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Modeling Neuron-Astrocyte Interactions: Towards Understanding Synaptic Plasticity and Learning in the Brain

Part of the Lecture Notes in Computer Science book series (LNISA,volume 10362)

Abstract

Spiking neural networks represent a third generation of artificial neural networks and are inspired by computational principles of neurons and synapses in the brain. In addition to neuronal mechanisms, astrocytic signaling can influence information transmission, plasticity and learning in the brain. In this study, we developed a new computational model to better understand the dynamics of mechanisms that lead to changes in information processing between a postsynaptic neuron and an astrocyte. We used a classical stimulation protocol of long-term plasticity to test the model functionality. The long-term goal of our work is to develop extended synapse models including neuron-astrocyte interactions to address plasticity and learning in cortical synapses. Our modeling studies will advance the development of novel learning algorithms to be used in the extended synapse models and spiking neural networks. The novel algorithms can provide a basis for artificial intelligence systems that can emulate the functionality of mammalian brain.

Keywords

  • Astrocyte
  • Neuron
  • Calcium
  • Computational model
  • Synaptic plasticity
  • Learning

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Acknowledgments

The authors wish to thank Tampere University of Technology Graduate School, Emil Aaltonen Foundation, The Finnish Concordia Fund, and Ulla Tuominen Foundation for support for RH.

Funding

Funding.

This project received funding from the European Union Seventh Framework Programme (FP7) under grant agreement No. 604102 (HBP), European Union’s Horizon 2020 research and innovation programme under grant agreement No. 720270, and Academy of Finland (decision No. 297893).

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Correspondence to Marja-Leena Linne .

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Havela, R., Manninen, T., Saudargiene, A., Linne, ML. (2017). Modeling Neuron-Astrocyte Interactions: Towards Understanding Synaptic Plasticity and Learning in the Brain. In: Huang, DS., Jo, KH., Figueroa-García, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_14

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  • DOI: https://doi.org/10.1007/978-3-319-63312-1_14

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