The CSNG Lab at the Faculty of Mathematics and Physics at the Charles University  

is seeking a highly motivated Postdoctoral Researcher to join our team to work on

digital twin model of visual system. Funded by the JUNIOR Post-Doc Fund, this 

position offers an exciting opportunity to conduct cutting-edge research at the 

intersection of systems neuroscience, computational modeling and AI.

Project description:

Modern deep-learning techniques have transformed visual neuroscience by substantially improving the ability
of models to predict cortical neuron responses to unseen visual stimuli. However, current deep-learning methods
have two major shortcomings. First, they focus on predicting only the average neural response, failing to capture
the fine temporal dynamics generated within recurrent neural populations. Second, these models rely on standard
"off-the-shelf" architectures optimized for efficient training rather than reflecting the biological substrate under study.
As a result, they function as black boxes, making it difficult to interpret the learned representations in terms of how
visual processing is organized and implemented in biological neural circuits. These limitations hinder the ability of
deep-learning models to provide meaningful insights into the principles governing vision and translate them to 

clinical applications such as brain-machine-interface systems. 

To address these challenges, in this project we will develop novel modular, multi-layer recurrent neural
network (RNN) architectures that directly mirror the architecture of the primary visual cortex. Our models
will establish a one-to-one mapping between individual neurons at different stages of the visual pathway
and their artificial counterparts. They will explicitly incorporate functionally specific lateral recurrent interactions,
excitatory and inhibitory neuronal classes, complex single-neuron transfer functions with adaptive mechanisms,
synaptic depression, and others. We will first train our new RNNs on synthetic data generated by a state-of-the-art
biologically realistic recurrent spiking model of the primary visual cortex developed in our group. After we
establish the proof-of-concept on the synthetic data, we will translate our models to publicly available mouse
and macaque data, as well as additional data from our experimental collaborators.

What do we offer:

We are the Computational Systems Neuroscience Group (CSNG) based at the Faculty of Mathematics
and Physics of Charles University, Prague. The main goal of our group is to identify computations implemented
in the neural system that underlies sensory perception, as well as applying this knowledge to the design of
stimulation protocols for visual prosthetic systems. To that end, we build models of visual systems at various
levels of abstraction using a variety of computational techniques including, but not limited to, machine learning
and large-scale biologically plausible spiking neural network simulations.

The position is fully funded for 2 years, and comes with a salary equivalent to ~2400 EUR/month. We offer a 

dynamic international working environment and collaborations with world-leading experimental labs (Stanford,
University of Pennsylvania, Institute de la Vision Paris etc.).

Candidate profile:

Strong background in modern machine learning techniques. Prior experience with training recurrent 

neural networks, and neuroscience or related disciplines is an advantage, but not strict requirement.

Interested candidates should contact Dr. Ján Antolík (jan.antolik@mff.cuni.cz) with their CV.