I find neural systems fascinating, and I love to engineer them. I have decades of experience with AI and its applications.
I've worked from machine learning and computational neuroscience, through neuromorphic sensors and processors, to prosthetic systems and wet biology.
In particular, I've developed lots of know-how as a neuromorphic engineer, creating electronic circuits which mimic computation in nervous systems. These circuits, integrated on microchips, often use electrical currents to imitate currents in our nerve cells and brains. I'm therefore skilled at mixed-signal (analogue, asynchronous digital) silicon chip design and test (VLSI - CMOS, CIS), as well as PCBs, FPGA logic and software, from algorithms to applications.
As an entrepreneur and technologist, I've been involved in the commercialisation of neuromorphic vision sensors, also known as event cameras or dynamic vision sensors. This has taken me into dozens of commercial R&D departments and I've developed an unparalleled insight into the current attempts of many major companies to adopt neuromorphic technology.
I've also worked on cloud deployment (AWS) of deep learning models for natural language processing; I've learnt Tensorflow, Lambda serverless compute and Gremlin graph database. This high-level focus on practical applications of neural networks has complemented my understanding of their low-level properties.
Recently I've been further developing applications of event-based perception, for robotics. At IIT our robot is equipped with event-based sensors for both vision and touch. I've been mixing 3D geometrical algorithms and deep learning models for perception.
I see huge potential in neural computation, AI and robotic technology to assist us and improve our lives. I'm also very curious to understand more about how our nervous systems allow us to function, and how our mental lives arise.
Projects (past and present)
Key publications(Further publications are listed in project-specific pages; see also google scholar)
- "A neuro-inspired model-based closed-loop neuroprosthesis for the substitution of a cerebellar learning function in anesthetized rats", Hogri R, Bamford SA, Taub AH, Magal A, Del Giudice P, Mintz M. Scientific Reports, 2015, vol. 5, pp 8451. pdf
- "A VLSI field-programmable mixed-signal array to perform neural signal processing and neural modelling in a prosthetic system", Bamford SA, Hogri R, Giovannucci A, Taub AH, Herreros I, Verschure PFMJ, Mintz M, Del Giudice P. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2012, vol. 20, no. 4, pp. 455-467. pdf correction
- "Spike-timing-dependent plasticity with weight dependence evoked from physical constraints", Bamford SA, Murray AF, Willshaw DJ. IEEE Transactions on Biomedical Circuits and Systems, 2012, vol. 6, no. 4, pp. 385-398. pdf
- "Large Developing Receptive Fields Using a Distributed and Locally Reprogrammable Address-Event Receiver", Bamford SA, Murray AF, Willshaw DJ. IEEE Transactions on Neural Networks, 2010, vol. 21, no. 2, pp. 286-304. pdf
- "Synaptic Rewiring for Topographic Map Formation and Receptive Field Development", Bamford SA, Murray AF, Willshaw DJ. Neural Networks, 2010, vol. 23 pp. 517-527. pdf
- "Intimate mixing of analogue and digital signals in a field-programmable mixed-signal array with lopsided logic", Bamford SA, Giulioni M, IEEE Biomedical Circuits and Systems Conference (BIOCAS), 2010, pp. 234-237. pdf
- "A Sensitive Dynamic and Active Pixel Vision Sensor for Color or Neural Imaging Applications", Moeys D, Corradi F, Li C, Bamford S, Longinotti L, Voigt FF, Berry S, Taverni G, Helmchen F, Delbruck T. IEEE Transactions on Biomedical Circuits and Systems, 2018, vol. 12, no. 1, pp: 123-136. pdf
- "Demonstration: Real-time machine-vision applications on a million-spiking-neuron chip", Merolla P, Alvarez-Icaza R, Amir A, Longinotti L, Bamford S, Taba B, Modha D, Akopyan F Neural Information Processing Systems (NIPS), 2014. Abstract