Today's most powerful computers are still outperformed by biological brains in
routine functions such as vision, audition, and motor control. The reasons for
this gap between biological and artificial systems are not fully understood.
Understanding the principles of biological computation and how to implement them
in hardware, is crucial for developing novel techniques for information
Neuromorphic engineering attempts to use the principles and style of
computation observed in biology. Neuromorphic systems emphasize distributed,
collective, self-organized, eventdriven mechanisms. The building blocks of these
systems are analog circuits in which transistors are mostly operated weak
inversion (below threshold), where their exponential I-V characteristics and low
currents can be exploited. These features allow the implementation of massively
parallel, low-power spiking recurrent neural networks as well as ecient sensors.
Students will acquire basic analog design skills necessary to understand the
building blocks of neuromorphic engineering. They will become familiar with the
most widely used neuronal and synaptic circuits.