Modeling Psycho-Emotional States via Neurosimulation of Monoamine Neurotransmitters

  • Max Talanov
  • Alexey Leukhin
  • Hugo Lövheim
  • Jordi Vallverdú
  • Alexander Toschev
  • Fail Gafarov
Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS, volume 12)


In this paper we present a new computational bio-inspired approach. We use the three-dimensional model of emotions created by the Hugo Lövheim “cube of emotions” and validated it via neurosimulation in NEST. We present a computational model that bridges psycho-emotional states with computational processes as the extension of the model “cube of emotions.” Results of the neurosimulation indicate the incremental influence of dopamine over computational resources used for the computation of a simulation of a psycho-emotional state as well as noradrenaline modulation of the dopamine system, whereas in contrast serotonin decreases the computational resources used to calculate the simulation of a psycho-emotional state. These results indicate the overall correctness of the neuro-mimetic approaches of artificial cognition that not only are feasible but also offer new and unique ways of designing computing architectures with special performing potential.


Affective computing; Affective computation; Spiking neural networks; Bio-inspired cognitive architecture 



The specific researches of Professor Vallverdú are supported by the project “Innovacion epistemológica: el caso de las ciencias biomédicas” (FFI2017-85711-P). The work of Max Talanov, Alexey Leukhin, and Fail Gafarov is supported by the Program of Competitive Growth of KFU and was funded by the subsidy allocated to KFU for the state assignment in the sphere of scientific activities number 2.8303.2017/8.9.


