This course teaches the foundation of Deep Learning for beginners in machine learning and also advanced students.
This course teaches the foundation of Deep Learning and advanced neural networks for a more experienced audience.
This course teaches the fundamental engineering and mathematical tools to understand and analyze basic bioelectricity and circuit theory in the context of the mammalian nervous system. The course shows for the similarities between electricity in biology and in silicon circuits, enabling students to begin interfacing the two in simple recording and stimulating experiments. A solid quantitative understanding of electric phenomenon in the context of the biological system is essential for designing many devices for biomedical diagnosis, treatment, and beyond. This class gives the theoretical framework needed to begin exploring electrophysiological devices with biomedical engineering applications. Neural network models and learning techniques will be used to model cortical sensory processing of neural circuits.
This course will provide students with the knowledge of neuromorphic circuits and systems for applications in synthetic and artificial vision and sensory systems. This course will teach how to use and write software and hardware models of the human visual system, retinal pre-processing, and vision sub-blocks. Lecture will include an overview of current neuromorphic systems, new opportunities for these systems and ideas for innovation and success with such systems. We will include details on neural networks, digital systems design, digital synthesis in programmable logic, and large scale integration. System level lectures will be in the form of recent paper review and discussion.
This course focuses on the new silicon-on-sapphire CMOS technology. The course will provide students with the knowledge of the silicon-on-sapphire technology, its devices and circuits. The course teaches basic integrated analog blocks and how to combine these circuits into systems for sensing and biomedical application. Target areas are in physiology, brain-machine interfaces, neural recording and stimulation, imaging and bio-imaging. Lecture will include details on operational amplifiers, voltage amplifiers, current mode circuits, analog to digital converters, photo-transduction circuits, layout, simulation and design of VLSI circuits and systems. System level lectures will be in the form of recent paper review and discussion. The class will mainly admit a graduate audience.
This course will provide students with the knowledge of basic integrated analog blocks and how to combine these circuits into sensory systems for biomedical applications. Target areas are in physiology, brain-machine interfaces, neural recording and stimulation, imaging and bio-imaging. Lecture will include details on operational amplifiers, voltage amplifiers, current mode circuits, analog to digital converters, photo-transduction circuits, layout, simulation and design of VLSI circuits and systems. System level lectures will be in the form of recent paper review and discussion. The class will also admit a graduate audience.
Designed to develop engineering skills in the design and analysis of digital logic components and circuits, to make students thoroughly familiar with the basics of gate-level circuit design starting from single gates and building up to complex systems, and to provide hands-on experience and exposure to circuit design using state-of-the-art computer aided design tools and programmable logic devices using VHDL.
Neuromorphic analog integrated circuit design, fabrication processes, fundamentals of devices, circuits and basic topologies. Analog and mixed-signal VLSI and SOC for biomedical instrumentation and bio-inspired circuits. System-level design, simulation, layout and tapeout. Examples of VLSI systems for biomedical applications: models of biological systems and circuit implementation. Biomedical sensors, SNR and electronic circuit noise. Sensor arrays, communication and analog-digital circuit interaction and co-design. Signal conversion, conditioning, compression and reconstruction.