BioMediTech Research Groups

Computational Neuroscience Group

Group Leader: Adjunct Professor Marja-Leena Linne


About Us

The CNS group has a long history of studying neural systems and developing models of biological neural networks, cells and signal transduction pathways. The models developed by us help to explain the different observed types of excitability, activity dynamics and neurotransmission as well as growth, plasticity and learning in the brain. The work promotes understanding of complex dynamic phenomena underlying the structural and functional properties of the brain circuitry in health and disease. We use both detailed biophysical and biochemical (data-driven) and phenomenological modeling approaches while employing a variety of mathematical and computational techniques, not conventionally used in neuroscience.

We are a core member of the EU Future Emerging Flagship (FET) Human Brain Project (, where we contribute to the development of neuroscience infrastructure in theory, simulation and neuromorphic engineering and develop computational models of glial-neuronal interactions.

Research interests and expertise

Our primary research interests are the following:

  • Computational modeling of neural functions, including neurotransmission, information processing, plasticity and learning in neural networks in the brain
  • Development of new mathematical tools for modeling
  • Neural disease mechanisms
  • Brain-inspired computations and technology (neuromorphic devices)

We are a multi-method computational and experimental neuroscience research group, with expertise in:

  • Integration of wet-lab data (e.g. electrophysiology, microscopy) into integrative analysis
  • Electrophysiology (patch-clamp, microelectrode arrays, local field potentials)
  • Computational and theoretical neuroscience
  • Application of mathematical theories in neuroscience
  • Signal processing and image analysis
  • High-performance computing
  • Neuromorphic engineering


We have

  • developed novel mathematical methods using stochastic differential equations to model neural functions,
  • proposed new theoretical framework to assess how neuronal morphology influences neuronal connectivity,
  • evaluated and benchmarked neural simulation software for the benefit of the research community,
  • proposed guidelines and good practices for modeling neuron-glia interactions in the brain, based on our detailed characterization of models,
  • shown for the first time that taurine, an important neuromodulator shaping development of synaptic neurotransmission, is an agonist for GABAA receptor-ion channel, and
  • become core members, through competitive call, in the European Union’s H2020 Future Emerging Technology (FET) Human Brain Project to develop future neuroscience infrastructure.


The Computational Neuroscience research group has members with a variety of backgrounds: cell and molecular biology, neurophysiology, applied mathematics including dynamical systems theory, computer science, signal processing, etc. The group has produced 7 doctoral theses. The group is using high-performance computing provided by Tampere Centre for Scientific Computing and the Human Brain Project Platforms.

Collaboration offer and requests

We are interested to collaborate on scientifically ambitious projects where computational methods are integrated with wet-lab data to understand neural information processing and dynamics, including neuronal excitability, neurotransmission, plasticity and learning.

Major Publications

  1. Manninen T, Havela R, Linne M-L. Reproducibility and comparability of computational models for astrocyte calcium excitability. Frontiers in Neuroinformatics 11:11, 2017.
  2. Aćimović J, Mäki-Marttunen T, Linne M-L. The role of neuroanatomy in shaping network connectivity: Analysis of a two-level statistical model. Frontiers in Neuroanatomy 9:76, 2015.
  3. Teppola H, Sarkanen R, Jalonen TO, Linne M-L. Morphological differentiation towards neuronal phenotype of SH-SY5Y neuroblastoma cells by estradiol, retinoic acid and cholesterol. Neurochemistry Research. 2015 Oct 30.
  4. Hituri K, Linne M-L. Comparison of models for IP3 receptor kinetics using stochastic simulations. PLoS ONE, 8(4): e59618, 2013.
  5. Manninen T, Hituri K, Hellgren-Kotaleski J, Blackwell KT, Linne M-L. Postsynaptic signal transduction models for long-term potentiation and depression. Frontiers in Computational Neuroscience 4:152, 2010.
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