  1. Aldahmash A (2010) Cell numbers in the dorsal and median raphe nuclei of as and AS/AGU rats. Biol Res 21:15–22Google Scholar
  2. Arbib M, Fellous JM (2004) Emotions: from brain to robot. Trends Cogn Sci 8(12):554–559PubMedGoogle Scholar
  3. Berridge KC, Robinson TE (1998) What is the role of dopamine in reward: hedonic impact, reward learning, or incentive salience? Brain Res Rev 28(3):309–369PubMedGoogle Scholar
  4. Birmingham JT, Tauck DL (2003) Neuromodulation in invertebrate sensory systems: from biophysics to behavior. J Exp Biol 206(20):3541–3546. PubMedGoogle Scholar
  5. Bosch-Bouju C, Hyland B, Parr-Brownlie L (2013) Motor thalamus integration of cortical, cerebellar and basal ganglia information: implications for normal and parkinsonian conditions. Front Comput Neurosci 7:163. PubMedPubMedCentralGoogle Scholar
  6. Boussida S, Traoré AS, Durif DF (2017) Mapping of the brain hemodynamic responses to sensorimotor stimulation in a rodent model: a bold FMRI study. PLoS One 12(4):e0176512. PubMedPubMedCentralGoogle Scholar
  7. Bridges MW, Distefano S, Mazzara M, Minlebaev M, Talanov M, Vallverdú J (2015) Towards anthropo-inspired computational systems: the pˆ3 model. In: Jezic, G, Howlett RJ, Jain LC (eds) Smart innovation, systems and technologies, vol 38. Springer, Cham, pp. 311–321. Google Scholar
  8. Cassel JC, Jeltsch H (1995) Serotonergic modulation of cholinergic function in the central nervous system: cognitive implications. Neuroscience 69(1):1–41PubMedGoogle Scholar
  9. Çavdar S, Bay HH, Yıldız SD, Akakın D, Şirvancı S, Onat F (2014) Comparison of numbers of interneurons in three thalamic nuclei of normal and epileptic rats. Neurosci Bull 30(3):451–460. PubMedPubMedCentralGoogle Scholar
  10. Cools R, Nakamura K, Daw ND (2011) Serotonin and dopamine: unifying affective, activational, and decision functions. Neuropsychopharmacology 36(1):98PubMedGoogle Scholar
  11. Counts SE, Mufson EJ (2012) Chapter 12 – locus coeruleus. In: The human nervous system (3rd edn). Academic, San Diego, pp 425–438. Google Scholar
  12. Damasio A (1999) The feeling of what happens: body and emotion in the making of consciousness. Harcourt Inc, New YorkGoogle Scholar
  13. Damasio A (1994) Descartes’ error: emotion, reason and the human brain. Putnam Publishing, New YorkGoogle Scholar
  14. Damasio AR (1998) Emotion in the perspective of an integrated nervous system. Brain Res Rev 26:83–86PubMedGoogle Scholar
  15. DeLancey C (2001) Passionate engines: what emotions reveal about the mind and artificial intelligence. Oxford University Press, New YorkGoogle Scholar
  16. Dudman JT, Gerfen CR (2015) Chapter 17 – the basal ganglia. In: The rat nervous system, 4th edn. Academic, San Diego, pp 391–440. Google Scholar
  17. Durieux P, Schiffmann S, de Kerchove d’Exaerde A (2011) Targeting neuronal populations of the striatum. Front Neuroanat 5:40.
  18. Ebner FF, Kaas JH (2015) Chapter 24 – somatosensory system. In: Paxinos G (ed) The rat nervous system, 4th edn. Academic, San Diego, pp 675–701. Google Scholar
  19. Ekman P (2007) Emotions revealed: recognizing faces and feelings to improve communication and emotional life. Macmillan, New YorkGoogle Scholar
  20. Feldmeyer D (2012) Excitatory neuronal connectivity in the barrel cortex. Front Neuroanat 6:24.
  21. Franzoni V, Milani A, Vallverdú J (2017) Emotional affordances in human-machine interactive planning and negotiation. In: Proceedings of the international conference on web intelligence, WI ’17. ACM, New York, pp 924–930. Google Scholar
  22. Gewaltig MO, Diesmann M (2007) Nest (neural simulation tool). Scholarpedia 2(4):1430Google Scholar
  23. Geyer S, Luppino G, Rozzi S (2012) Chapter 27 – motor cortex. In: Mai JK, Paxinos G (eds) The human nervous system, 3rd edn. Academic, San Diego, pp 1012–1035. Google Scholar
