Neural Engineering Laboratory
Your brain processes every action, thought, and feeling that compose your conscious and subconscious experience. Billions of neurons (processing units) and trillions of synapses (connections) allow this miracle to happen every day, but very little is known about the computational and biological properties of this complex machine.
The neural engineering laboratory (NEL) studies how different learning and sensing mechanisms interact within the brain, with the final goal of developing neural prostheses to recover lost motor or cognitive functions.
The laboratory research is both experimental and computational, with strong emphasis on optical brain imaging and machine learning applied to data analysis. The NEL lab develops tools for simultaneous recording, modulation and real time extraction of neural dynamics at different brain locations and scales. We support open-source and open-science, therefore distributing our tools as free resources for the scientific community.
Available postdoctoral positions in the lab
Giovannucci, A.*, Minden, V.*, Pehlevan, C., & Chklovskii, D. B. (2018). Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching. IEEE Big Data 2018. In press. Article. Software in Matlab and Python.
Pnevmatikakis, E. A., & Giovannucci, A. (2017). NoRMCorre: An online algorithm for piecewise rigid motion correction of calcium imaging data. Journal of neuroscience methods, 291, 83-94. Python and Matlab Software. Article.
Giovannucci, A., Friedrich, .., & Pnevmatikakis, E. A. (2017). Onacid: Online analysis of calcium imaging data in real time. In Advances in Neural Information Processing Systems (pp. 2381-2391). Software. Article.
Bamford, S. A., Hogri, R., Giovannucci, … & Del Giudice, P. (2012). A VLSI field-programmable mixed-signal array to perform neural signal processing and neural modeling in a prosthetic system. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(4), 455-467. Article.
Giovannucci, A.*, Badura, A.*, … & Wang, S.S-H. (2017). Cerebellar granule cells acquire a widespread predictive feedback signal during motor learning. Nature neuroscience, 20(5), 727. Article.
Najafi, F., Giovannucci, … & Medina, J. F. (2014). Coding of stimulus strength via analog calcium signals in Purkinje cell dendrites of awake mice. Elife, 3, e03663. Article.
Kloth, A. D., Badura, A., Li, A., Cherskov, A., Connolly, S. G., Giovannucci, A., … & Tsai, P. T. (2015). Cerebellar associative sensory learning defects in five mouse autism models. Elife, 4, e06085. Article.
Najafi, F.*, Giovannucci, A.*, … & Medina, J. F. (2014). Sensory-driven enhancement of calcium signals in individual Purkinje cell dendrites of awake mice. Cell reports, 6(5), 792-798. Article.
Sun, X. R., Giovannucci, … & Wang, S. S. H. (2012). SnapShot: optical control and imaging of brain activity. Cell, 149(7), 1650-1650. Article.