PhD position - Linking rate manifolds to -order spike patterns cortical layers

Organisation/Company Forschungszentrum Jülich Research Field All Researcher Profile First Stage Researcher (R1) Final date to receive applications 19 Jan 2038 - 03:14 (UTC) Country Germany Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure?

No

IAS-6 - Theoretical Neuroscience

Area of research:

PHD Thesis

Your Job:

This PhD project aims at relating precisely timed spike constellations across subsets of neurons to low-dimensional manifolds of high-dimensional space of population neuronal firing rates. Thus neuronal experimental data are to be analyzed for both aspects by PCA analysis and statistical multivariate methods to extract spatio-temporal spike patterns. Finally both results will be linked and related in space and time and to behavioral events.

Core Tasks:

• Getting familiar with the experimental data and the concepts of neuronal coding, and Elephant
• Analysis of the parallel rate data for submanifolds and their temporal dynamics during behavior
• Leverage dimensionality reduction and regression models to isolate task-related submanifolds and their respective role for sensory processing and task performance
• Analysis of the data to identify higher-order spike correlations and their temporal dynamics
• Are there particular manifold transition points and is their timing related to the occurrence of specific spike correlation patterns and animal behaviors?
• Further extend the results on the contributions of specific cortical layers and neural activity in various brain regions. Are task-specific submanifolds or spike correlation patterns limited to local neural circuits or span across brain regions?
• Set up a network model to reproduce the main results and provide potential neuronal mechanisms. Existing recordings with optogenetic inactivation could be leveraged to causally verify or reject important model predictions
• Writing of the thesis and publication about the relation of experimental data and model results

Your Profile:

• A Masters degree 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

Our Offer:

We work on the very latest issues that impact our society and are offering you the chance to actively help in shaping the change! This HDS-LEE PhD position will be located at Forschungszentrum Jülich and RWTH Aachen University. We offer ideal conditions for you to complete your doctoral degree:

• World class science environment at the interface between neuroscience and digital technologies, enabling scientific progress on the most complex known systems
• Outstanding scientific and technical infrastructure
• A highly motivated group as well as an international and interdisciplinary working environment at one of Europe’s largest research establishments
• Continuous scientific mentoring by your scientific advisors
• Chance of participating in (international) conferences
• Unique HDS-LEE graduate school program (including data science courses, soft skill courses and annual retreats)
• 30 days of annual leave (depending on agreed working time arrangements) and provision for days off between public holidays and weekends (e.g. between Christmas and New Year)
• A large research campus with green spaces, offering the best possible means for networking with colleagues and pursuing sports alongside work
• Further development of your personal strengths, e.g. through an extensive range of…