Giacomo Indiveri

What I cannot create I do not understand. – R. Feynman, 1988

Short Biography

Giacomo Indiveri is a Professor at the Faculty of Science at the University of Zurich, Switzerland. He obtained an M.Sc. degree in electrical engineering and a Ph.D. degree in computer science from the University of Genoa, Italy. Indiveri was a post-doctoral research fellow in the Division of Biology at Caltech and at the Institute of Neuroinformatics of the University of Zurich and ETH Zurich. In 2006 he attained the “habilitation” in Neuromorphic Engineering at the ETH Zurich Department of Information Technology and Electrical Engineering. He won an ERC Starting Grans on “Neuromorphic processors” in 2011 and an ERC Consolidator Grant on neuromophic cognitive agents in 2016. His research interests lie in the study of neural computation, with particular interest in spike-based learning and selective attention mechanisms, and in the hardware implementation of real-time sensory-motor systems using analog/digital neuromorphic circuits and emerging VLSI technologies.

Research field and activities

  • Neuromorphic circuits
  • Neural processing systems
  • Autonomous cognitive agents
  • Computational neuroscience models
  • Spike-based learning models, circuits, and systems

Contact

Institute of Neuroinformcatics
University of Zurich and ETH Zürich
giacomo@ini.uzh.ch

References

  • Marc Osswald, Sio-Hoi Ieng, Ryad Benosman, and Giacomo Indiveri. A spiking neural network model of 3D perception for event-based neuromorphic stereo vision systems. Scientific Reports 7(40703)1-12, 2017.
  • R. Berdan, E. Vasilaki, A. Khiat, G. Indiveri, A. Serb, and T. Prodromakis. Emulating short-term synaptic dynamics with memristive devices. Scientific Reports, 6(18639):1–9, 2016.
  • Hesham Mostafa, L. K. Muller, and Giacomo Indiveri. Rhythmic inhibition allows neural networks to search for maximally consistent states. Neural Computation, 27:2510–2547, 2015.
  • Hesham Mostafa, L. K. Muller, and Giacomo Indiveri. An event-based architecture for solving constraint satisfaction problems. Nature Communications, 6:1–10, 2015.
  • Hesham Mostafa, Ali Khiat, Alexander Serb, Christian G Mayr, Giacomo Indiveri, and Themis Prodromakis. Implementation of a spike-based perceptron learning rule using TiO2x memristors. Frontiers in Neuroscience, 9(357), 2015.
  • F. Corradi and G. Indiveri. A neuromorphic event-based neural recording system for smart brain-machine-interfaces. Biomedical Circuits and Systems, IEEE Transactions on, 9(5):699–709, 2015.
  • G. Indiveri and S.-C. Liu. Memory and information processing in neuromorphic systems. Proceedings of the IEEE, 103(8):1379–1397, 2015.
  • Ning Qiao, Hesham Mostafa, Federico Corradi, Marc Osswald, Fabio Stefanini, Dora Sumislawska, and Giacomo Indiveri. A re-configurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128k synapses. Frontiers in Neuroscience, 9(141), 2015.
  • E. Chicca, F. Stefanini, C. Bartolozzi, and G. Indiveri. Neuromorphic electronic circuits for building autonomous cognitive systems. Proceedings of the IEEE, 102(9):1367–1388, Sep 2014.
  • E. Neftci, J. Binas, U. Rutishauser, E. Chicca, G. Indiveri, and R. Douglas. Synthesizing cognition in neuromorphic electronic systems. Proceedings of the National Academy of Sciences, 110(37):E3468–E3476, 2013.
  • G. Indiveri, B. Linares-Barranco, T.J. Hamilton, A. van Schaik, R. Etienne-Cummings, T. Delbruck, S.-C. Liu, P. Dudek, P. Hafliger, S. Renaud, J. Schemmel, G. Cauwenberghs, J. Arthur, K. Hynna, F. Folowosele, S. Saighi, T. Serrano-Gotarredona, J. Wijekoon, Y. Wang, and K. Boahen. Neuromorphic silicon neuron circuits. Frontiers in Neuroscience, 5:1–23, 2011.