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Available Theses & Projects

Autonomous Robotics

Computational Neurology

Explore dynamical consequences of neurostimulation using neural mass modeling

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.

Computational Neuroscience

Databases and Information Systems

Deep Learning for Optimization of Spatial Light Modulators

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/).

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.

Reinforcement learning models of spatial cognition in pigeons

A famous computational challenge is the Travelling Salesman Problem, in which a traveller needs to find the shortest route to visit a set of cities. Humans and other animals are very good at finding efficient solutions to practical tasks related to the Traveling Salesman Problem, but we do not know which strategies or algorithms they use to solve the problem. In computer science promising approaches to find an optimal solution include (deep) reinforcement learning [1]. Do humans and other animals use a strategy, similar to a reinforcement learning approach? In this project you will implement a reinforcement learning model of animal behaviour and compare the behaviour of the model with the animal behaviour to identify the underlying strategy and algorithm.

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.

Optical Imaging Group

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.

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.

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through 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 approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

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

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