The aim of our lab is to provide updated information how neural networks work synchronously in a large spatiotemporal pattern.
Long-term research area: Development of multimodal strategy to induce directed neuroblast migration and integration into functional circuits of peri-infarct zone
Short-term research areas:
- Analysis of neural networks with electrophysiological recordings.
- Two-photon microscopy imaging of neural activity on both awake and behaving animals.
Two-photon microscopy Ca transient imaging is used to analyze neural network firing alterations in behaving animals and spike detection analysis using customized programs.
- Establishment of advanced optogenetics methods.
Priority of our group is to deal with cutting edge methodologies and fill our arsenal of methods with advanced optical parts.
- Neural network analysis using complex behavioral tasks on awake animals.
We construct miniature imaging systems, that can be attached to behaving animals and let them explore the environment freely, thus reducing disadvantages of traditional head-fixed imaging methods.
- Implementation of “Brain machine interface” module.
Reason of interest in LFPs is that they provide stable signal for a longer period and therefore, are useful for long‐term chronic experiments and for clinical applications such as Brain Machine Interfaces (BMI). However, as LFP captures a multitude of neural processes, the extraction of essential features is among the main problems of Computational Neuroscience. The investigation of neural activity underlying cognitive functions requires recording of activity in several brain regions and analytical methods that will give an opportunity to combine multiple features of spatially and temporally separated complex signals to achieve a robust classification of brain states. The extraction of cognitive functions requires complex processing of information. Deep learning methods can fill the missing links between recorded data and causal behavior of neural populations and serve to advance the classification of brain states. Our project aims to record and analyze brain activity with further development of analysis tools via brain stimulation. This will lead to more detailed and deep understanding of brain cognitive functions, which will help in the development of Brain Machine Interfaces. During the project realization the methods of registration of LFP and the data analyzing algorithms will be developed.