PhD in Computational Neuroscience

PhD in Computational Neuroscience
Project title: Computational approaches to study neural population coding

Stefano Panzeri is seeking candidates for a PhD position fully funded (including a full stipend) for 4 years. 

The PhD project can focus on any of the research topics about neural information processing that are of interest to both the candidate and my lab, including: developing mathematical analysis methods and neural network models for studying large scale neural population coding during cognitive tasks, for studying the neural bases of sensory perception and decision making, for linking functional to anatomical connectivity with cellular resolution at the neural population level, and for determining the best theoretical framework to combine recording of neural activity (calcium imaging, electrophysiology, EEG) and intervention on it (optogenetics, TMS).

Panzeri’s computational neuroscience lab has developed long-term collaboration with many experimental laboratories including Christopher Harvey (Harvard Medical School), Tobias Donner (UKE), Tommaso Fellin (IIT), Mathew Diamond (SISSA), Alex Thiele (Newcastle), Cristina Becchio (IIT), Alessandro Gozzi (IIT), Mike Lombardo (IIT) and others. The PhD project will likely involve collaborations with one or more experimental lab. 

For recent representative publications please see:

Runyan, C.A., Piasini, E., Panzeri, S., and Harvey, C.D. (2017). Distinct timescales of population coding across cortex. Nature 548, 92-96. 

Panzeri S., Harvey, C.D.  Piasini, E., Latham, P.E. , Fellin, T. (2017) Cracking the neural code for sensory perception by combining statistics, intervention and behaviour. Neuron 93: 491-507

G. Pica, E. Piasini, H. Safaai, C.A. Runyan, M.E. Diamond, T. Fellin, C. Kayser, C.D. Harvey, S. Panzeri, (2017) Quantifying how much sensory information in a neural code is relevant for behavior, Neural Information Processing Systems (NIPS) 30, 2017, available online

H. Safaai, A. Onken, C.D. Harvey, S. Panzeri (2018) Information estimation using nonparametric copulas.  Phys. Rev. E 98, 053302  

E. Chong, M. Moroni, S. Shoham, S. Panzeri, D. Rinberg (2020) Manipulating synthetic optogenetic odors reveals the coding logic of olfactory perception. Science 368, 1329. 

S. Pashkovski, G. Iurilli, D. Brann, D. Chicharro, K. Drummey, K. Franks, S. Panzeri, S.R. Datta (2020). Structure and flexibility in cortical representations of odour space. Nature: 583: 253-258 doi:  https://doi.org/10.1038/s41586-020-2451-1

J-F Patri, A. Cavallo, K. Pullar, M. Soriano, M. Valente, A. Koul, A. Avenanti, S. Panzeri*, C. Becchio* (2020) Transient disruption of the inferior parietal lobule impairs the ability to attribute intention to action. Current Biology 30: 4594-4605 https://doi.org/10.1016/j.cub.2020.08.104

S. Trakoshis, P. Martínez-Cañada, … A. Gozzi, S. Panzeri, and M.V. Lombardo (2020) Intrinsic excitation-inhibition imbalance affects medial prefrontal cortex differently in autistic men versus women. eLIFE  9: e55684. doi.org/10.7554/eLife.55684  

D. Ferro, J. van Kempen, M. Boyd, S. Panzeri*, A. Thiele* (2021) Directed information exchange between cortical layers in macaque V1 and V4 and its modulation by selective attention. PNAS 118 (12), e2022097118, doi.org/10.1073/pnas.2022097118 

Valente, A., Pica, G., Bondanelli, G., Moroni, M., Runyan, C.A., Morcos, A., Harvey, C.D., and Panzeri, S. (2021). Correlations enhance the behavioral readout of neural population activity in association cortex. Nature Neuroscience, in press, early preprint available at: https://t.co/QJ3aHlTFad

Main Supervisor: Stefano Panzeri 

Essential expertise: A degree in numerate disciplines, with a strong interest in interdisciplinary research and in understanding the brain. No previous experience in neuroscience is required, although it is useful. 

How to apply: Prospective students must refer to the web pages of the PhD course on Data Science and Computation of Alma Mater Studiorum Università di Bologna for a full information on the application and enrolment processes.