An Information Theoretic Approach to Stimulus Processing in the Olfactory System

Part of the Lecture Notes in Bioengineering book series (LNBE)


Biological communication and information systems have evolved over millions of years. Although they have been optimized under different design criteria than recent man-made technical communication systems, both are subject to the same information theoretic principles. It is the purpose of this proposal to design manageable channel models which describe information flow and signal processing by cellular and neural entities. In biology, channels are formed by transmitting intertwined chemical and electrical stimuli. A typical, however, still tractable example is the olfactory system of mammals. Mice will be used as a model to explore the basic principles of information exchange between sensory neurons and the brain by information theoretic means. Massive parallelism, optimal quantization, and information fusion will be important challenges to cope with. The final goal of this proposal is twofold. First, biologists will be provided with analytical models to simulate certain aspects of neural processes on a purely numerical basis. Second, the functionality of biological transmission channels will be explored, the basic principles will be isolated and useful features will be carried over to technical communication systems.

Publications Within the Project

  1. Alirezaei G, Mathar R (2015a) Optimum one-bit quantization. In: IEEE information theory workshop (ITW 2015), Jeju Island, Korea, pp 357–361Google Scholar
  2. Alirezaei G, Mathar R (2015b) An upper bound on the capacity of censored channels. In: The 9th international conference on signal processing and communication systems (ICSPCS’15), Australia, Cairns, p 6Google Scholar
  3. Arts M et al (2013) Modelling biological systems using a parallel quantized MIMO channel. In: The tenth international symposium on wireless communication systems (ISWCS 2013), Ilmenau, Germany, pp 385–389Google Scholar
  4. Arts M et al (2016) A discontinuous neural network for non-negative sparse approximation. In: ArXiv e-prints. arXiv:1603.06353 [cs.NE]
  5. Gorin M et al (2016) Interdependent conductances drive infraslow intrinsic rhythmogenesis in a subset of accessory olfactory bulb. J Neurosci 36(11):3127–3144CrossRefGoogle Scholar

Other Publications

  1. Aungst JL et al (2003) Centre–surround inhibition among olfactory bulb glomeruli. Nature 426:623–629CrossRefGoogle Scholar
  2. Balavoine A, Romberg J, Rozell CJ (2012) Convergence and rate analysis of neural networks for sparse approximation. IEEE Trans Neural Netw Learn Syst 23(9):1377–1389CrossRefGoogle Scholar
  3. Ben-Shaul Y et al (2010) In vivo vomeronasal stimulation reveals sensory encoding of conspecific and allospecific cues by the mouse accessory olfactory bulb. Proc Natl Acad Sci U S A 107(11):5172–5177CrossRefGoogle Scholar
  4. Blankenship AG, Feller MB (2009) Mechanisms underlying spontaneous patterned activity in developing neural circuits. Nat Rev Neurosci 11(1):18–29CrossRefGoogle Scholar
  5. Blethyn KL et al (2006) Neuronal basis of the slow (<1 Hz) oscillation in neurons of the nucleus reticularis thalami in vitro. J Neurosci 26(9):2474–2486CrossRefGoogle Scholar
  6. Bouzerdoum A, Pattison TR (1993) Neural network for quadratic optimization with bound constraints. IEEE Trans Neural Netw 4(2):293–304CrossRefGoogle Scholar
  7. Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, New YorkCrossRefMATHGoogle Scholar
  8. Brown CH (2004) Rhythmogenesis in vasopressin cells. J Neuroendocr 16(9):727–739CrossRefGoogle Scholar
  9. Brody CD, Hopfield JJ (2003) Simple networks for spike-timing-based computation, with application to olfactory processing. Neuron 37(5):843–852CrossRefGoogle Scholar
  10. Buzsáki G, Draguhn A (2004) Neuronal oscillations in cortical networks. Science 304(5679):1926–1929CrossRefGoogle Scholar
