Martino Sorbaro Sindaci

Martino Sorbaro Sindaci


Research Interests

I'm working in Matthias Hennig 's group on population-level models of neural activity, based on experimental measurements of retinas and cultures, provided by collaborators in Newcastle and Genova. Multielectrode arrays measure the electrical potential at up to 4096 different locations on a small chip, that covers a retina or a group of cultured neurons entirely, allowing us to eavesdrop the electrical signals that neurons produce and transmit, which constitute the fundamental way of communicating and computing in the nervous system. The work I'm doing involves preparation of the data for analysis, validation of the technique used to identify the signature of single neurons, and will finally focus on models of the population activity, based on the data.

Publications:
2017
  Unsupervised spike sorting for large scale, high density multielectrode arrays
Hilgen, G, Sindaci, MS, Pirmoradian, S, Muthmann, J-O, Kepiro, I, Ullo, S, Juarez Ramirez, C, Encinas, AP, Maccione, A, Berdondini, L, Murino, V, Sona, D, Cella Zanacchi, F, Sernagor, E & Hennig, MH 2017, 'Unsupervised spike sorting for large scale, high density multielectrode arrays' Cell Reports, vol 18, no. 10, pp. 2521-2532. DOI: 10.1016/j.celrep.2017.02.038
A new method for automated spike sorting for recordings with high density, large scale multielectrode arrays is presented. It is based on an efficient, low-dimensional representation of detected events by their estimated spatial current source locations and dominant spike shape features. Millions of events can be sorted in just minutes, and the full analysis chain scales roughly linearly with recording time. We demonstrate this method using recordings from the mouse retina with a 4,096 channel array, and present validation based on anatomical imaging and model-based quality control. Our analysis shows that it is feasible to reliably isolate the activity of hundreds to thousands of retinal ganglion cells in single recordings.
General Information
Organisations: Edinburgh Neuroscience.
Authors: Hilgen, Gerrit, Sindaci, Martino Sorbaro, Pirmoradian, Sahar, Muthmann, Jens-Oliver, Kepiro, Ibolya, Ullo, Simona, Juarez Ramirez, Cesar, Encinas, Albert Puente , Maccione, Alessandro, Berdondini, Luca, Murino, Vittorio, Sona, Diego, Cella Zanacchi, Francesca, Sernagor, Evelyne & Hennig, Matthias H.
Number of pages: 18
Pages: 2521-2532
Publication Date: 7 Mar 2017
Publication Information
Category: Article
Journal: Cell Reports
Volume: 18
Issue number: 10
ISSN: 2211-1247
Original Language: English
DOIs: 10.1016/j.celrep.2017.02.038
2015
  Critical behavior of the relaxation rate, the susceptibility, and a pair correlation function in the Kuramoto model on scale-free networks
Yoon, S, Sindaci, MS, Goltsev, AV & Mendes, JFF 2015, 'Critical behavior of the relaxation rate, the susceptibility, and a pair correlation function in the Kuramoto model on scale-free networks' Physical Review E, vol 91, no. 3. DOI: 10.1103/PhysRevE.91.032814
We study the impact of network heterogeneity on relaxation dynamics of the Kuramoto model on uncorrelated complex networks with scale-free degree distributions. Using the Ott-Antonsen method and the annealed-network approach, we find that the critical behavior of the relaxation rate near the synchronization phase transition does not depend on network heterogeneity and critical slowing down takes place at the critical point when the second moment of the degree distribution is finite. In the case of a complete graph we obtain an explicit result for the relaxation rate when the distribution of natural frequencies is Lorentzian. We also find a response of the Kuramoto model to an external field and show that the susceptibility of the model is inversely proportional to the relaxation rate. We reveal that network heterogeneity strongly impacts a field dependence of the relaxation rate and the susceptibility when the network has a divergent fourth moment of degree distribution. We introduce a pair correlation function of phase oscillators and show that it has a sharp peak at the critical point, signaling emergence of long-range correlations. Our numerical simulations of the Kuramoto model support our analytical results.
General Information
Organisations: Neuroinformatics DTC.
Authors: Yoon, S., Sindaci, Martino Sorbaro, Goltsev, A. V. & Mendes, J. F. F..
Publication Date: 30 Mar 2015
Publication Information
Category: Article
Journal: Physical Review E
Volume: 91
Issue number: 3
ISSN: 1539-3755
Original Language: English
DOIs: 10.1103/PhysRevE.91.032814

Projects:
Statistical modelling of large-scale neural population recordings (PhD)