| Neuromorphing
- Building Brains in Silicon (BE 526)
Kwabena Boahen, PhD Department of Bioengineering boahen@seas.upenn.edu Description: We introduce neurobiologists to the physical constraints on neural computation—namely, noise, wiring, and energy. We introduce engineers to the unrivaled performance of biological systems—achieved by using physical resources efficiently. We pursue these goals by studying large-scale models of entire neural systems, consisting of thousands of silicon neurons that respond in real-time. Students conduct analytical (deriving mathematical solutions), computational (simulating circuit behavior), and experimental (testing prefabricated chips) exercises. They work in multidisciplinary teams, combining their expertise in neuroscience and neuroengineering, and thus rudimentary knowledge of one or the other is fine. Prerequisites: Students with advanced knowledge in neurobiology but rudimentary knowledge in electrical engineering or vice versa are welcome. Biology students should have a course in Cellular Neurobiology (e.g., BIOL251 or INSC572). A course in Systems Neuroscience (e.g., BIOL451 or INSC573) is recommended but not required. Engineering students should have a course in Solid-State Circuits (e.g., EE319). A course in Integrated Circuits (e.g., EE419, 560, or 562) is recommended but not required. Goals: To capture the structure and function of entire neural systems in real-time using microelectronic devices. To build these neuromorphic models, we proceed from the device level, through the circuit level, to the system level. At the device level, we draw parallels between electrodiffusion of electrons through transistor channels and electrodiffusion of ions through membrane channels. At the circuit level, we implement synaptic interaction, dendritic integration and active membrane behavior using transistors. At the system level, we synthesize the spatiotemporal dynamics of the cochlea, the retina, and networks of spiking neurons in cortex. Textbooks: None required. But, the monograph, Analog VLSI and Neural Systems, by Carver Mead, is a good introduction to Very Large Scale Integrated electronic systems. The book, From Neuron to Brain, by Kuffler, Nicholls and Martin, is a good introduction to the brain. Grading: Based on individual homework and team lab reports (groups of two). Target Audience: This course is intended to draw advanced students from multiple disciplines with an interest in bridging disciplines. Students are encouraged to pool their expertise in different areas in teams of two. Topics: |
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