Paolo Massobrio

Ricercatore Universitario

Indirizzo Via All'Opera Pia, 13 - 16145 Genova
Telefono ufficio (+39) 010353 - 2761
Programmi di Ricerca
Laboratorio Neuroengineering and Bio-nano Technology (NBT)


Paolo Massobrio received the M.S. degree in Biomedical Engineering and the Ph.D. degree in Bioengineering Materials Engineering and Robotics (curriculum: Bioengineering and Bioelectronics) from the University of Genova, Italy, in 2004 and 2008, respectively. Currently he is assistant professor of Bioengineering at DIBRIS. His research activities are in the field of the neuroengineering and computational neuroscience, and they include both experimental and theoretical aspects. In particular, he is currently working on the interplay between dynamics and connectivity in dissociated neuronal assemblies, computational models able to describe the coupling between microtransducers (both metal microelectrodes and FET-based devices). In this field, he is coauthor of 30 peer-reviewed journal papers.


1.    V. P. Pastore, D. Poli, A. Godjoski, S. Martinoia, P. Massobrio. ToolConnect: a functional connectivity toolbox for in vitro networks. Frontiers in Neuroinformatics, Vol. 10, No. 13, doi: 10.3389/fninf.2016.00013,2016.

2.    D. Poli, V. P. Pastore, S. Martinoia, P. Massobrio. From functional to structural connectivity using partial correlation in neuronal assemblies. Journal of Neural Engineering, Vol. 13, No. 2, 2016.

3.    M. Tedesco, M. Frega, S. Martinoia, M. Pesce, P. Massobrio. Interfacing 3D engineered neuronal cultures to Micro-Electrode Arrays: an innovative in vitro experimental model, Journal of Visualized Experiments, No. 104, doi: 10.3791/53080, 2015.

4.    D. Poli, V. P. Pastore, P. Massobrio.Functional connectivity in in vitro neuronal assemblies, Frontiers in Neural Circuits, Vol. 9, No. 57, doi: 10.3389/fncir.2015.00057, 2015.

5.    P. Massobrio, V. Pasquale, S. Martinoia. Self-organized criticality in cortical assemblies occurs in concurrent scale-free and small-world networks, Scientific Reports, Vol. 5, No. 10578, doi: 10.1038/srep10578, 2015.

6.    P. Massobrio, J. Tessadori, M. Chiappalone, M. Ghirardi, In vitro studies of neuronal networks and synaptic plasticity in invertebrates and in mammals using Multi-Electrode Arrays (MEAs). Neural Plasticity, Vol. 2015, doi: 10.1155/2015/196195, 2015.

7.    P. Massobrio, L. de Arcangelis, V. Pasquale, H. J. Jensen, D. Plenz. Criticality as a signature of healthy neural systems. Frontiers in Systems Neuroscience, Vol. 9, No. 22 doi: 10.3389/fnsys.2015.00022, 2015.

8.    P. Balbi, S. Martinoia, P. Massobrio. Axon-somatic back-propagation in detailed models of spinal alpha motoneurons. Frontiers in Computational Neuroscience, Vol. 9, No. 15, doi: 10.3389/fncom.2015.00015, 2015.

9.    V. Pirino, E. Riccomagno, S. Martinoia, P. Massobrio. A topological study of repetitive co-activation networks in in vitro cortical assemblies. Physical Biology, Vol. 12, No. 1, 2015.

10.  M. Frega, M. Tedesco, P. Massobrio, M. Pesce, S. Martinoia. Network dynamics of 3D engineered neuronal cultures: a new experimental model for in-vitro electrophysiology. Scientific Reports, Vol. 4, N. 5489, doi: 10.1038/srep05489, 2014.

11.  P. Balbi, S. Martinoia, R. Colombo, P. Massobrio. Modeling the spinal α-motoneuron recurrent discharge: a reappraisal of F wave. Clinical Neurophysiology, Vol. 125, No. 2, pp 427-429, 2014.

12.  M. Mulas, P. Massobrio. NeuVision: a novel simulation environment to model spontaneous and stimulus-evoked activity of large-scale neuronal networks. Neurocomputing, Vol. 122, pp 441-457, 2013.

13.  P. Bonifazi*, F. Difato*, P. Massobrio*, G. Breschi, V. Pasquale, T. Levi, M. Goldin, Y. Bornat, M. Tedesco, M. Bisio, S. Kanner, R. Galron, J. Tessadori, S. Taverna, M. Chiappalone. In vitro large-scale experimental and theoretical studies for the realization of bi-directional brain-prostheses. Frontiers in Neural Circuits, Vol. 7, No. 40, doi: 10.3389/fncir.2013.00040, 2013. (*equal contribution).

14.  P. Massobrio, C. N. G. Giachello, M. Ghirardi, S. Martinoia. Selective modulation of chemical and electrical synapses of Helix neuronal networks during in vitro development. BMC Neuroscience, Vol. 14, No. 22, doi: 10.1186/1471-2202-14-22, 2013.