  11. Bucher D, Taylor AL, Marder E (2006) Central pattern generating neurons simultaneously express fast and slow rhythmic activities in the stomatogastric ganglion. J Neurophysiol 95(6):3617–3632CrossRefGoogle Scholar
  12. Buzsáki G, Logothetis N, Singer W (2013) Scaling brain size, keeping timing: evolutionary preservation of brain rhythms. Neuron 80(3):751–764CrossRefGoogle Scholar
  13. Candès EJ, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52(2):489–509MathSciNetCrossRefMATHGoogle Scholar
  14. Castro JB, Hovis KR, Urban NN (2007) Recurrent dendrodendritic inhibition of accessory olfactory bulb mitral cells requires activation of group I metabotropic glutamate receptors. J Neurosci 27(21):5664–5671CrossRefGoogle Scholar
  15. Chen T-W, Lin B-J, Schild D (2009) Odor coding by modules of coherent mitral/tufted cells in the vertebrate olfactory bulb. Proc Natl Acad Sci U S A 106(7):2401–2406CrossRefGoogle Scholar
  16. Chu Z et al (2012) Two types of burst firing in gonadotrophin-releasing hormone neurones. J Neuroendocr 24(7):1065–1077CrossRefGoogle Scholar
  17. Colwell CS (2011) Linking neural activity and molecular oscillations in the SCN. Nat Rev Neurosci 12(10):553–569CrossRefGoogle Scholar
  18. Cover TM, Thomas JA (2006) Elements of information theory. Telecommunications and signal processing. Wiley, New YorkGoogle Scholar
  19. Crunelli V, Hughes SW (2010) The slow (<1 Hz) rhythm of non-REM sleep: a dialogue between three cardinal oscillators. Nat Neurosci 13(1):9–17CrossRefGoogle Scholar
  20. Cury KM, Uchida N (2010) Robust odor coding via inhalation-coupled transient activity in the mammalian olfactory bulb. Neuron 68(3):570–585CrossRefGoogle Scholar
  21. Del Punta K et al (2002) A divergent pattern of sensory axonal projections is rendered convergent by second-order neurons in the accessory olfactory bulb. Neuron 35(6):1057–1066CrossRefGoogle Scholar
  22. Deco G, Jirsa VK, McIntosh AR (2011) Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat Rev Neurosci 12(1):43–56CrossRefGoogle Scholar
  23. Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306MathSciNetCrossRefMATHGoogle Scholar
  24. Dulac C, Torello AT (2003) Molecular detection of pheromone signals in mammals: from genes to behaviour. Nat Rev Neurosci 4(7):551–562CrossRefGoogle Scholar
  25. Dulac C, Wagner S (2006) Genetic analysis of brain circuits underlying pheromone signaling. Annu Rev Genet 40:449–467CrossRefGoogle Scholar
  26. Ermentrout GB, Galán RF, Urban NN (2008) Reliability, synchrony and noise. Trends Neurosci 31(8):428–434CrossRefGoogle Scholar
  27. Firestein S (2001) How the olfactory system makes sense of scents. Nature 413(6852):211–218CrossRefGoogle Scholar
  28. Fukunaga I et al (2012) Two distinct channels of olfactory bulb output. Neuron 75(2):320–329CrossRefGoogle Scholar
  29. Gao Y, Strowbridge BW (2009) Long-term plasticity of excitatory inputs to granule cells in the rat olfactory bulb. Nat Neurosci 12(6):731–733CrossRefGoogle Scholar
  30. Ganguli S, Sompolinsky H (2012) Compressed sensing, sparsity, and dimensionality in neuronal information processing and data analysis. Annu Rev Neurosci 35:485–508CrossRefGoogle Scholar
  31. Giridhar S, Doiron B, Urban NN (2011) Timescale-dependent shaping of correlation by olfactory bulb lateral inhibition. Proc Natl Acad Sci U S A 108(14):5843–5848CrossRefGoogle Scholar
  32. Grillner S (2006) Biological pattern generation: the cellular and computational logic of networks in motion. Neuron 52(5):751–766CrossRefGoogle Scholar
  33. Gutierrez GJ, O’Leary T, Marder E (2013) Multiple mechanisms switch an electrically coupled, synaptically inhibited neuron between competing rhythmic oscillators. Neuron 77(5):845–858CrossRefGoogle Scholar
  34. Hayar A et al (2004) Olfactory bulb glomeruli: external tufted cells intrinsically burst at theta frequency and are entrained by patterned olfactory input. J Neurosci 24(5):1190–1199CrossRefGoogle Scholar
  35. Hayar A, Shipley MT, Ennis M (2005) Olfactory bulb external tufted cells are synchronized by multiple intraglomerular mechanisms. J Neurosci 25(36):8197–8208CrossRefGoogle Scholar
