Riccardo Marrocchio received his BSc in Physics from the Sapienza University of Rome and his MSc in Physics from the University of Rome Tor Vergata. During his master, he had the opportunity to study and develop analytical and computational techniques to build mathematical models of complex biological systems, in particular of neuronal networks and the hearing system. He then joined the Institute of Sound and Vibration Research as a Ph.D. researcher at the University of Southampton. During his Ph.D., he worked on the development of a model of active cochlear micromechanics. After his PhD he continued at the University of Southampton joining the DigiTwin project as a Research Fellow, to work on the generalization of the biological feedback system of the cochlea to the design of control systems. To pursue his interests in hearing research, he joined Dr. Ó Maoiléidigh Lab, where he is working on stochastic fluctuations in hair bundles.

Honors & Awards

  • FGSA Award for Excellence in Graduate Research, American Physical Society (2021)

Boards, Advisory Committees, Professional Organizations

  • Member, Association for Research in Otolaryngology (2021 - Present)
  • Member, UK Acoustic Network (2020 - Present)
  • Member, American Physical Society (2020 - Present)

Professional Education

  • Doctor of Philosophy, University Of Southampton (2022)
  • Bachelor of Science, Sapienza University of Rome (2014)
  • Master of Science, University of Rome Tor Vergata (2018)
  • PhD, University of Southampton, Engineering and Physical Sciences (2022)
  • MSc, University of Rome Tor Vergata, Physics (2018)
  • BS, Sapienza University of Rome, Physics (2014)

Stanford Advisors

All Publications

  • Inferring Excitatory and Inhibitory Connections in Neuronal Networks ENTROPY Ghirga, S., Chiodo, L., Marrocchio, R., Orlandi, J. G., Loppini, A. 2021; 23 (9)


    The comprehension of neuronal network functioning, from most basic mechanisms of signal transmission to complex patterns of memory and decision making, is at the basis of the modern research in experimental and computational neurophysiology. While mechanistic knowledge of neurons and synapses structure increased, the study of functional and effective networks is more complex, involving emergent phenomena, nonlinear responses, collective waves, correlation and causal interactions. Refined data analysis may help in inferring functional/effective interactions and connectivity from neuronal activity. The Transfer Entropy (TE) technique is, among other things, well suited to predict structural interactions between neurons, and to infer both effective and structural connectivity in small- and large-scale networks. To efficiently disentangle the excitatory and inhibitory neural activities, in the article we present a revised version of TE, split in two contributions and characterized by a suited delay time. The method is tested on in silico small neuronal networks, built to simulate the calcium activity as measured via calcium imaging in two-dimensional neuronal cultures. The inhibitory connections are well characterized, still preserving a high accuracy for excitatory connections prediction. The method could be applied to study effective and structural interactions in systems of excitable cells, both in physiological and in pathological conditions.

    View details for DOI 10.3390/e23091185

    View details for Web of Science ID 000699548000001

    View details for PubMedID 34573810

    View details for PubMedCentralID PMC8465838

  • Waves in the cochlea and in acoustic rainbow sensors WAVE MOTION Marrocchio, R., Karlos, A., Elliott, S. 2021; 106