Amiri M, Bahrami F, Janahmadi M (2012b) Modified thalamocortical model: a step towards more understanding of the functional contribution of astrocytes to epilepsy. J Comput Neurosci 33(2):285–299MathSciNetCrossRefGoogle Scholar
  3. Amiri M, Bahrami F, Janahmadi M (2012c) On the role of astrocytes in epilepsy: a functional modeling approach. Neurosci Res 72(2):172–180CrossRefzbMATHGoogle Scholar
  4. Amiri M, Hosseinmardi N, Bahrami F, Janahmadi M (2013) Astrocyte-neuron interaction as a mechanism responsible for generation of neural synchrony: a study based on modeling and experiments. J Comput Neurosci 34(3):489–504MathSciNetCrossRefGoogle Scholar
  5. Amiri M, Amiri M, Nazari S, Faez K (2016) A new bio-inspired stimulator to suppress hyper-synchronized neural firing in a cortical network. J Theor Biol 410:107–118CrossRefzbMATHGoogle Scholar
  6. Booth HD, Hirst WD, Wade-Martins R (2017) The role of astrocyte dysfunction in Parkinson’s disease pathogenesis. Trends Neurosci 40(6):358–370CrossRefGoogle Scholar
  7. Chen R, Romero G, Christiansen MG, Mohr A, Anikeeva P (2015) Wireless magnetothermal deep brain stimulation. Science 347(6229):1477–1480CrossRefGoogle Scholar
  8. Clarke LE, Barres BA (2013) Emerging roles of astrocytes in neural circuit development. Nat Rev Neurosci 14(5):311–321CrossRefGoogle Scholar
  9. Darian-Smith C, Gilbert CD (1994) Axonal sprouting accompanies functional reorganization in adult cat striate cortex. Nature 368(6473):737–740CrossRefGoogle Scholar
  10. Diehl PU, Cook M (2015) Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Front Comput Neurosci 9:99CrossRefGoogle Scholar
  11. Dossi E, Vasile F, Rouach N (2018) Human astrocytes in the diseased brain. Brain Res Bull 136:139–156CrossRefGoogle Scholar
  12. Fields RD, Araque A, Johansen-Berg H, Lim S-S, Lynch G, Nave K-A, Nedergaard M, Perez R, Sejnowski T, Wake H (2013) Glial biology in learning and cognition. The Neuroscientist 20(5):426–431CrossRefGoogle Scholar
  13. Halassa MM, Fellin T, Haydon PG (2009a) Tripartite synapses: roles for astrocytic purines in the control of synaptic physiology and behavior. Neuropharmacology 57(4):343–346CrossRefGoogle Scholar
  14. Halassa MM, Fellin T, Haydon PG (2009b) Tripartite synapses: roles for astrocytic purines in the control of synaptic physiology and behavior. Neuropharmacology 57(4):343–346CrossRefGoogle Scholar
  15. Halassa MM, Florian C, Fellin T, Munoz JR, Lee SY, Abel T, Haydon PG, Frank MG (2009c) Astrocytic modulation of sleep homeostasis and cognitive consequences of sleep loss. Neuron 61:213–219CrossRefGoogle Scholar
  16. Henneberger C, Papouin T, Oliet SH, Rusakov DA (2010) Long-term potentiation depends on release of d-serine from astrocytes. Nature 463:232–236CrossRefGoogle Scholar
  17. Hogan JA, Lakey JD (2011) Duration and bandwidth limiting: prolate functions, sampling, and applications. Springer, BerlinzbMATHGoogle Scholar
  18. Hung J, Colicos MA (2008) Astrocytic Ca 2 + waves guide CNS growth cones to remote regions of neuronal activity. PLoS ONE 3(11):1–10CrossRefGoogle Scholar
  19. Izhikevich EM (2003) Simple model of spiking neurons. IEEE Trans Neural Netw 14(6):1569–1572MathSciNetCrossRefGoogle Scholar
  20. Jeon S, Nam Y, Cho S (2009) A neural recording and stimulation technique using passivated electrodes and micro-inductors. In: IEEE Asian solid-state circuits conference, 2009Google Scholar
  21. Ji ZG, Wang H (2015) Optogenetic control of astrocytes: is it possible to treat astrocyte-related epilepsy? Brain Res Bull 110:20–25CrossRefGoogle Scholar
  22. Kudela P, Franaszczuk PJ, Bergey GK (2003) Changing excitation and inhibition in simulated neural networks: effects on induced bursting behavior. Biol Cybern 88(4):276–285CrossRefzbMATHGoogle Scholar
  23. Lewis S (2015) Nanoscale neuronal activation. Nat Rev Neurosci 16(5):247Google Scholar
  24. Li Y, Rinzel J (1994) Equations for inositol-triphosphate receptor-mediated calcium oscillations derived from a detailed kinetic model: a Hodgkin-Huxley like formalism. J Theor Biol 166:461–473CrossRefGoogle Scholar
  25. Linne ML, Jalonen TO (2014) Astrocyte-neuron interactions: from experimental research-based models to translational medicine. Prog Mol Biol Transl Sci 123:191CrossRefGoogle Scholar
  26. Liu D, Yue S (2017) Fast unsupervised learning for visual pattern recognition using spike timing dependent plasticity. Neurocomputing 249:212–224CrossRefGoogle Scholar
  27. Mazzoni A, Panzeri S, Logothetis NK, Brunel N (2008) Encoding of naturalistic stimuli by local field potential spectra in networks of excitatory and inhibitory neurons. PLoS Comput Biol 4(12):1–20MathSciNetCrossRefGoogle Scholar
