Stage: Developing algorithms to recover the retinotopic map of micro-electrodes implanted in the visual cortex
One of the challenges for cortical visual prostheses is to determine the corresponding retinotopic location of each implanted electrode, as traditional receptive field (RF) mapping involving the presentation of visual stimuli is not an option for blind individuals. It is known, however, that correlations in neuronal activity across electrodes can be indicative of the proximity between electrodes, as well as the degree of structural and functional connectivity between neurons in their immediate vicinity.
We would like to explore the possibility of developing algorithms to infer the corresponding retinotopic locations of each implanted microelectrode using resting-state neuronal activity recorded through high-density microelectrode arrays implanted in the visual cortex of normally sighted animals. The performance of the developed algorithm(s) will be benchmarked against the ground-truth RF maps obtained via traditional RF mapping techniques. Ultimately, we aim to apply this technique in human patients with a cortical visual prosthesis.
The student will be involved in analyzing large neuronal datasets and developing the aforementioned algorithms, with the possibility of co-authoring a manuscript to report this development, should the results allow. For this internship position, we seek a MSc-level student with a solid background in programming and a strong interest in computational neuroscience. The duration of this internship should be no less than 5 months and the student is expected to engage in this work full-time.
If you are interested in this project, please respond by sending 1) a short motivation letter, 2) your CV and 3) grades from your bachelor and/or master study.
Please note, this will be an unpaid internship and we will not be able to provide housing.
Thank you for your interest!