Distinguished lectures

Copying Brain

Prof. Dr. Donhee Ham,
Harvard University


Institute of Microengineering - Distinguished Lecture

Due to the covid-19 restrictions currently in place, the lecture will take place remotely by zoom only.

Zoom Live Stream: https://epfl.zoom.us/j/934241343

Abstract: Massively parallel, intracellular recording of a large number of mammalian neurons across a network has been a great technological pursuit in neurobiology, but it has not been achieved until our recent breakthrough [1]. For example, the intracellular recording by the patch clamp revolutionized neurobiology with its unparalleled sensitivity that can measure down to subthreshold synaptic activities, but it is too bulky to scale into a dense array, and only ~10 parallel patch recordings have so far been possible. For another example, the microelectrode array (MEA) can record from many more neurons, but this extracellular technique has too low a sensitivity to tap into synaptic events. In this talk, I will share the recent breakthrough of ours [1], a CMOS nanoelectrode array that massively parallelizes the intracellular recording from thousands of connected mammalian neurons. I will also explore the applications of this unprecedented tool in fundamental and applied neurobiology, in particular, functional connectome mapping, high-throughput drug screening for neurological disorder, and copying biological neuronal networks as a possible new synthesis of machine intelligence.

[1] J. Abbott et al, “A nanoelectrode array for obtaining intracellular recordings from thousands of connected neurons,”  Nature Biomed. Eng., doi: 10.1038/s41551-019-0455-7 (2019)

Bio: Donhee Ham is Gordon McKay Professor of Applied Physics and EE at Harvard and Samsung Fellow. He earned a BS in physics from Seoul National University. Following a military service, he went to Caltech for graduate training, where he worked in LIGO under Prof. Barry Barish in physics, and later obtained a PhD in EE winning the Wilts Prize for the best EE thesis. His experiences/recognitions include IBM T. J. Watson Research, distinguished visiting professorship at Seoul National University, IEEE conference committees (e.g., ISSCC), distinguished lecturer for IEEE SSC Society, associate editor for IEEE TBioCAS, IBM faculty fellowship, and MIT TR35. His intellectual focus includes neuro-electronic interface, neuromorphic processor, low-dimensional and quantum devices, NMR technology, and integrated circuits.


Note: The Seminar Series is eligible for ECTS credits in the EDMI doctoral program


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Information and Light in Complex Media

Prof. Dr. Allard Mosk,
Utrecht University


Institute of Microengineering - Distinguished Lecture

Campus Lausanne BM 5202 (live)
Campus Microcity MC B0 302 (video)
Zoom Live Stream: https://epfl.zoom.us/j/119888136

Abstract: Random scattering of light, which takes place in paper, paint and biological tissue is an obstacle to imaging and focusing of light and thus hampers applications ranging from laser ablation to precision measurements. At the same time scattering is a phenomenon of basic physical interest as it allows the study of fascinating interference effects such as open transport channels [1,2], which enable lossless transport of waves through strongly scattering materials. The frequency bandwidth of these channels [3] is critical to their usefulness as it determines their ability to carry pulses and their information-carrying capacity. After a broad overview of the field, we present new measurements of the frequency bandwidth and intensity fluctuations in these channels. Moreover, we show that  optimizing the incident light wave is essential to  extract precise information about the position of any scatterer. The information we retrieve turns out to be limited by our knowledge of the position of the other scatterers and the local density of states [5].

Bio: Allard Mosk (1970) started his physics career in ultracold atomic gases with work in Amsterdam (Ph.D. 1994), Heidelberg, and Paris, performing the first observation of a Feshbach resonance in Li, and of photoassociation of H. In 2003 he joined the Complex Photonic Systems group at the University of Twente. where he pioneered wavefront shaping methods to focus and image through strongly scattering media. Since 2015 he holds a chair at Utrecht University, The Netherlands, where he studies statistical properties of light in complex scattering media with a view on imaging and optical precision measurements.

Note: The Seminar Series is eligible for ECTS credits in the EDMI doctoral program

Note: After the lecture, there will be time for discussion and interaction with the distinguished speaker, sandwich lunch and refreshments sponsored by the Institute of Microengineering will be provided for attendees in front of the lecture hall (BM 5104, ca. 13h15)

References:

  1. A. P. Mosk, A. Lagendijk, G. Lerosey, and M. Fink, Controlling waves in space and time for imaging and focusing in complex media, Nat. Photon., 6, 283 (2012).
  2. I.M. Vellekoop and A.P. Mosk, Universal optimal transmission of light through disordered materials, Phys. Rev. Lett. 101, 120601 (2008).
  3. Jeroen Bosch, Sebastianus A. Goorden, and Allard P. Mosk, Frequency width of open channels in multiple scattering media, Opt. Expr. 24, 26472-26478 (2016)
  4. X. Xu, X. Xie, A. Thendiyammal, H. Zhuang, J. Xie, Y. Liu, J. Zhou, and A. P. Mosk, Imaging of objects through a thin scattering layer using a spectrally and spatially separated reference, Opt. Express 26 (12), 15073–15083 (2018).
  5. D. F. Bouchet, R. Carminati, and A. P. Mosk, Influence of the local density of states on the localization precision of single particles in scattering environments, arXiv. org 1909.02501 (2019).