  28. Montaseri G, Yazdanpanah MJ, Amiri M (2011) Astrocyte-inspired controller design for desynchronization of two coupled limit-cycle oscillators. 2011 third world congress on nature and biologically inspired computingGoogle Scholar
  29. Moran M (2017) A noninvasive novel method of deep brain stimulation in animal model. Neurol Today 17(13):16–19CrossRefGoogle Scholar
  30. Nazari S, Faez K, Janahmadi M (2018) A new approach to detect the coding rule of the cortical spiking model in the information transmission. Neural Netw 99:68–78CrossRefGoogle Scholar
  31. Ooyen AV, Zagolla V, Ulrich C, Schnakenberg U (2009) Pulse-clamp technique for single neuron stimulation electrode characterization. In: Annual international conference of the IEEE engineering in medicine and biology society, 2009Google Scholar
  32. Papa M, De Luca C, Petta F, Alberghina L, Cirillo G (2014) Astrocyte–neuron interplay in maladaptive plasticity. Neurosci Biobehav Rev 42:35–54CrossRefGoogle Scholar
  33. Perea G, Araque A (2005) Properties of synaptically evoked astrocyte calcium signal reveal synaptic information processing by astrocytes. J Neurosci 25(9):2192–2203CrossRefGoogle Scholar
  34. Perea G, Yang A, Boyden ES, Sur M (2014) Optogenetic astrocyte activation modulates response selectivity of visual cortex neurons in vivo. Nat Commun 5(1)Google Scholar
  35. Postnov DE, Koreshkov RN, Brazhe NA, Brazhe AR, Sosnovtseva OV (2009) Dynamical patterns of calcium signaling in a functional model of neuron–astrocyte networks. J Biol Phys 35(4):425–445CrossRefGoogle Scholar
  36. Querlioz D, Bichler O, Dollfus P, Gamrat C (2013) Immunity to device variations in a spiking neural network with memristive nanodevices. IEEE Trans Nanotechnol 12(3):288–295CrossRefGoogle Scholar
  37. Santello M, Volterra A (2009) Synaptic modulation by astrocytes via Ca2+-dependent glutamate release. Neuroscience 158(1):253–259CrossRefGoogle Scholar
  38. Santello M, Calì C, Bezzi P (2012) Gliotransmission and the tripartite synapse. Synaptic Plast 970:307–331CrossRefGoogle Scholar
  39. Schafer DP, Lehrman EK, Kautzman AG, Koyama R, Mardinly AR, Yamasaki R, Stevens B (2012) Microglia sculpt postnatal neural circuits in an activity and complement-dependent manner. Neuron 74(4):691–705CrossRefGoogle Scholar
  40. Seifert G, Steinhäuser C (2013) Neuron–astrocyte signaling and epilepsy. Exp Neurol 244:4–10CrossRefGoogle Scholar
  41. Shepherd JD, Huganir RL (2007) The cell biology of synaptic plasticity: AMPA receptor trafficking. Annu Rev Cell Dev Biol 23(1):613–643CrossRefGoogle Scholar
  42. Skangiel-Kramska J, Głażewski S, Jabłońska B, Siucińska E, Kossut M (1994) Reduction of GABAA receptor binding of [3H]muscimol in the barrel field of mice after peripheral denervation: transient and long-lasting effects. Exp Brain Res 100(1):39–46CrossRefGoogle Scholar
  43. Slepian D (1964) Prolate spheroidal wave functions, Fourier analysis and uncertainty—IV: extensions to many dimensions; generalized prolate spheroidal functions. Bell Syst Tech J 43(6):3009–3057CrossRefzbMATHGoogle Scholar
  44. Slepian D (1978) Prolate spheroidal wave functions, fourier analysis, and uncertainty—V: the discrete case. Bell Labs Tech J 57(5):1371–1430CrossRefzbMATHGoogle Scholar
  45. Slepian D, Pollak H (1961) Prolate spheroidal wave functions, fourier analysis and uncertainty—I. Bell Labs Tech J 40(1):43–63MathSciNetCrossRefzbMATHGoogle Scholar
  46. Tang J, Ma J, Yi M, Xia H, Yang X (2011) Delay and diversity-induced synchronization transitions in a small-world neuronal network. Phys Rev E 83(4):046207–1–046207–6CrossRefGoogle Scholar
  47. Terman D, Rubin JE, Yew AC, Wilson CJ (2002) Activity patterns in a model for the subthalamopallidal network of the basal ganglia. J Neurosci 22(7):2963–2976CrossRefGoogle Scholar
  48. Thompson AC, Stoddart PR, Jansen ED (2015) Optical stimulation of neurons. Curr Mol Imag 3(2):162–177CrossRefGoogle Scholar
  49. Verkhratsky A, Nedergaard M (2014) Astroglial cradle in the life of the synapse. Philos Trans R Soc B: Biol Sci 369(1654):1–9CrossRefGoogle Scholar
  50. Yang X, Liu Y, Long T (2013) Robust non-homogeneity detection algorithm based on prolate spheroidal wave functions for space-time adaptive processing. IET Radar Sonar Navig 7(1):47–54CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical EngineeringAmirkabir University of TechnologyTehranIran
  2. 2.Medical Biology Research CenterKermanshah University of Medical SciencesKermanshahIran

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