We invite applications for a master's thesis for contributing to advance control theory to brain networks to simulate treatment effects (e.g., medication or neurostimulation). This interdisciplinary project has a strong methodological focus and touches topics of neural data analysis and optimization.
Internship in Computational Neuroscience
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.
Using reinforcement learning (RL) to model different aspects of navigation by training artificial agents to learn to solve different navigation tasks.
The objective of this project is to explore how synaptic plasticity and oscillations facilitate spatial learning and navigation in a closed-loop model of a spiking neural network and a behaving agent.
The goal of this project is to identify what behavioral evidence would be necessary and sufficient to claim that a nonhuman species possesses episodic memory traces. The project requires background knowledge of memory and experience in developing philosophical analyses.
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.
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.
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.
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.
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.
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.
If you have an applied ML topic from industry (e.g., proposed during a student job / internship) and need an academic advisor, feel free to contact me.
Master-Arbeit zu vergeben in Kooperation mit Prof. Stefan Herlitze, Lehrstuhl für Allgemeine Zoologie und Neurobiologie
Einfluss der Extrazellulärmatrix auf die Aktivitätsausbreitung im visuellen Kortex der Maus
"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.
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.
The goal of MoNN&Di is to create a focused 4 year doctoral training programme that enables scrutiny of how monoaminergic neuromodulators influence neuronal circuits and shape behavioral responses.
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.
Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany
Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210