  24. Haber SN, Adler A, Bergman H (2012) Chapter 20 – the basal ganglia. In: Mai JK, Paxinos G (eds) The human nervous system, 3rd edn. Academic, San Diego, pp 678–738. Google Scholar
  25. Haikonen PO (2003) Cognitive approach to conscious machinesGoogle Scholar
  26. Halliday G, Reyes S, Double K (2012) Chapter 13 – substantia nigra, ventral tegmental area, and retrorubral fields. In: Mai JK, Paxinos G (eds) The human nervous system, 3rd edn. Academic, San Diego, pp 439–455. Google Scholar
  27. Hornung JP (2003) The human raphe nuclei and the serotonergic system. J Chem Neuroanat 26(4):331–343. Special Issue on the Human Brain – The Structural Basis for Understanding Human Brain Function and DysfunctionPubMedGoogle Scholar
  28. Hornung JP (2012) Chapter 11 – raphe nuclei. In: Mai JK, Paxinos G (eds) The human nervous system, 3rd edn. Academic, San Diego, pp 401–424. Google Scholar
  29. Jaeger D, Kita H (2011) Functional connectivity and integrative properties of globus pallidus neurons. Neuroscience 198:44–53. Function and dysfunction of the Basal GangliaPubMedPubMedCentralGoogle Scholar
  30. Janhunen S, Ahtee L (2007) Differential nicotinic regulation of the nigrostriatal and mesolimbic dopaminergic pathways: implications for drug development. Neurosci Biobehav Rev 31(3):287–314. PubMedGoogle Scholar
  31. Johard L, Breitwieser L, Meglio AD, Manca M, Mazzara M, Talanov M (2016, Withdrawn) The biodynamo project: a platform for computer simulations of biological dynamics. CoRR abs/1608.01818.
  32. Kaas JH (2012) Chapter 30 – somatosensory system. In: Mai JK, Paxinos G (eds) The human nervous system, 3rd edn. Academic, San Diego, pp 1074–1109. Google Scholar
  33. Kager H, Wadman W, Somjen G (2002) Conditions for the triggering of spreading depression studied with computer simulations. J Neurophysiol 88(5):2700–2712PubMedGoogle Scholar
  34. Koob GF, Le Moal M (2001) Drug addiction, dysregulation of reward, and allostasis. Neuropsychopharmacology 24(2):97PubMedPubMedCentralGoogle Scholar
  35. Koob GF, Arends MA, Moal ML (2014) Chapter 2 – introduction to the neuropsychopharmacology of drug addiction. In: Drugs, addiction, and the brain. Academic, pp 29–63. Google Scholar
  36. Kunkel S, Schenck W (2017) The nest dry-run mode: efficient dynamic analysis of neuronal network simulation code. Front Neuroinform 11:40PubMedPubMedCentralGoogle Scholar
  37. Kuramoto E, Fujiyama F, Nakamura KC, Tanaka Y, Hioki H, Kaneko T (2011) Complementary distribution of glutamatergic cerebellar and GABAergic basal ganglia afferents to the rat motor thalamic nuclei. Eur J Neurosci 33(1):95–109. PubMedGoogle Scholar
  38. Lefort S, Tomm C, Sarria JCF, Petersen CC (2009) The excitatory neuronal network of the c2 barrel column in mouse primary somatosensory cortex. Neuron 61(2):301–316. PubMedGoogle Scholar
  39. Leukhin A, Talanov M, Sozutov I, Vallverdú J, Toschev A (2016) Simulation of a fear-like state on a model of dopamine system of rat brain. In: Samsonovich AV et al (eds) Biologically inspired cognitive architectures (BICA) for young scientists. Springer, Cham pp 121–126Google Scholar
  40. Li Y, Zhong W, Wang D, Feng Q, Liu Z, Zhou J, Jia C, Hu F, Zeng J, Guo Q et al (2016) Serotonin neurons in the dorsal raphe nucleus encode reward signals. Nat Commun 7:10503PubMedPubMedCentralGoogle Scholar
  41. Llinás RR (2001) I of the vortex: from neurons to self, vol 50. MIT Press, Cambridge, MAGoogle Scholar
  42. Lövheim H (2012) A new three-dimensional model for emotions and monoamine neurotransmitters. Med Hypotheses 78(2):341–348PubMedGoogle Scholar
  43. Lübke J, Feldmeyer D (2007) Excitatory signal flow and connectivity in a cortical column: focus on barrel cortex. Brain Struct Funct 212(1):3–17. PubMedGoogle Scholar
  44. Mai JK, Forutan F (2012) Chapter 19 – thalamus. In: Mai JK, Paxinos G (eds) The human nervous system, 3rd edn. Academic, San Diego, pp 618–677. Google Scholar
