Skip to main content

Sequence Learning in Associative Neuronal-Astrocytic Networks

  • Conference paper
  • First Online:
Brain Informatics (BI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12241))

Included in the following conference series:

Abstract

The neuronal paradigm of studying the brain has left us with limitations in both our understanding of how neurons process information to achieve biological intelligence and how such knowledge may be translated into artificial intelligence and even its most brain-derived branch, neuromorphic computing. Overturning our assumptions of how the brain works, the recent exploration of astrocytes reveals how these long-neglected brain cells dynamically regulate learning by interacting with neuronal activity at the synaptic level. Following recent experimental studies, we designed an associative, Hopfield-type, neuronal-astrocytic network and analyzed the dynamics of the interaction between neurons and astrocytes. We show how astrocytes were sufficient to trigger transitions between learned memories in the network and derived the timing of these transitions based on the dynamics of the calcium-dependent slow-currents in the astrocytic processes. We further evaluated the proposed brain-morphic mechanism for sequence learning by emulating astrocytic atrophy. We show that memory recall became largely impaired after a critical point of affected astrocytes was reached. These results support our ongoing efforts to harness the computational power of non-neuronal elements for neuromorphic information processing.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Adamsky, A., Kol, A., Kreisel, T., et al.: Astrocytic activation generates de novo neuronal potentiation and memory enhancement. Cell 174(1), 59–71 (2018)

    Article  Google Scholar 

  2. Araque, A., Parpura, V., Sanzgiri, R.P., Haydon, P.G.: Tripartite synapses: glia, the unacknowledged partner. Trends in neurosciences 22(5), 208–215 (1999)

    Article  Google Scholar 

  3. Barres, B.A.: The mystery and magic of glia: a perspective on their roles in health and disease. Neuron 60(3), 430–440 (2008)

    Article  Google Scholar 

  4. Bazargani, N., Attwell, D.: Astrocyte calcium signaling: the third wave. Nature Neuroscience 19(2), 182–189 (2016)

    Article  Google Scholar 

  5. Blum, H., Dietmüller, A., et al.: A neuromorphic controller for a robotic vehicle equipped with a dynamic vision sensor. In: Robotics: Science and Systems (2018)

    Google Scholar 

  6. Chaudhuri, R., Fiete, I.: Computational principles of memory. Nature Neuroscience 19(3), 394–403 (2016)

    Article  Google Scholar 

  7. Chung, W.S., Welsh, C.A., Barres, B.A., Stevens, B.: Do glia drive synaptic and cognitive impairment in disease? Nature Neuroscience 18(11), 1539–1545 (2015)

    Article  Google Scholar 

  8. Cossart, R., Aronov, D., Yuste, R.: Attractor dynamics of network up states in the neocortex. Nature 423(6937), 283–288 (2003)

    Article  Google Scholar 

  9. Davies, M., Srinivasa, N., Lin, T.H., Chinya, G., et al.: Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro 38(1), 82–99 (2018)

    Article  Google Scholar 

  10. De Pittà, M., Brunel, N., Volterra, A.: Astrocytes: Orchestrating synaptic plasticity? Neuroscience 323, 43–61 (2016)

    Article  Google Scholar 

  11. Fellin, T., Pascual, O., et al.: Neuronal synchrony mediated by astrocytic glutamate through activation of extrasynaptic nmda receptors. Neuron 43(5), 729–743 (2004)

    Article  Google Scholar 

  12. Fields, R.D., Araque, A., Johansen-Berg, H., Lim, S.S., Lynch, G., et al.: Glial biology in learning and cognition. The neuroscientist 20(5), 426–431 (2014)

    Article  Google Scholar 

  13. Furber, S.B., Galluppi, F., Temple, S., Plana, L.A.: The spinnaker project. Proceedings of the IEEE 102(5), 652–665 (2014)

    Article  Google Scholar 

  14. Goldberg, M., De Pittà, M., et al.: Nonlinear gap junctions enable long-distance propagation of pulsating calcium waves in astrocyte networks. PLoS Comput Biol 6(8), e1000909 (2010)

    Google Scholar 

  15. Halassa, M.M., Fellin, T., Takano, H., et al.: Synaptic islands defined by the territory of a single astrocyte. Journal of Neuroscience 27(24), 6473–6477 (2007)

    Article  Google Scholar 

  16. Han, X., et al.: Forebrain engraftment by human glial progenitor cells enhances synaptic plasticity and learning in adult mice. Cell stem cell 12(3), 342–353 (2013)

    Article  MathSciNet  Google Scholar 

  17. Hebb, D.O.: The organization of behavior: A neuropsychological theory. Psychology Press (2005)

    Google Scholar 

  18. Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. PNAS 79(8), 2554–2558 (1982)

    Article  MathSciNet  Google Scholar 

  19. Kleinfeld, D., Sompolinsky, H.: Associative neural network model for the generation of temporal patterns. theory and application to central pattern generators. Biophysical Journal 54(6), 1039–1051 (1988)

    Google Scholar 

  20. Lind, B.L., et al.: Rapid stimulus-evoked astrocyte ca2+ elevations and hemodynamic responses in mouse somatosensory cortex in vivo. PNAS 110(48), E4678–E4687 (2013)

    Article  Google Scholar 

  21. Maass, W.: Networks of spiking neurons: the third generation of neural network models. Neural Networks 10(9), 1659–1671 (1997)

