Neural Systems Engineering

  • Steve Furber
  • Steve Temple
Part of the Studies in Computational Intelligence book series (SCI, volume 115)

Biological brains and engineered electronic computers fall into different categories. Both are examples of complex information processing systems, but beyond this point their differences outweigh their similarities. Brains are flexible, imprecise, error-prone and slow; computers are inflexible, precise, deterministic and fast. The sets of functions at which each excels are largely non-intersecting. They simply seem to be different types of system. Yet throughout the (admittedly still rather short) history of computing, scientists and engineers have made attempts to cross-fertilize ideas from neurobiology into computing in order to build machines that operate in a manner more akin to the brain. Why is this?

Part of the answer is that brains display very high levels of concurrency and fault-tolerance in their operation, both of which are properties that we struggle to deliver in engineered systems. Understanding how the brain achieves these properties may help us discover ways to transfer them to our machines. In addition, despite their impressive ability to process numbers at ever-increasing rates, computers continue to be depressingly dumb, hard to use and totally lacking in empathy for their hapless users. If we could make interacting with a computer just a bit more like interacting with another person, life would be so much easier for so many people.


Neural System Logic Gate Spike Neural Network Leaky Integrator Spike Event 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Steve Furber
    • 1
  • Steve Temple
    • 1
  1. 1.School of Computer ScienceUniversity of ManchesterManchesterUK

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