Research group

Neurophysiology and Neuroevolution Group

The group's objective is to develop an algorithm for building an optimal neural network topology for pattern recognition and classification, clustering, decision-making, prediction, adaptive management, and various other tasks. We plan to achieve this by developing a model in which interacting neurons are treated as living cells.

Developing a system for effective neural network modeling based on available data, without manual adjustments for each specific task, will have a strong impact both on applied and research problems. Such modeling could be helpful in optimizing operating practices in manufacturing plants and improving performance indicators without changing the technological process.

The project was initially launched in December 2013, since May 2014, it has been operating as part of JetBrains Research.

Main research projects

Neural Network Modeling

In the field of biology and medicine, models developed by our group are required, to help to study the neurodegenerative processes leading to Alzheimer's and Parkinson's diseases. A model of schizophrenia development also exists, which is based on dopamine modulation in the prefrontal cortex. Modeling interactions in the central nervous system also has an important part in experimental pharmacology and neurorehabilitation.

The global objectives of the group:

  • Creating a molecular model of neural network functioning, which is as close to reality as possible.
  • Developing a system to emulate evolutionary development of biologically approximate artificial neural network models (based on neurons functioning as living cells). Such a system can be used to choose network models with optimal structure and external parameters for different tasks: pattern recognition, categorization, clustering, forecasting, decision-making, and adaptive management.


  • Simulation examples

    We present a neurogenesis model covering the following biological processes:

    1. Symmetric and asymmetric cell division
    2. Cellular differentiation in accordance with a given differentiation lineage
    3. Factor-dependent axon growth between different types of neurons
    4. Establishment of synaptic connections

    Results of the simulation process:

    1. Individual.xml - snapshot of spatial objects and factor concentrations
    2. AxonLength.csv - axon lengths for each neuron
    3. SpaceSnapshot.csv - axon coordinates
    4. Movements.csv - simulation event log