  36. Hammen GF et al (2014) Functional organization of glomerular maps in the mouse accessory olfactory bulb. Nat Neurosci 1–11Google Scholar
  37. Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci 79(8):2554–2558MathSciNetCrossRefGoogle Scholar
  38. Hopfield JJ (1984) Neurons with graded response have collective computational properties like those of two-state neurons. Proc Natl Acad Sci 81(10):3088–3092CrossRefGoogle Scholar
  39. Hopfield JJ (1995) Pattern recognition computation using action potential timing for stimulus representation. Nature 376(6535):33–36CrossRefGoogle Scholar
  40. Ho N-D, Van Dooren P, Blondel VD (2011) Descent methods for nonnegative matrix factorization. In: Van Dooren P (ed) Numerical linear algebra in signals, systems and control, vol 80. Lecture notes in electrical engineering. Springer, Netherlands, pp 251–293Google Scholar
  41. Hovis KR et al (2012) Activity regulates functional connectivity from the vomeronasal organ to the accessory olfactory bulb. J Neurosci 32(23):7907–7916CrossRefGoogle Scholar
  42. Izhikevich EM et al (2003) Bursts as a unit of neural information: selective communication via resonance. Trends Neurosci 26(3):161–167CrossRefGoogle Scholar
  43. Kay LM et al (2009) Olfactory oscillations: the what, how and what for. Trends Neurosci 32(4):207–214MathSciNetCrossRefGoogle Scholar
  44. Koizumi H, Smith JC (2008) Persistent Na+ and K+-dominated leak currents contribute to respiratory rhythm generation in the pre-Bötzinger complex in vitro. J Neurosci 28(7):1773–1785CrossRefGoogle Scholar
  45. Koulakov AA, Rinberg D (2011) Sparse incomplete representations: a potential role of olfactory granule cells. Neuron 72(1):124–136CrossRefGoogle Scholar
  46. Koulakov AA, Gelperin A, Rinberg D (2007) Olfactory coding with all-or-nothing glomeruli. J Neurophysiol 98(6):3134–3142CrossRefGoogle Scholar
  47. Larriva-Sahd J (2008) The accessory olfactory bulb in the adult rat: a cytological study of its cell types, neuropil, neuronal modules, and interactions with the main olfactory system. J Comp Neurol 510(3):309–350CrossRefGoogle Scholar
  48. Ledoux JE (2000) Emotion circuits in the brain. Annu Rev Neurosci 23:155–184CrossRefGoogle Scholar
  49. Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791CrossRefGoogle Scholar
  50. Leszkowicz E et al (2012) Noradrenaline-induced enhancement of oscillatory local field potentials in the mouse accessory olfactory bulb does not depend on disinhibition of mitral cells. Eur J Neurosci 1–13Google Scholar
  51. Lin DY et al (2005) Encoding social signals in the mouse main olfactory bulb. Nature 434(7032):470–477CrossRefGoogle Scholar
  52. Lin DY, Shea SD, Katz LC (2006) Representation of natural stimuli in the rodent main olfactory bulb. Neuron 50(6):937–949CrossRefGoogle Scholar
  53. Ma J, Lowe G (2004) Action potential backpropagation and multiglomerular signaling in the rat vomeronasal system. J Neurosci 24(42):9341–9352CrossRefGoogle Scholar
  54. Mathar R, Schmeink A (2011a) A bio-inspired approach to condensing information. In: IEEE international symposium on information theory (ISIT), Saint-Petersburg, pp 2524–2528Google Scholar
  55. Mathar R, Schmeink A (2011b) Cooperative detection over multiple parallel channels: a principle inspired by nature. In: IEEE international symposium on personal, indoor and mobile radio communications (PIMRC), Toronto, Canada, pp 1768–1772Google Scholar
  56. Mathar R, Dörpinghaus M (2013) Threshold optimization for capacity achieving discrete input one-bit output quantization. In: 2013 IEEE international symposium on information theory proceedings (ISIT), pp 1999–2003Google Scholar
  57. Maier N et al (2011) Coherent phasic excitation during hippocampal ripples. Neuron 72(1):137–152CrossRefGoogle Scholar
  58. McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133MathSciNetCrossRefMATHGoogle Scholar
