Project theme

Neural events are characteristics, transient, coordinated, neural activities that we can identify in aggregated signals (e.g., local field potentials or LFPs). It has been shown some neural events have signatures across several scales (neurons, neural populations, and large-scale networks). Moreover, they are also closely connected to behavior; for instance, Sharp-Wave Ripples is one of the most studied neural events and, over two decades, it has been shown they are broadly involved in cognitive functions (from memory consolidation to offline and online planning). Then, the key gaps that we will try to fill during this project are the following:

1. How we can detect neural events in data reliably? Thus, we will develop unsupervised machine-learning methods to identify neural events.
2. If and how neural events are coupled to behavior? Thus, we will use our method(s) to investigate the occurrence of different kinds of neural events in a variety of behavioral tasks.
3. How do rich multi-scale dynamics of neural events support the behavior? Thus, we will characterize the multi-scale signature of neural events (e.g., how different brain regions interact/communicate in the vicinity of each event) and during a variety of behavioral tasks.

Overall, in this project, we will find accessible neural markers of cognitive processes, that likely shed light on behaviorally relevant coordination mechanisms in the brain.

CMC lab strives to create an inclusive and diverse environment. We therefore expressly encourage everyone (in particular minorities) to consider joining us.

Suggested skills (N=necessary, D=desired, P=plus)

• (N) Have background (Master/Diploma) in (computational) neuroscience, neuroscience, physics, mathematics, statistics, machine learning, psychology, and other related fields.
• (N) Being comfortable with programming (best would be, Python, or Matlab).
• (D) Have experience in the analysis of neurophysiology data.
• (D) Have experience with computational and systems neuroscience.
• (D) Familiarity with machine learning techniques or/and sufficient mathematical background.
• (P) Basic knowledge of software engineering (e.g., packaging scientific tools).

Learning targets

• General research-oriented skills:
  • Critical thinking
  • Organizing and optimizing a research flow
  • Scientific writing
  • Networking
• Learn data analysis techniques for multi-scale analysis of brain data
• Learn about several approaches to bridge neural dynamics and behavior
• Learn a wide range of topics in systems and computational neuroscience
• Receive training toward establishing an independent research group

Interested?

If you are interested, please submit your application (preferably) via TU Dresden’s Medical Faculty application portal by November 30, 2023, otherwise via email. As required by the official announcement of the PhD position, for applying, please include, a cover letter, brief research interests, and your CV. Please do not hesitate to contact us for questions (applications at cmclab dot org). If you sending your application or contacting us for question, please use the subject heading of bne_phd_202310 (otherwise it might get lost).