15.  T. Kanagasabapathi*, P. Massobrio*, R. A. Barone, M. Tedesco, S. Martinoia, W. J. Wadman, M. M. J. Decre. Functional connectivity and dynamics of cortical-thalamic networks co-cultured in a dual compartment device. Journal of Neural Engineering, doi: JNE/420796, 2012. (* equal contribution).

16.  G. Massobrio, P. Massobrio, S. Martinoia. Investigation of extracellular signal shapes recorded by planar metal microelectrodes covered with carbon nanotubes: modeling and simulations. IEEE Transactions on Nanotechnology, Vol. 10, No. 6, pp. 1328- 1336, 2011.

17.  G. Massobrio, A. Massobrio, L. Massobrio, P. Massobrio. Silicon-based biosensor functionalized with Carbon NanoTubes to investigate neuronal electrical activity in pH-stimulated environment: a modelling approach. Micro & Nano Letters, Vol. 6, No. 9, pp. 689-693, 2011.

18.  M. Mulas, P. Massobrio, S. Martinoia, M. Chiappalone. A simulated neuro-robotic environment for bi-directional closed-loop experiments. Journal of Behavioral Robotics, Vol. 1, No., 3, pp. 179-186, 2010.

19.  E. Bottino, P. Massobrio, S. Martinoia, G. Pruzzo, M. Valle. Low-noise low-power CMOS preamplifier for multisite extracellular neuronal recordings. Microelectronics Journal, Vol. 40, No. 12, pp. 1779-1787, 2009.

20.  P. Massobrio, M. Tedesco, C. Giachello, M. Ghirardi, F. Fiumara, S. Martinoia. Helix neuronal ensembles with controlled cell type composition and placement develop functional polysynaptic circuits on Micro-Electrode Arrays, Neuroscience Letters, Vol. 467, No. 2, pp. 121-126, 2009.

21.  M. Garofalo, T. Nieus, P. Massobrio, S. Martinoia. Evaluation of the performances of information theory-based methods and cross-correlation to estimate the functional connectivity in cultured cortical networks, PLoS ONE, Vol. 4, No. 8, pp. e6482, 2009.

22.  L. Berdondini, P. Massobrio, M. Chiappalone, M. Tedesco, K. Imfeld, A. Maccione, M. Koudelka-Hep, S. Martinoia. Extracellular recordings from locally dense microelectrode arrays coupled to dissociated cortical cultures. Journal of Neuroscience Methods, Vol. 177, No. 2, pp. 386-396, 2009.

23.  A. Maccione, M. Gandolfo, P. Massobrio, A. Novellino, S. Martinoia M. Chiappalone. A novel algorithm for precise identification of spikes in extracellularly recorded neuronal signals. Journal of Neuroscience Methods, Vol. 177, No. 1, pp. 241-249, 2009.

24.  G. Massobrio, P. Massobrio, S. Martinoia. Modeling the neuron-carbon Nanotube-ISFET junction to investigate the electrophysiological neuronal activity. Nano Letters, Vol. 8, No. 12, pp. 4433-4440, 2008.

25.  P. Massobrio, S. Martinoia. Modeling small-patterned neuronal networks coupled to Micro-Electrode Arrays. Journal of Neural Engineering, Vol. 5, No. 3, pp. 350-359, 2008.

26.  M. Chiappalone, P. Massobrio, S. Martinoia. Network plasticity in cultured cortical assemblies. European Journal of Neuroscience, Vol. 28, No. 1, pp. 221-237, 2008.

27.  V. Pasquale, P. Massobrio, L. L. Bologna, M. Chiappalone, S. Martinoia. Self-organization and neuronal avalanches in networks of dissociated cortical neurons. Neuroscience, Vol. 153, pp. 1354-1369, 2008.

28.  G. Massobrio, P. Massobrio, S. Martinoia. Modeling and simulation of silicon neuron-to-ISFET junction. Journal of Computational Electronics, Vol. 6, No. 4, pp. 431-437, 2007.

29.  P. Massobrio, G. Massobrio, S. Martinoia. Multi-program approach for simulating recorded extracellular signals generated by neurons coupled to microelectrode arrays. Neurocomputing, Vol. 70, pp. 2467-2476, 2007.

30.  E. Macis, M. Tedesco, P. Massobrio, R. Raiteri, S. Martinoia. An automated micro-drop delivery system for neuronal network patterning on microelectrode arrays. Journal of Neuroscience Methods, Vol. 161, pp. 88-95, 2007.

31.  S. Martinoia, P. Massobrio. ISFET-neuron junction: circuit models and extracellular signal simulations. Biosensors & Bioelectronics, Vol. 19, No. 11, pp. 1487-1496, 2004.

32.  S. Martinoia, P. Massobrio, M. Bove, G. Massobrio. Cultured neurons coupled to microelectrode arrays: circuit models, simulations and experimental data. IEEE Transactions on Biomedical Engineering, Vol. 51, No. 5, pp. 859-864, 2004.