  59. McDonnell MD, Stocks NG, Abbott D (2007) Optimal stimulus and noise distributions for information transmission via suprathreshold stochastic resonance. Phys Rev E 75(6):061105MathSciNetCrossRefGoogle Scholar
  60. McDonnell MD, Amblard P-O, Stocks NG (2009) Stochastic pooling networks. J Stat Mech Theory Exp 2009(01):P01012CrossRefGoogle Scholar
  61. McDonnell MD, Amblard P-O, Stocks NG (2010) Bio-inspired communication: performance limits for information transmission and compression in stochastic pooling networks with binary quantizing nodes. J Comput Theor Nanosci 7(5):876–883CrossRefGoogle Scholar
  62. Mizuseki K et al (2009) Theta oscillations provide temporalwindows for local circuit computation in the entorhinal-hippocampal loop. Neuron 64(2):267–280CrossRefGoogle Scholar
  63. Miura K, Mainen ZF, Uchida N (2012) Odor representations in olfactory cortex: distributed rate coding and decorrelated population activity. Neuron 74(6):1087–1098CrossRefGoogle Scholar
  64. Mombaerts P (2004) Genes and ligands for odorant, vomeronasal and taste receptors. Nat Rev Neurosci 5(4):263–278CrossRefGoogle Scholar
  65. Mombaerts P et al (1996) Visualizing an olfactory sensory map. Cell 87(4):675–686CrossRefGoogle Scholar
  66. Murphy GJ, Darcy DP, Isaacson JS (2005) Intraglomerular inhibition: signaling mechanisms of an olfactory microcircuit. Nat Neurosci 8(3):354–364CrossRefGoogle Scholar
  67. Peña F et al (2004) Differential contribution of pacemaker properties to the generation of respiratory rhythms during normoxia and hypoxia. Neuron 43(1):105–117CrossRefGoogle Scholar
  68. Ressler KJ, Sullivan SL, Buck LB (1994) Information coding in the olfactory system: evidence for a stereotyped and highly organized epitope map in the olfactory bulb. Cell 79(7):1245–1255CrossRefGoogle Scholar
  69. Rieke F, de Ruyter van Steveninck R, Bialek W (1997) Spikes: exploring the neural code. In: Sejnowski TJ, Poggio TA (eds). The MIT Press, CambridgeGoogle Scholar
  70. Rozell CJ et al (2008) Sparse coding via thresholding and local competition in neural circuits. Neural Comput 20(10):2526–2563MathSciNetCrossRefGoogle Scholar
  71. Romano SA et al (2015) Spontaneous neuronal network dynamics reveal circuit’s functional adaptations for behavior. Neuron 1070–1085Google Scholar
  72. Schoppa NE, Urban NN (2003) Dendritic processing within olfactory bulb circuits. Trends Neurosci 26(9):501–506CrossRefGoogle Scholar
  73. Schroeder CE, Lakatos P (2009) Low-frequency neuronal oscillations as instruments of sensory selection. Trends Neurosci 32(1):9–18CrossRefGoogle Scholar
  74. Shadlen MN, Newsome WT (1998) The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J Neurosci 18(10):3870–3896Google Scholar
  75. Shepherd GM, Chen WR, Greer CA (2004) Olfactory bulb. In: Shepherd GM (ed) The synaptic organization of the brain. Oxford University Press, New York, pp 165–216CrossRefGoogle Scholar
  76. Shao Z et al (2009) Two GABAergic intraglomerular circuits differentially regulate tonic and phasic presynaptic inhibition of olfactory nerve terminals. J Neurophysiol 101(4):1988–2001CrossRefGoogle Scholar
  77. Shusterman R et al (2011) Precise olfactory responses tile the sniff cycle. Nat Neurosci 14(8):1039–1044CrossRefGoogle Scholar
  78. Shapero S et al (2012) Low power sparse approximation on reconfigurable analog hardware. IEEE J Emerg Sel Top Circuits Syst 2(3):530–541CrossRefGoogle Scholar
  79. Shpak G et al (2012) Calcium-activated sustained firing responses distinguish accessory from main olfactory bulb mitral cells. J Neurosci 32(18):6251–6262CrossRefGoogle Scholar
  80. Singer W (1999) Time as coding space? Curr Opin Neurobiol 9(2):189–194MathSciNetCrossRefGoogle Scholar
  81. Simerly RB (2002) Wired for reproduction: organization and development of sexually dimorphic circuits in the mammalian forebrain. Annu Rev Neurosci 25:507–536CrossRefGoogle Scholar
  82. Slawski M, Hein M (2011) Sparse recovery by thresholded non-negative least squares. In: Shawe-Taylor J et al (ed) Advances in neural information processing systems 24, pp 1926–1934Google Scholar