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Polymer-based artificial synapses: Using protons and electrons to impart plasticity to semiconductors

Prof. Dr. Alberto Salleo,
Stanford University


Institute of Microengineering - Distinguished Lecture

Campus Lausanne BM 5202 (live)
Campus Microcity MC B0 302 (video)
Zoom Live Stream: https://epfl.zoom.us/j/843927942

Abstract: Organic semiconductors have been traditionally developed for making low-cost and flexible transistors, solar cells and light-emitting diodes. In the last few years, emerging applications in health case and bioelectronics have been proposed. A particularly interesting class of materials in this application area takes advantage of mixed ionic and electronic conduction in certain semiconducting polymers. Indeed, the ability to transduce ionic fluxes into electrical currents is useful when interacting with living matter or bodily fluids. My presentation will first discuss the fundamental aspects of how mixed conduction works in polymeric materials and show some applications in biosensing. The bulk of my talk will focus on polymer-based artificial synapses.
The brain can perform massively parallel information processing while consuming only ~1- 100 fJ per synaptic event. I will describe a novel electrochemical neuromorphic device that switches at record-low energy (<0.1 fJ projected, <10 pJ measured) and voltage (< 1mV, measured), displays >500 distinct, non-volatile conductance states within a ~1 V operating range. Furthermore, it achieves record classification accuracy when implemented in neural network simulations. Our organic neuromorphic device works by combining ionic (protonic) and electronic conduction and is essentially similar to a concentration battery. The main advantage of this device is that the barrier for state retention is decoupled from the barrier for changing states, allowing for the extremely low switching voltages while maintaining non-volatility. Our synapses display outstanding speed (<20 ns) and endurance achieving over 109 switching events with very little degradation all the way to high temperature (up to 120°C). These properties, which are unheard of in the realm of organic semiconcuctors, are very promising in terms of the ability to integrate with Si electronics to demonstrate online learning and inference. When connected to an appropriate access device our device exhibits excellent linearity, which is an important consideration for neural networks that learn with blind updates.

Bio: Alberto Salleo is currently Full Professor of Materials Science and Department Chair at Stanford University. Alberto Salleo holds a Laurea degree in Chemistry from La Sapienza and graduated as a Fulbright Fellow with a PhD in Materials Science from UC Berkeley in 2001. From 2001 to 2005 Salleo was first post-doctoral research fellow and successively member of research staff at Xerox Palo Alto Research Center. In 2005 Salleo joined the Materials Science and Engineering Department at Stanford as an Assistant Professor in 2006. Salleo is a Principal Editor of MRS Communications since 2011.While at Stanford, Salleo won the NSF Career Award, the 3M Untenured Faculty Award, the SPIE Early Career Award, the Tau Beta Pi Excellence in Undergraduate Teaching Award, and the Gores Award for Excellence in Teaching, Stanford’s highest teaching award. He has been a Thomson Reuters Highly Cited Researcher since 2015, recognizing that he ranks in the top 1% cited researchers in his field.

Note: The Seminar Series is eligible for ECTS credits in the EDMI doctoral program

Note: After the lecture, there will be time for discussion and interaction with the distinguished speaker, sandwich lunch and refreshments sponsored by the Institute of Microengineering will be provided for attendees in front of the lecture hall (BM 5104, ca. 13h15)


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IMT Distinguished Lecture - Prof. Dr. Martin Kaltenbrunner

Prof. Dr. Martin Kaltenbrunner
Johannes Kepler University Linz


Institute of Microengineering - Distinguished Lecture

Campus Lausanne BM 5202 (live)
Campus Microcity MC B0 302 (video)
Zoom Live Stream:

Abstract:

Bio:

Note: The Seminar Series is eligible for ECTS credits in the EDMI doctoral program

Note: After the lecture, there will be time for discussion and interaction with the distinguished speaker, sandwich lunch and refreshments sponsored by the Institute of Microengineering will be provided for attendees in front of the lecture hall (BM 5104, ca. 13h15)


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