Neural Data Science

We develop advanced analysis methods to apply them to neural and behavioural data, and combine them with computational models. In close collaboration with experimental research groups we analyse and model large data sets of electrophysiological recordings. Our goal is to identify the neural mechanisms underlying cognitive control, in particular in prefrontal and basal ganglia circuits, and to determine how the neuromodulator dopamine contributes to them. We use a variety of analysis and computational modelling techniques, such as machine learning approaches and numerical simulations of single neuron and network activity.

We also offer study projects, BSc and MSc theses in our group. If you are interested, please email robert.schmidt@ruhr-uni-bochum.de.

Group Leader

Prof. Dr. Robert Schmidt

Members

Gergö Gömöri, M.Sc.

Student Members

Shoto Yamada

Analysing Big Open Neural Data

Rapid technological advances have recently opened up new possibilities in understanding how the brain works. In particular the number of neurons that can be simultaneously recorded has increased considerably to hundreds (and soon thousands!) of neurons. This has lead to a big challenge on how to actually process and analyze the resulting big data sets. Conventional approaches often do not take advantage of big data sets and lack mathematical models to describe and connect them to cognitive functions. In this project we are working on solutions for these challenges using Open Data. This includes developing novel analysis methods and testing them with synthetic data, but also applying advanced data analysis and machine learning methods in combination with computational models to study cognitive processes.

Spatio-temporal dopamine signalling

Dopamine is a neurotransmitter with complex, not well-understood effects. In the basal ganglia dopamine modulates cortical input to the striatum. We study the functional role of dopamine with computational models ranging from `low-level’ cellular models, over neural network models to `high-level’ reinforcement learning models. The goal is to understand how dopamine contributions to learn selecting good actions (e.g. via synaptic plasticity) as well as the actual execution of actions (e.g. by changing motivational aspects of behavior). We apply our research results to clinical scenarios including Parkinson’s disease to study how pathological dopamine levels affect behavior.

    2023

  • Two modes of midfrontal theta suggest a role in conflict and error processing
    Muralidharan, V., Aron, A. R., Cohen, M. X., & Schmidt, R.
    NeuroImage, 273, 120107
  • Transient oscillations as computations for cognition: Analysis, modeling and function
    Schmidt, R., Rose, J., & Muralidharan, V.
    Current Opinion in Neurobiology, 83, 102796
  • 2022

  • Transient beta modulates decision thresholds during human action-stopping
    Muralidharan, V., Aron, A. R., & Schmidt, R.
    NeuroImage, 254, 119145
  • Aberrant Phase Precession of Lateral Septal Cells in a Maternal Immune Activation Model of Schizophrenia Risk May Disrupt the Integration of Location with Reward
    Speers, L. J., Schmidt, R., & Bilkey, D. K.
    The Journal of Neuroscience, 42(20), 4187–4201
  • 2021

  • Bilateral Intracranial Beta Activity During Forced and Spontaneous Movements in a 6-OHDA Hemi-PD Rat Model
    Mottaghi, S., Kohl, S., Biemann, D., Liebana, S., Crespo, R. E. M., Buchholz, O., et al.
    Frontiers in Neuroscience, 15
  • Hippocampal Sequencing Mechanisms Are Disrupted in a Maternal Immune Activation Model of Schizophrenia Risk
    Speers, L. J., Cheyne, K. R., Cavani, E., Hayward, T., Schmidt, R., & Bilkey, D. K.
    The Journal of Neuroscience, 41(32), 6954–6965
  • Basal ganglia and cortical control of thalamic rebound spikes
    Nejad, M. M., Rotter, S., & Schmidt, R.
    European Journal of Neuroscience, 54(1), 4295–4313
  • 2020

  • Occasion setters determine responses of putative DA neurons to discriminative stimuli
    Aquili, L., Bowman, E. M., & Schmidt, R.
    Neurobiology of Learning and Memory, 173, 107270
  • Globus pallidus dynamics reveal covert strategies for behavioral inhibition
    Gu, B. -M., Schmidt, R., & Berke, J. D.
    eLife, 9
  • Abundance Compensates Kinetics: Similar Effect of Dopamine Signals on D1 and D2 Receptor Populations
    Hunger, L., Kumar, A., & Schmidt, R.
    The Journal of Neuroscience, 40(14), 2868–2881
  • 2019

  • Beta Oscillations in Working Memory, Executive Control of Movement and Thought, and Sensorimotor Function
    Schmidt, R., Ruiz, M. H., Kilavik, B. E., Lundqvist, M., Starr, P. A., & Aron, A. R.
    The Journal of Neuroscience, 39(42), 8231–8238

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.

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