  83. Smith RS, Araneda RC (2010) Cholinergic modulation of neuronal excitability in the accessory olfactory bulb. J Neurophysiol 104(6):2963–2974CrossRefGoogle Scholar
  84. Smear M et al (2011) Perception of sniff phase in mouse olfaction. Nature 479(7373):397–400CrossRefGoogle Scholar
  85. Stocks NG (2000) Suprathreshold stochastic resonance in multilevel threshold systems. Phys Rev Lett 84(11):2310–2313CrossRefGoogle Scholar
  86. Strotmann J (2001) Targeting of olfactory neurons. Cell Mol Life Sci CMLS 58:531–537CrossRefGoogle Scholar
  87. Stocks NG (2001a) Suprathreshold stochastic resonance: an exact result for uniformly distributed signal and noise. Phys Lett A 279(5–6):308–312Google Scholar
  88. Stocks NG (2001b) Information transmission in parallel threshold arrays: suprathreshold stochastic resonance. Phys Rev E 63(4):041114Google Scholar
  89. Stowers L, Logan DW (2010) Sexual dimorphism in olfactory signaling. Curr Opin Neurobiol 20(6):770–775CrossRefGoogle Scholar
  90. Stein RB, Gossen ER, Jones KE (2005) Neuronal variability: noise or part of the signal? Nat Rev Neurosci 6(5):389–397CrossRefGoogle Scholar
  91. Spehr M et al (2006) Parallel processing of social signals by the mammalian main and accessory olfactory systems. Cell Mol Life Sci CMLS 63(13):1476–1484CrossRefGoogle Scholar
  92. Sugai T et al (2005) Developmental changes in oscillatory and slow responses of the rat accessory olfactory bulb. Neuroscience 134(2):605–616MathSciNetCrossRefGoogle Scholar
  93. Tank D, Hopfield JJ (1986) Simple ‘neural’ optimization networks: an A/D converter, signal decision circuit, and a linear programming circuit. IEEE Trans Circuits Syst 33(5):533–541CrossRefGoogle Scholar
  94. Tazerart S, Vinay L, Brocard F (2008) The persistent sodium current generates pacemaker activities in the central pattern generator for locomotion and regulates the locomotor rhythm. J Neurosci 28(34):8577–8589CrossRefGoogle Scholar
  95. Touhara K, Vosshall LB (2009) Sensing odorants and pheromones with chemosensory receptors. Annu Rev Physiol 71:307–332CrossRefGoogle Scholar
  96. Tolokh II, Fu X, Holy TE (2013) Reliable sex and strain discrimination in the mouse vomeronasal organ and accessory olfactory bulb. J Neurosci 33(34):13903–13913CrossRefGoogle Scholar
  97. Vassar R et al (1994) Topographic organization of sensory projection to the olfactory bulb. Cell 79:981–991CrossRefGoogle Scholar
  98. Watta PB, Hassoun MH (1996) A coupled gradient network approach for static and temporal mixed-integer optimization. IEEE Trans Neural Netw 7(3):578–593CrossRefGoogle Scholar
  99. Wachowiak M, Shipley MT (2006) Coding and synaptic processing of sensory information in the glomerular layer of the olfactory bulb. Semin Cell Dev Biol 17(4):411–423CrossRefGoogle Scholar
  100. Wang F et al (1998) Odorant receptors govern the formation of a precise topographic map. Cell 93:47–60CrossRefGoogle Scholar
  101. Wachowiak M, Denk W, Friedrich RW (2004) Functional organization of sensory input to the olfactory bulb glomerulus analyzed by two-photon calcium imaging. Proc Natl Acad Sci U S A 101(24):9097–9102CrossRefGoogle Scholar
  102. Wagner S et al (2006) A multireceptor genetic approach uncovers an ordered integration of VNO sensory inputs in the accessory olfactory bulb. Neuron 50(5):697–709CrossRefGoogle Scholar
  103. Wen U-P, Lan K-M, Shih H-S (2009) A review of Hopfield neural networks for solving mathematical programming problems. Eur J Oper Res 198(3):675–687MathSciNetCrossRefMATHGoogle Scholar
  104. Zozor S, Amblard P-O, Duchêne C (2007) On pooling networks and fluctuation in suboptimal detection framework. Fluct Noise Lett 7(01):L39–L60CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Institute for Theoretical Information TechnologyRWTH Aachen UniversityAachenGermany
  2. 2.Department of Chemosensorik, Institute for Biology IIRWTH Aachen UniversityAachenGermany

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