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Available Theses & Projects

Computational Neurology

Brain-Derived Semantic Maps for Personalizing Large Language Models

This project explores a new direction for brain–computer interfaces: using neural responses not only to detect intentions, but also to estimate how object meaning is represented in the brain and to enrich human–AI interaction with this information. The student will work with open EEG, iEEG and other neuroimaging datasets in which participants viewed images of objects or words. Representational similarity analysis and other multivariate approaches, combined with behavioral data analysis, will be used to construct brain-derived semantic maps, and then translate them into a format usable by an AI agent.

Computational Neuroscience

Modeling Memory Mechanisms in Spatial Navigation

Spatial navigation is crucial for survival and reproduction, relying heavily on memory to recall important locations. This project integrates neuroscience and machine learning, using reinforcement learning (RL) to model how artificial agents navigate and form memories in simulated environments. By comparing these processes to biological systems, we aim to understand how memories are encoded, stored, and retrieved.

Replay Dynamics and the role of hippocampal subregion CA2

Our life is full of experiences of which we remember only few. For an experience to be remembered, corresponding sequences of neural activity have to be replayed in the hippocampus during so called sharp wave-ripples. But how is it decided which experiences are to be remembered and which ones are not? We hypothesize that this may - at least in part - be a self-organized process within the hippocampus, with multiple sequences simultaneously becoming active at the beginning of a sharp wave-ripple and then competing for their reactivation. In this project you will be searching for fingerprints of such competition dynamics with state-of-the-art tools from replay analysis on data provided from our collaborators at the McHugh lab at Riken Center for Brain Sciences, Japan. This project can be extended to a Ph.D. thesis within Computational Neuroscience. For example, in our collaborators' data you may dive deeper by investigating how hippocampal subregion CA2 influences such competition dynamics, as we have previously postulated (Stöber et al, 2019, Hippocampus). To excel in this project, you should be able to program, have some experience in handling data, and should not be scared of formulas. If this fits you, do not hesitate to get in touch with us.

Temporal Organization of Associative Learning: Bridging Millisecond-Scale Neural Learning Rules and Behavioral Timescales in a Spatial Navigation Model

The main objective is to study the generation of associative neuronal sequences in the context of spatial learning tasks and navigation. We aim to provide biologically plausible models explaining how spatial information is encoded, stored, and retrieved in the brain. In particular, the consolidation of existing temporal associative sequences and the formation of new sequences through the mechanism of replay is the primary focus of this project.

The role of replay in learning and memory

We offer projects at bachelor and master level focused on the development and use of RL algorithms driven by experience replay to model memory and dynamics of learning in both spatial and non-spatial settings. All projects are conducted using CoBeL-RL, which is a neuroscience-oriented simulation framework written in Python that was developed by the Computational Neuroscience group.

Neural Data Science

Cognitive maps in Artificial Intelligence

Cognitive maps represent the spatial, temporal, and conceptual relations underlying events occuring in an environment. Recently, it has been proposed that the computational structure of a cognitive map might correspond to a clone-structured cognitive graph (CSCG; George et al., 2021). One important property of cognitive maps is that they contain representations for flexible behavior, so that the system can efficiently learn and deal with ambiguous situations e.g. when similar observations occur in different contexts. Learning in CSCGs can be implemented via Expectation Maximization in Hidden Markov Models using a modified Baum-Welch algorithm. In this project you will study whether CSCGs are suitable to describe learning situations that humans face, where often only a limited number of training trials are available. To do so you will implement and study an adaptive, online version of the CSCG learning algorithm in sequence learning tasks, a simplified account for episodic memory in humans. Furthermore, you will study how key events in sequences with overlapping observations trigger the recruitment of novel nodes (“cloning”) and how this relates to prediction error based forms of structural learning (Gershman et al., 2017). This is important in the context of how artificial systems can be equipped with cognitive components that allow them to perform complex tasks.

Modeling Human Prediction Horizons in Partially Observable Environments

Humans frequently need to predict the future state of partially observable systems, such as tracking objects that temporarily disappear from view. However, the ability to make accurate predictions is limited by uncertainty that accumulates over time, giving rise to a prediction horizon beyond which forecasts become unreliable. This project aims to develop computational models that characterize human prediction behavior in such settings. In collaboration with the Biological Psychology lab led by Prof. Ricarda Schubotz (University of Münster), we will study a grid-world paradigm in which a target (e.g., a “mole”) moves according to structured but partially hidden dynamics. Participants observe the target intermittently and are asked to predict its future location after varying delays. On the computational side, the project will simulate the behavioural task using probabilistic models of latent state inference, such as Hidden Markov Models or related state-space models. These models maintain a belief distribution over the hidden state and propagate it forward in time to generate predictions at different horizons. A key focus will be on how uncertainty evolves during periods without observation and how this limits predictive performance. Different decision strategies will be evaluated, including maximum a posteriori prediction and probabilistic choice rules. The models will be used to generate quantitative predictions for human behavior, such as accuracy as a function of prediction horizon and sensitivity to the statistical structure of the environment. The project will provide insight into the computational principles underlying human predictive inference and establish a link between probabilistic modeling and behavioral data.

Transient oscillations in the brain during decision making

Neural oscillations are a key feature of brain activity and have been studied extensively in the context of cognitive functions and sensorimotor processing. However, recent studies have highlighted that oscillations in the brain are often transient in nature, consisting only of a few oscillation cycles, rather than being sustained throughout performing a cognitive task. In this project you will analyse oscillations recorded in the local field potential of mice performing a decision-making task (using open neural data from the International Brain Laboratory), or study the performance of algorithms by using synthetic data with known ground truth. The results of this project are important to better understand how transient oscillations contribute to information processing in the brain and affect behaviour.

Theory of Neural Systems

Visual Neuroscience -- Optical Imaging

PhD position - JOINT RESEARCH, EU funded Project, ERA-Net Neuron

"I-See" - Improving intracortical visual prostheses. Our multidisciplinary EU-funded project brings together scientists from different fields and complementary experimental and theoretical know-how. The project part of the PhD position comprises electrical stimulation in the mouse brain combined with cutting-edge (optogenetic) voltage imaging techniques (Knöpfel Lab, Imperial College London). The aim of our international consortium (Switzerland, Canada, UK, and Germany) is to improve the ability of cortical prostheses to 'mimic' the language of the brain and increase the safety and longevity of visual prosthetic devices.

PhD position - RUB-China Scholarship Council (CSC)

Our lab participates in a new call offered by the RUB to attract students from China. This is also to strengthen existing education and research cooperation with Chinese universities and research institutions. The China Scholarship Council (CSC) offers scholarships to highly qualified Chinese candidates who wish to study and/or carry out research at the Ruhr University Bochum, Germany.

About the INI

The Institut für Neuroinformatik (INI) is a research unit of the Faculties of Computer Science and Medicine at the Ruhr-Universität Bochum. Its scientific goal is to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory and effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental psychology and neurophysiology as well as machine learning, neural artificial intelligence, computer vision, and robotics.

Contact

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210

sekretariat@ini.rub.de
www.ini.rub.de