PhD position - Bridging classical and AI-based approaches for the analysis of massively parallel neural spike trains within the HDS-LEE graduate school

This PhD project bridges between classical analytical methods and modern AI based techniques to analyse spike train recordings to advance our understanding of neural population coding while maintaining clarity in the interpretation of results. Concurrently, AI-based methods are developed that prioritize interpretability and reduce data dependency by imposing desirable constraints on model behavior.

We will divide our work into three thrusts:

• Thrust A: A first major objective will be to augment classical spike train analysis methods particularly those developed by Prof. Grün and others for detecting synchronous spiking activity with AI-based enhancements. After profiling the classical methods for their bottlenecks, these steps will then be replaced or supplemented with ML-based surrogates or approximators, such as random forests or shallow neural networks, trained to mimic the outputs of the original computations at a fraction of the cost. This hybridization aims not only to accelerate performance but also to maintain, if not improve, analytical rigor. The improved modules will be integrated into an updated analytical pipeline and validated against benchmark datasets drawn from prior studies in the field.
• Thrust B: A second central thrust of this project focuses on making contemporary ML-based techniques more interpretable and biologically meaningful in their application to neural population coding. As a starting point, we will build upon recent advances in graph neural networks (GNNs), particularly those described by which offer a promising architecture for modelling population-level neural interactions. Prior work has emphasized rate-based codes due to their relative simplicity; our approach will explicitly extend these models to capture temporal structure within spike trains thereby moving towards analyses that are sensitive not just to firing rates but also precise timing relationships underpinning temporal codes. To ensure that these advanced models do not become opaque "black boxes," we will integrate post-hoc explainability tools such as SHAP values (SHapley Additive exPlanations)
• Thrust C: The utility of all developed methods will be rigorously evaluated using both synthetic and real-world datasets. Synthetic benchmarks will be generated using established generative models capable of producing ground-truth synchronous patterns under varying conditions to enable systematic validation across relevant scenarios. All software arising from this work including improved analysis pipelines and benchmarking datasets will be released through an open-source library.

PhD position - Bridging classical and AI-based approaches for the analysis of massively parallel neural spike trains within the HDS-LEE graduate school

PhD position - Bridging classical and AI-based approaches for the analysis of massively parallel neural spike trains within the HDS-LEE graduate school, * A Masters degreee with a strong academic background in physics, mathematics, computer science, or a related field

• Proficiency in at least one programming language (Python, C++, …)
• Experience in neuroscience is an advantage
• Good analytical skills with a sound understanding of data evaluation
• Good organisational skills and ability to work systematically, independently and collaboratively
• Effective communication skills and an interest in contributing to a highly international and interdisciplinary team
• Motivation for academic development, supported by bachelor's and master's transcripts and two reference letters
• Working proficiency in English for daily communication and professional contexts (TOEFL or equivalent or excemption required)
• Knowledge of German is beneficial

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