/Exploring in vitro neuronal circuit dynamics and connectivity patterns by machine learning techniques

Exploring in vitro neuronal circuit dynamics and connectivity patterns by machine learning techniques

PhD - Leuven | More than two weeks ago

Use state of the art neural interface microchips and novel algorithms to decipher complex in vitro neuronal circuits

Neuronal networks of human-derived neurons represent an emerging opportunity to accelerate research on brain function and neurological disease states. Electrophysiological signals recorded from neuronal networks provide distinctive information on the nature of the network and can be employed to unravel mechanisms of relevant neurodegenerative diseases. The complex architecture of excitatory and inhibitory connections thereby governs the selectivity of neuronal responses that can be observed from the network. Hence, a key step in uncovering the functional connectivity within these networks resides in the accurate analysis of how a single neuron can modulate the circuit function and behavior.

 

Imec's high-density microelectrode array (HD-MEA) platform is a powerful tool to study these cellular networks at single cell level. The MEA chips can interrogate and interact with the neuronal network grown on top of the sub-cellular sized electrodes by recording their electrical signatures or stimulating them. With more than 16k electrodes and an electrode pitch of 15 mm, the chip provides an unprecedented resolution for neuronal interfacing. Inferring knowledge about developing neural circuits is however not only determined by the scale of data acquisition. Scaling data dimensionality can overwhelm data analysis capacity and lead to exponentially increasing computation times. The implementation of innovative algorithms and experimental paradigms will be required to ensure an increasing rate of scientific discovery while increasing the scale of data.

 

This PhD project will explore algorithmic approaches and a computationally scalable data processing pipeline for data-efficient interrogation of neuronal circuits on the HD-MEA platform. Adaptive selection of recording electrodes will be combined with closed loop experimental feedback to maximize the rate at which electrical profiles can be derived from single neuron and neuronal network behavior. Novel experimental paradigms will be explored to infer network connectivity patterns that are based on external stimulation with different modalities (e.g. mild voltage stimulation or optogenetics). The successful candidate will be supervised and supported by a team of data/computer scientists, engineers and biologists at imec, in collaboration with neuronal communication experts at VIB and Stanford university.

 

Required background: Computer science, computational neuroscience

 

Type of work: 70% data analysis, 20% experimental, 10% literature

Supervisor: Liesbet Lagae

Co-supervisor: Dries Braeken

Daily advisor: Dennis Lambrechts, Roel Wuyts

The reference code for this position is 2022-092. Mention this reference code on your application form.