TRACKING FUNCTIONAL CONNECTIVITY IN DEVELOPING NEURONAL NETWORKS OVER TIME
Summary
Tracking activity on long time-scales in biological neural networks remains a major challenge, as it requires monitoring thousands of individual neurons at the millisecond and during long time periods (several weeks or months). The interests are both of applied and fundamental importance, such as deciphering information processes, and to diagnose, treat or restore disabled functionality (e.g. neurodegenerative disease, trauma). For example, activity evolution over time can be the signature (hallmark) of learning and can be used for understanding the origin of complex behaviors in living species. Monitoring such activity requires tracking fast events such as action potentials (ms range) and slow evolutions such as long-term synaptic processes or structural modifications (from s to days). The aim of this PhD is to develop innovative approaches and tools that will enable to track evolution of activity in biological neural networks on multiple spatial and temporal scales. The PhD will be co-supervised between Dr Yger (expert in extracellular recordings and computational neuroscience, in Lille) and Dr Delacour (expert in physics of materials and multi-electrode arrays design, in Grenoble). The core of the PhD will be mainly devoted to the development of advanced spike sorting algorithms to track the neuronal network (NN)’s connectivity and its evolution in time. This will require possible Machine Learning approaches for online processing of datastreams, potentially with Deep Learning architectures. Such algorithms could be augmented with calcium imaging data, to see how multimodal integration could enhance the estimation of functional connectivity in developing tissues.
Required qualification
- To have defended a Master in mathematics, physics or neuroscience, or an engineering diploma in computer science or signal processing before starting the PhD. Expertise in either extracellular recordings, deep learning or signal processing is a bonus.
- Preference will be given to candidates who can demonstrate good coding skills (notably in Python +/- torch and ML algorithms), and an interest in analyzing neuronal activity.
- Women and under-represented minorities in academia are strongly encouraged to apply.
Submission Guidelines
Please submit application materials to pierre.yger@univ-lille.fr & cecile.delacour@neel.cnrs.fr. To be considered, your application must include: (i) an updated CV, (ii) a cover letter highlighting relevant research experience to the project, interest in the position, and the earliest date you could start, (iii) two letters of endorsement/support from academic mentor/reference (including their contact information), and specifically written for this position.