Internships
Internship in Computational Neuroscience
Available Theses & Projects
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.
Modeling spatial behaviors and representations using deep reinforcement learning
Using reinforcement learning (RL) to model different aspects of navigation by training artificial agents to learn to solve different navigation tasks.
Modeling Spatial Learning in the Hippocampus: Integrating Rate-Based and Temporal Coding through Synaptic Learning and Oscillations
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.
Necessary and Sufficient Behavioral Evidence for Episodic Memory Traces
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.
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.
Electrophysiological characterization of emotional and cognitive states
In this project you will be part of a scientific collaboration between the Neural Data Science group and the LWL-Universitätsklinikum Bochum (Clinic for Psychiatry). The goal is to find signatures in EEG data that correlate with emotional and cognitive states in humans. The data is measured in ongoing experiments at the Klinikum, in which participants are asked to imagine positive and negative emotions, as well as different objects and abstract concepts. We will apply various spectral methods to analyse the EEG in relation to the different task conditions. In addition, we will use machine learning algorithms, such as Hidden Markov Models, to determine whether the different task conditions are related to specific oscillatory latent states. In your project you will be responsible for processing and analysing the data sets, using state-of-the-art signal processing and machine learning tools. You will also have the opportunity to learn about EEG data recording procedures and participate in conducting experiments at the Klinikum. This project is ideal for an MSc Cognitive Science student with strong interests in neuroscience, data analysis, and the clinical context of mental health research.
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). The results of this project are important to better understand how transient oscillations contribute to information processing in the brain and affect behaviour.
Scalable Machine Learning
Research internships in the UK for Masters and Bachelors students
Research internships for theses through the DAAD PROMOS program at collaboration partner Royal Holloway, University of London in various areas of deep learning.
Sustainable Machine Learning
Activity and parameter sparsity in recurrent networks
Recent advances in machine learning have demonstrated impressive performance on complex tasks such as human-level image understanding and natural language processing. However, the increase in size and performance of these models has been accompanied by an increase in their energy consumption. This development has led to a growing interest in sparse, energy-efficient models in recent years. In this project, we will investigate activity- and parameter sparsity in a recurrent neural network architecture. The dependence of these two types of sparsity will be studied and optimal trade-offs between performance and efficiency will be identified.
Efficient transformer networks for video object detection
In recent years, software products such as ChatGPT and DALLE have demonstrated a new quality of automated data processing based on machine learning. These models are based on deep transformer networks, which are at the forefront of today's machine learning research and show state-of-the-art performance in virtually every relevant task. However, the high resource and energy consumption of these models has been an obstacle to the widespread adoption of these networks. In this project, we will investigate approaches to exploit sparsity in transformer networks to make them more resource efficient.
Theory of Neural Systems
Academic supervision on industry topic
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.
Visual Neuroscience -- Optical Imaging
Einfluss von Serotonin auf die Informations-Verarbeitung im visuellen Kortex der Maus - Master-Thesis in Physics/Biology
Master-Arbeit zu vergeben in Kooperation mit Prof. Stefan Herlitze, Lehrstuhl für Allgemeine Zoologie und Neurobiologie
Optical Imaging Projects, Master-Thesis in Physics/Biology
Einfluss der Extrazellulärmatrix auf die Aktivitätsausbreitung im visuellen Kortex der Maus
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.
Research Training Group (RTG) - "MoNN&Di" - Monoaminergic Neuronal Networks & Disease
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