  45. Marder E (2012) Neuromodulation of neuronal circuits: back to the future. Neuron 76(1):1–11. PubMedPubMedCentralGoogle Scholar
  46. Mayer RE (1999) 22 fifty years of creativity research. Handbook of creativity, vol 449. Cambridge University Press, CambridgeGoogle Scholar
  47. Mazzara M, Rademakers F, Talanov M, Tchitchigin AD (2017) The biodynamo project: experience report. In: Vallverdú J et al (eds) Advanced research on biologically inspired cognitive architectures. Hershey, Pennsylvania, p 117Google Scholar
  48. Minsky M (1988) The society of mind. Simon & Schuster, New YorkGoogle Scholar
  49. Minsky M (2007) The emotion machine: commonsense thinking, artificial intelligence, and the future of the human mind. Simon & Schuster, New YorkGoogle Scholar
  50. Nair-Roberts R, Chatelain-Badie S, Benson E, White-Cooper H, Bolam J, Ungless M (2008) Stereological estimates of dopaminergic, gabaergic and glutamatergic neurons in the ventral tegmental area, substantia nigra and retrorubral field in the rat. Neuroscience 152(4):1024–1031. PubMedPubMedCentralGoogle Scholar
  51. Nakamura K (2013) The role of the dorsal raphé nucleus in reward-seeking behavior. Front Integr Neurosci 7:60. PubMedPubMedCentralGoogle Scholar
  52. Oatley K, Keltner D, Jenkins JM (2006) Understanding emotions. Blackwell Publishing, LondonGoogle Scholar
  53. Oorschot DE (1996) Total number of neurons in the neostriatal, pallidal, subthalamic, and substantia nigral nuclei of the rat basal ganglia: a stereological study using the cavalieri and optical disector methods. J Comp Neurol 366(4):580–599.<580::AID-CNE3>3.0.CO;2-0 PubMedGoogle Scholar
  54. Ordway GA, Schwartz MA, Frazer A (2007) Brain norepinephrine: neurobiology and therapeutics. Cambridge University Press, Cambridge/New York, pp 1–642. Google Scholar
  55. Ortony A, Clore GL, Collins A (1990) The cognitive structure of emotions. Cambridge University Press, CambridgeGoogle Scholar
  56. Panksepp J (2004) Affective neuroscience: the foundations of human and animal emotions. Oxford University Press, OxfordGoogle Scholar
  57. PollakDorocic I, Fürth D, Xuan Y, Johansson Y, Pozzi L, Silberberg G, Carlén M, Meletis K (2014) A whole-brain atlas of inputs to serotonergic neurons of the dorsal and median raphe nuclei. Neuron 83(3):663–678. PubMedGoogle Scholar
  58. Reddy WM (2001) The navigation of feeling: a framework for the history of emotions. Cambridge University Press, CambridgeGoogle Scholar
  59. Rolls ET (2012) Chapter 38 – the emotional systems. In: Mai JK, Paxinos G (eds) The human nervous system, 3rd edn. Academic, San Diego, pp 1328–1350. Google Scholar
  60. Ruhé HG, Mason NS, Schene AH (2007) Mood is indirectly related to serotonin, norepinephrine and dopamine levels in humans: a meta-analysis of monoamine depletion studies. Mol Psychiatry 12(4):331PubMedGoogle Scholar
  61. Samuels ER, Szabadi E (2008) Functional neuroanatomy of the noradrenergic locus coeruleus: its roles in the regulation of arousal and autonomic function part I: principles of functional organisation. Curr Neuropharmacol 6(3):235–253. PubMedPubMedCentralGoogle Scholar
  62. Sara S, Bouret S (2012) Orienting and reorienting: the locus coeruleus mediates cognition through arousal. Neuron 76(1):130–141. PubMedGoogle Scholar
  63. Scherer KR, Schorr A, Johnstone T (2001) Appraisal processes in emotion: theory, methods, research. Oxford University Press, OxfordGoogle Scholar
  64. Schultz W (1998) Predictive reward signal of dopamine neurons. J Neurophysiol 80(1):1–27PubMedGoogle Scholar
  65. Sloman A, Chrisley R (2003) Virtual machines and consciousness. J Conscious Stud 10:133–172Google Scholar
  66. Talanov M, Toschev A (2014) Computational emotional thinking and virtual neurotransmitters. Int J Synth Emot (IJSE) 5(1):1–8Google Scholar
