PhD candidate
Research group | Section for Physiology and Cell Biology
Main supervisor | Gaute Einevoll
Co-supervisor | Marianne Fyhn
Affiliation | Department of Biosciences, UiO
Contact | constb@ibv.uio.no
Short bio
After graduating with a Bachelor degree in psychology from the University of Sofia, Bulgaria, I decided to take a more quantitative turn and did a Masters in Cognitive Neuroscience at Radboud University, the Netherlands. There I wrote a thesis on the topic “Topological Properties of Neural Manifolds”, in which I studied the topology generated by the activity of feature selective neurons in neural networks. Now I am hoping to understand how the brain (at least the mouse one) performs visual computation by combining theory, biophysical modeling and machine learning.
Research interests and hobbies
My main research interests are in neural manifolds, neural coding, machine learning and topological data analysis. As for hobbies, I like to play music, read books and hike.
CompSci project
Large-scale network simulations of mouse visual cortex
My project at UiO involves simulating both biophysical (Billeh et al., Neuron, 2020) and artificial (Perich et al., bioRxiv, 2021) neural network models of mouse visual cortex in order to gain a deeper understanding of the visual system. Besides modelling I will also develop machine learning algorithms with which to improve the aforementioned models by making them reproduce population based measurements like the local field potential (LFP). Finally I will use the improved models as an in-silico study case with which to answer questions about neural representation, manifolds and coding.
Publications
CompSci publications
- Kosio Beshkov, Jonas Verhellen and Mikkel Elle Lepperød (2022) “Isometric Representations in Neural Networks Improve Robustness”; Preprint
This paper studies the benefits of using an isometric loss term in neural networks. We show that adding such a loss term in classification problems leads to representations which are isometric within class and highly separated between classes. We show that this approach is robust to adversarial attacks and describe some of its applications to hierarchical dimensionality reduction.
https://arxiv.org/abs/2211.01236 | Full text in Research Archive - Kosio Beshkov, Marianne Fyhn, Torkel Hafting and Gaute Einevoll (2024) “Topological structure of population activity in mouse visual cortex encodes densely sampled stimulus rotations” iScience 27 (4) 109370
This paper studies the topology of neural activity in response to rotating images in primary mouse visual cortex. We modified the persistent homology algorithm to use approximations of geodesic distances. This made it possible to discover circle-like manifolds in neural activity in response to rotating visual stimuli. Furthermore we discuss in detail, what type of experiments are needed in order to discover the topology of neural manifolds in new tasks.
https://doi.org/10.1016/j.isci.2024.109370 | Full text in Research Archive - Jonas Verhellen, Kosio Beshkov , Sebastian Amundsen, Torbjørn Vefferstad Ness and Gaute Tomas Einevoll (2023) “Multitask Learning of Biophysically-Detailed Neuron Models”; Preprint
https://www.biorxiv.org/content/10.1101/2023.12.05.570220v1 | Full text in Research Archive
Previous publications
- Beshkov, K., & Tiesinga, P. (2021) "Geodesic-based distance reveals non-linear topological features in neural activity from mouse visual cortex" Biological Cybernetics 116, 53-68
https://link.springer.com/article/10.1007/s00422-021-00906-5