Research activity

My research activities fit into the field of Neuroengineering, concerning both experimental and modeling aspects. Within this framework, my research interest are mainly focused on the following topics:

-       Computational models of large-scale neuronal networks

-       Engineered neuronal networks, and experimental studies on network dynamics and plasticity


-       Bioelectronic models of the neuro-electronic junction


Here below, a brief description of the main research topics is summarized.

§  Engineered neuronal networks and investigations on dynamics and plasticity

The possibility to design specific network architectures by “forcing” the neurons to follow pre-defined structures allows to obtain information on the synaptic interaction among neurons, on the interplay between dynamic and network topology, and the mechanisms underlying synaptic plasticity and learning. On this topic, during my PhD, I performed experiments on dissociated cortical neurons coupled to MEAs by applying specific protocols of electrical stimulation that induce changes in network dynamics. I found that the effects of these high-frequency protocols induce long-term network plasticity. In 2007-2008, in collaboration with Philips (Eindhoven, The Netherlands) to understand the interplay between neural dynamics and connectivity, I developed an in vitro system for the study of the interactions between the cortico-thalamic neurons. More recently, we develop a new experimental protocol for culturing in vitro 3D networks coupled to MEAs and investigate how a 3D organization can shape the emergent dynamics. In addition, to characterize the dynamics and the related neural connectivity, in collaboration with the University of Torino (Italy), I performed experiments with neurons of invertebrate (Helix aspersa). Networks of neurons of Helix were designed and coupled to MEAs; by exploiting the big dimension of the somata of such neurons, a one-to-one coupling was achieved and two aspects were investigated: the dynamics expressed by the different families of Helix neurons in a network context and the emerging functional connectivity.

§  Computational models of neuronal assemblies

To model the dynamics of neuronal networks, I followed two different approaches: i) networks made up of a low number of neurons, whose activity has been described with accurate models (e.g., Hodgkin and Huxley); ii) large-scale networks (high number of neurons, complex connectivity rules) made up of point neurons with reduced models (e.g., Izhikevich equations). The common factor of these simulations is to understand the interdependence between connectivity and emerging dynamics. Neuronal networks can be ascribed to the category of complex systems, given the presence of numerous elements, such as the non-linearity of the interactions, the appearance at the global level of emergent properties without a similar microscopic, and not least the ability to self-organize. It has been found that a critical state is the key point for understanding the properties of plasticity and self-organization of the nervous system. By exploiting a modeling approach, I tried to correlate the critical behavior with the underlying network topology: the most important achieved result is that a temporal power-law behavior can be induced by a scale-free connectivity.

§  Bioelectronics models of the neuro-microtransducer interface


The recording of electrophysiological signals by extracellular electrodes is possible by the presence of a coupling circuit between the neuronal membrane and the recording device. During the first years of my research activity, I developed different circuit models of neuro-electronic junction for active devices (FET-based) and passive devices (metal microelectrodes and microelectrode arrays). Finally, the effects that the deposition of a layer of carbon nano-tubes (CNT) produce on the recorded signal was modelled.

Teaching activity

Since 2005, I have been teaching lessons to master degree as well as PhD students. In particular:

 Teaching assistant of the following courses:

 -       Bioelectronics (2005-now) for the students of the 2nd year of the bachelor degree in Biomedical Engineering (University of Genova, Genova, Italy).

-       Methods and Techniques for the Neuroengineering (2004-2009) for the students of the 1sy year of the master degree in Bioengineering (University of Genova, Genova, Italy).

-       Fundaments of Neuroengineering (2004-2006) for the students of the 1sy year of the master degree in Biomedical Engineering (University of Pavia, Pavia, Italy).

Co-holder of:

­          Neuroengineering and Computational Neuroscience (2009-2011) for the students of the 1sy year of the master degree in Bioengineering (University of Genova, Genova, Italy).

 Holder of the following courses:

­          Computational Neuroscience (2012-now) for the students of the 2nd year of the master degree in Bioengineering (University of Genova, Genova, Italy).

­          Modeling neuronal structures: from single neurons to large-scale networks (2010-2014) for the PhD students in Bioengineering and Robotics.


Finally, I was invited as tutor for the 18th Advanced Course in Computational Neuroscience during the summer 2013, in Bedlewo (Poland).


Supervision of People

Currently, I am supervising two PhD students.


Additionally, since 2004 I supervised

-       2 PhD students (University of Genova, Genova, Italy)

-       1 post-doc (University of Genova, Genova, Italy)

-       5 master students in Bioengineering (University of Genova, Genova, Italy)

-       23 bachelor students in Biomedical Engineering (University of Genova, Genova, Italy)

-       2 bachelor students in Biomedical Engineering (University of Cagliari, Cagliari, Italy)


-       1 master student in Biochemistry and Biotechnology (Universiteit Antwerpen, Antwerp, Belgium)

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