  67. Talanov M, Vallverdu J, Distefano S, Mazzara M, Delhibabu R (2015a) Neuromodulating cognitive architecture: towards biomimetic emotional AI. In: 2015 IEEE 29th international conference on advanced information networking and applications (AINA). IEEE, pp 587–592Google Scholar
  68. Talanov M, Vallverdú J, Distefano S, Mazzara M, Delhibabu R (2015b) Neuromodulating cognitive architecture: towards biomimetic emotional AI. In: Advanced information networking and applications (AINA), pp 587–592. ISSN: 1550–445XGoogle Scholar
  69. Talanov M, Toschev A, Leukhin A (2017a) Modeling the fear-like state in realistic neural network. BioNanoScience 7(2):446–448Google Scholar
  70. Talanov M, Zagulova M, Distefano S, Pinus B, Leukhin A, Vallverdu J (2017b) The implementation of noradrenaline in the neucogar cognitive architecture. In: Proceedings of the ninth international conference on advanced cognitive technologies and applications. IARIA XPS Press, pp 10–15Google Scholar
  71. Talanov M, Zykov E, Gerasimov Y, Toschev A, Erokhin V (2017c) Dopamine modulation via memristive schematic. CoRR abs/1709.06325. COGNITIVE 2018, The Tenth International Conference on Advanced Cognitive Technologies and Applications February 18, 2018 to February 22, 2018 - Barcelona, Spain ISBN: 978-1-61208-609-5
  72. Talanov M, Gafarov F, Vallverdú J, Ostapenko S, Gazizov M, Toschev A, Leukhin A, Distefano S (2018) Simulation of serotonin mechanisms in neucogar cognitive architecture. Procedia Comput Sci 123:473–478. 8th annual international conference on biologically inspired cognitive architectures, BICA 2017 (Eighth annual meeting of the BICA society), held 1–6 Aug 2017 in Moscow. Scholar
  73. Tomkins SS (1984) Affect theory. Approaches Emotion 163:163–195Google Scholar
  74. Toschev A, Talanov M, Kurnosov V (2017) Spiking reasoning system. In: 2017 10th international conference on developments in esystems engineering (DeSE), Paris, France. IEEE, pp 251–55Google Scholar
  75. Uematsu A, Tan BZ, Johansen JP (2015) Projection specificity in heterogeneous locus coeruleus cell populations: implications for learning and memory. Learn Mem 22(9):444–451. Google Scholar
  76. Vallverdú J (2018) Re-embodying cognition with the same “biases”? Int J Eng Fut Technol TM 15(1):23–30Google Scholar
  77. Vallverdú J, Trovato G (2016) Emotional affordances for human–robot interaction. Adapt Behav 24(5):320–334Google Scholar
  78. Vallverdú J, Talanov M, Distefano S, Mazzara M, Tchitchigin A, Nurgaliev I (2015a) A cognitive architecture for the implementation of emotions in computing systems. Biol Inspired Cogn Architect Google Scholar
  79. Vallverdú J, Talanov M, Distefano S, Mazzara M, Tchitchigin A, Nurgaliev I (2015b) A cognitive architecture for the implementation of emotions in computing systems. Biol Inspired Cogn Architect 15:34–40Google Scholar
  80. Vallverdu J, Talanov M, Distefano S, Mazzara M, Manca M, Tchitchigin A (2016) Neucogar: a neuromodulating cognitive architecture for biomimetic emotional AI. Int J Artif Intell 14(1):27–40Google Scholar
  81. Vertes RP, Linley SB, Groenewegen HJ, Witter MP (2015) Chapter 16 – thalamus. In: Paxinos G (ed) The rat nervous system, 4th edn. Academic, San Diego, pp 335–390. Google Scholar
  82. Voigt BC, Brecht M, Houweling AR (2008) Behavioral detectability of single-cell stimulation in the ventral posterior medial nucleus of the thalamus. J Neurosci 28(47):12362–12367. PubMedGoogle Scholar

Copyright information

© Springer Nature Switzerland AG (outside the USA) 2019

Authors and Affiliations

  • Max Talanov
    • 1
  • Alexey Leukhin
    • 1
  • Hugo Lövheim
    • 2
  • Jordi Vallverdú
    • 3
  • Alexander Toschev
    • 1
  • Fail Gafarov
    • 1
  1. 1.Kazan Federal UniversityKazanRussia
  2. 2.Umeå UniversityUmeåSweden
  3. 3.Universitat Autònoma de BarcelonaCataloniaSpain

Personalised recommendations