    Article  Google Scholar 

  22. McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics 5(4), 115–133 (1943)

    Article  MathSciNet  Google Scholar 

  23. McNaughton, B.L., Battaglia, F.P., et al.: Path integration and the neural basis of the’cognitive map’. Nature Reviews Neuroscience 7(8), 663–678 (2006)

    Article  Google Scholar 

  24. Pál, B.: Astrocytic actions on extrasynaptic neuronal currents. Frontiers in cellular neuroscience 9 (2015)

    Google Scholar 

  25. Papernot, N., McDaniel, P., Jha, S., et al.: The limitations of deep learning in adversarial settings. In: IEEE EuroS&P. pp. 372–387. IEEE (2016)

    Google Scholar 

  26. Parpura, V., Haydon, P.G.: Physiological astrocytic calcium levels stimulate glutamate release to modulate adjacent neurons. PNAS 97(15), 8629–8634 (2000)

    Article  Google Scholar 

  27. Polykretis, I., Ivanov, V., Michmizos, K.P.: The astrocytic microdomain as a generative mechanism for local plasticity. In: International Conference on Brain Informatics. pp. 153–162. Springer (2018)

    Google Scholar 

  28. Polykretis, I., Ivanov, V., Michmizos, K.P.: A neural-astrocytic network architecture: Astrocytic calcium waves modulate synchronous neuronal activity. In: ACM Proceedings of 2018 ICONS. pp. 1–8 (2018)

    Google Scholar 

  29. Polykretis, I.E., Ivanov, V.A., Michmizos, K.P.: Computational astrocyence: Astrocytes encode inhibitory activity into the frequency and spatial extent of their calcium elevations. In: 2019 IEEE EMBS BHI. pp. 1–4. IEEE (2019)

    Google Scholar 

  30. Rosenfeld, A., Zemel, R., Tsotsos, J.K.: The elephant in the room. arXiv preprint arXiv:1808.03305 (2018)

  31. Sejnowski, T.J., Churchland, P.S., Movshon, J.A.: Putting big data to good use in neuroscience. Nature Neuroscience 17(11), 1440 (2014)

    Article  Google Scholar 

  32. Sompolinsky, H., Kanter, I.: Temporal association in asymmetric neural networks. Physical review letters 57(22), 2861 (1986)

    Article  Google Scholar 

  33. Su, J., Vargas, D.V., Sakurai, K.: One pixel attack for fooling deep neural networks. IEEE Trans. on Evolutionary Computation (2019)

    Google Scholar 

  34. Tang, G., Kumar, N., Michmizos, K.P.: Reinforcement co-learning of deep and spiking neural networks for energy-efficient mapless navigation with neuromorphic hardware. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 1–8 (2020)

    Google Scholar 

  35. Tang, G., Polykretis, I.E., Ivanov, V.A., Shah, A., Michmizos, K.P.: Introducing astrocytes on a neuromorphic processor: Synchronization, local plasticity and edge of chaos. ACM Proceedings of 2019 NICE 1(1), 1–10 (2019)

    Google Scholar 

  36. Tang, G., Shah, A., Michmizos, K.P.: Spiking neural network on neuromorphic hardware for energy-efficient unidimensional slam. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 4176–81 (2019)

    Google Scholar 

  37. Tavanaei, A., Ghodrati, M., Kheradpisheh, S.R., Masquelier, T., Maida, A.: Deep learning in spiking neural networks. Neural Networks (2018)

    Google Scholar 

  38. Theodosis, D.T., et al.: Activity-dependent structural and functional plasticity of astrocyte-neuron interactions. Physiological reviews 88(3), 983–1008 (2008)

    Article  Google Scholar 

  39. Turing, A.M.: Intelligent machinery, a heretical theory. The Turing Test: Verbal Behavior as the Hallmark of Intelligence 105 (1948)

    Google Scholar 

  40. Verkhratsky, A., Olabarria, M., Noristani, H.N., Yeh, C.Y., Rodriguez, J.J.: Astrocytes in alzheimer’s disease. Neurotherapeutics 7(4), 399–412 (2010)

    Article  Google Scholar 

  41. Volterra, A., Meldolesi, J.: Astrocytes, from brain glue to communication elements: the revolution continues. Nature Reviews Neuroscience 6(8), 626 (2005)

    Article  Google Scholar 

  42. Wade, J.J., McDaid, L.J., Harkin, J., Crunelli, V., Kelso, J.S.: Bidirectional coupling between astrocytes and neurons mediates learning and dynamic coordination in the brain: a multiple modeling approach. PloS one 6(12), e29445 (2011)

    Article  Google Scholar 

  43. Wimmer, K., Nykamp, D.Q., Constantinidis, C., Compte, A.: Bump attractor dynamics in prefrontal cortex explains behavioral precision in spatial working memory. Nature Neuroscience 17(3), 431–439 (2014)

    Article  Google Scholar 

  44. Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning requires rethinking generalization. arXiv preprint arXiv:1611.03530 (2016)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Konstantinos P. Michmizos .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kozachkov, L., Michmizos, K.P. (2020). Sequence Learning in Associative Neuronal-Astrocytic Networks. In: Mahmud, M., Vassanelli, S., Kaiser, M.S., Zhong, N. (eds) Brain Informatics. BI 2020. Lecture Notes in Computer Science(), vol 12241. Springer, Cham. https://doi.org/10.1007/978-3-030-59277-6_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-59277-6_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59276-9

  • Online ISBN: 978-3-030-59277-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics