Institute of Cognitive Science
My background includes both neuroscience and machine learning engineering. I am interested in combining these two, developing models of brain dynamics which can be biologically plausible and computationally efficient at the same time. One example of such exciting combination is neuromorphic computing. Neuromorphic chips are designed to work efficiently with spiking neural networks, which are likened to real brain neuronal populations. I am especially interested in modeling mechanisms of brain plasticity on neuromorphic hardware.
Also, before my PhD, I used to work as a machine learning engineer in the field of natural language processing. Although I am currently not involved in any NLP projects, I am still interested in such topics as language models, speech and text comprehension and generation.
I am currently involved in two interconnected projects. The goal of the first project is to develop a spiking model of V1 brain area, which can demonstrate synchronous neuronal activity in response to visual stimuli with specific geometrical properties. These properties are: visual continuity (e.g. a continuous contour) and orientation similarity (e.g. an image consisting of lines of the same angle). In the brain, such synchrony happens due to horizontal connections between neurons. The crucial component of the model is also the connection matrix between model units, which we define before running the model.
In the second project, the model will be transferred to neuromorphic hardware, and the connection matrix will not be redefined, but rather learned via the plasticity mechanisms between model units.