We invite applications for a master's thesis for contributing to developing models of personalized transcranial magnetic stimulation (TMS) in neurodegenerative diseases (e.g., Alzheimer’s Disease). The project touches topics of probabilistic modelling, data analysis and optimization.
Personalization of neurostimulation can improve its efficacy whilst taking into account the increasing complexity of the corresponding technology and the amount of technical choices (regarding stimulation location and stimulation settings). In this project we aim to create a whole-brain neural mass model that can accurately predict the effect of different neurostimulation settings in healthy and neurologically impaired individuals.
We invite applications for students' projects and bachelor/master theses for contributing to developing models of pathological influences of neurodegeneratives diseases (i.e., Alzheimer’s Disease) on brain activity. The project touches topics of whole-brain dynamics, biomarkers, and effective connectivity (i.e., directional influences of brain regions onto each other).
Closed-loop brain-behavior system
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.
Spatial Light Modulators can be used to modulate the effective shape of light, e.g., a laser beam. They are thus useful in industrial applications like laser cutting where beam shapes need to be adapted quickly. However, generating complex shapes is time-consuming and error-prone using current algorithms based on Fourier transformations. In this work, a deep learning approach that automatically generates the correct modulations to obtain the desired shape should be explored. The Thesis is conducted in cooperation with the company LIDROTEC(https://www.lidrotec.de/).
This thesis involves integrating a new method for estimating cardinality into query engines to assess its effectiveness in improving query optimization.
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.
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.
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.
Research internships for theses through the DAAD PROMOS program at collaboration partner Royal Holloway, University of London in various areas of deep learning.
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.
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.
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.
Comparing modern PPL packages regarding their qualitative and quantitative differences.
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 interdisciplinary research unit of the Ruhr-Universität Bochum. We aim to understand fundamental principles that characterize how organisms generate behavior and cognition while linked to their environments through sensory and effector systems. Inspired by insights into 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 approaches from psychology and neurophysiology, theoretical approaches from physics, mathematics, and computer science, including, in particular, machine learning, artificial intelligence, autonomous robotics, and computer vision.
Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany
Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210
