REFERENCES

The SpiNNaker book: SpiNNaker - a spiking neural network architecture

Open access: http://dx.doi.org/10.1561/9781680836523

The SpiNNaker Project, Proceedings of the IEEE (Volume: 102, Issue: 5, May 2014)

Open access: https://ieeexplore.ieee.org/document/6750072

Large-scale neuromorphic computing systems, Steve Furber 2016 J. Neural Eng. 13 051001

Open access: https://iopscience.iop.org/article/10.1088/1741-2560/13/5/051001/meta

Video Demos:

Demo 1

Demo 2

The caviar project:

http://www.imse-cnm.csic.es/caviar/videos/index.htm

Principles of Neural Design. Sterling, Peter, and Simon Laughlin. MIT Press, 2015.

The Human Advantage. Herculano-Houzel, Suzana. MIT Press, 2016.

Spin torque building blocks: https://www.nature.com/articles/nmat3823

Spintronic Nanodevices for Bioinspired Computing: https://ieeexplore.ieee.org/document/7563364

Neuromorphic Spintronics: https://www.nature.com/articles/s41928-019-0360-9

Physics for neuromorphic computing: https://www.nature.com/articles/s42254-020-0208-2

Sebastian et al., Memory devices and applications for in-memory computing, Nature Nanotechnology (2020) https://www.nature.com/articles/s41565-020-0655-z?proof=t

Memristive Devices for Brain-Inspired Computing, 1st Edition

From Materials, Devices, and Circuits to Applications - Computational Memory, Deep Learning, and Spiking Neural Networks

Editors: Sabina Spiga, Abu Sebastian, Damien Querlioz, Bipin Rajendran; Paperback ISBN: 9780081027820; eBook ISBN: 9780081027875. Imprint: Woodhead Publishing. Published Date: 12th June 2020. Page Count: 564

https://www.elsevier.com/books/memristive-devices-for-brain-inspired-computing/spiga/978-0-08-102782-0

J.Frascaroli, S. Brivio, E. Covi, and S. Spiga, “Evidence of soft bound behaviour in analogue memristive devices for neuromorphic computing”, Scientific Reports Vol. 8, 7178 (2018). DOI: 10.1038/s41598-018-25376-x

https://www.nature.com/articles/s41598-018-25376-x

E. Covi, R. George, J. Frascaroli, S. Brivio, C. Mayr, H. Mostafa, G. Indiveri and S. Spiga, “Spike-driven threshold-based learning with memristive synapses and neuromorphic silicon neurons”, Journal of Physics D: Applied Physics Vol. 51 Page 344003 (2018). DOI: /doi.org/10.1088/1361-6463/aad361

https://iopscience.iop.org/article/10.1088/1361-6463/aad361/meta?casa_token=vej0ZLpvEmQAAAAA:W3FLWy0dCzhaK9E8IQNr0a0q_xHJhFE_FZNq-ZFhr1iV_G7M4oskoO7Qjo32Cfm7sQSwZX6gMz4c

Z. Wang et al., "Resistive switching materials for information processing", Nature Reviews Materials volume 5, pages173–195(2020)

https://idp.nature.com/authorize/casa?redirect_uri=https://www.nature.com/articles/s41578-019-0159-3&casa_token=veKVkzkLAA0AAAAA:fY858Olu9B84EMio9p1PvHwjVcWWKchqj7ZJ76l_zgVwG12qO6ewDdKz-g-cqc620ezqRSrZYXdImACVWw

Educational Colloquim Slides

Videos:

https://www.youtube.com/watch?v=QZnKoS5rBs0

https://www.youtube.com/watch?v=QucFjyErXgc

Tools:

OpenNAS: https://github.com/RTC-research-group/OpenNAS

NAVIS: https://github.com/jpdominguez/NAVIS-Tool

pyNAVIS: https://github.com/jpdominguez/pyNAVIS

Papers:

https://ieeexplore.ieee.org/document/7523402 https://ieeexplore.ieee.org/document/6869048s https://ieeexplore.ieee.org/document/6658899 https://ieeexplore.ieee.org/document/7574309 https://ieeexplore.ieee.org/document/8472250 https://www.frontiersin.org/articles/10.3389/fnins.2018.00198/full https://www.sciencedirect.com/science/article/abs/pii/S0925231220319627 https://www.sciencedirect.com/science/article/abs/pii/S0925231216315624

Educational Colloquim Slides

Soft approach:

1) Dabbous A., Mastella M., Natarajan A., Valle M., Chicca E., Bartolozzi, C., Artificial Bio-inspired Tactile Receptive Fields for Edge Orientation Classification. In 2021 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE.

2) Parvizi-Fard, A., Amiri, M., Kumar, D., Iskarous, M. M., & Thakor, N. V. (2021). A functional spiking neuronal network for tactile sensing pathway to process edge orientation. Scientific reports, 11(1), 1-16.

3) Mazzoni, A., Oddo, C. M., Valle, G., Camboni, D., Strauss, I., Barbaro, M., ... & Micera, S. (2020). Morphological neural computation restores discrimination of naturalistic textures in trans-radial amputees. Scientific reports, 10(1), 1-14.

4) Bergner, F., Dean-Leon, E. and Cheng G. "Design and Realization of an Efficient Large-Area Event-Driven E-Skin." Sensors 20, no. 7 (2020): 1965.

Neuromorphic approach:

1) John, R. A., Tiwari, N., Patdillah, M. I. B., Kulkarni, M. R., Tiwari, N., Basu, J., ... & Mathews, N. (2020). Self healable neuromorphic memtransistor elements for decentralized sensory signal processing in robotics. Nature communications, 11(1), 1-12.

2) Caviglia, S., Pinna, L., Valle, M., & Bartolozzi, C. (2016). Spike-based readout of POSFET tactile sensors. IEEE Transactions on Circuits and Systems I: Regular Papers, 64(6), 1421-1431.

3) Lee, W. W., Tan, Y. J., Yao, H., Li, S., See, H. H., Hon, M., ... & Tee, B. C. (2019). A neuro-inspired artificial peripheral nervous system for scalable electronic skins. Science Robotics, 4(32).

4) Ward-Cherrier, B., Pestell, N., & Lepora, N. F. (2020, May). NeuroTac: A neuromorphic optical tactile sensor applied to texture recognition. In 2020 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2654-2660). IEEE.

Educational Webinar Slides

Educational Webinar Jupyter Notebook  Tutorial 1

Educational Webinar Jupyter Notebook  Tutorial 2