Neuromorphic Engineering Overview

Neuromorphic engineering, also known as neuromorphic computing, is a concept developed by Carver Mead, in the late 1980s, describing the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system.

In recent times the term neuromorphic has been used to describe analog, digital, and mixed-mode analog/digital VLSI and software systems that implement models of neural systems (for perception, motor control, or multisensory integration).

IBM’s TrueNorth – The first neuromorphic chip

IBM has recently released details of a neuromorphic chip named TrueNorth via their website, the press, and a research report in the journal Science. The research team, headed by Dharmendra Modha as part of the DARPA SyNAPSE Program, developed a chip containing a million “programmable spiking neurons” and 256 million synapses. The chips use 5.4 billion transistors on 4096 “neurosynaptic cores” which each has its memory (in the form of connection routing and timing delays) close to its “neuronal” processing units.

“On Intelligence” by Jeff Hawkins

This book is surprisingly good in its ability to reach both the lay reader (for at least the first half) and the reader familiar with neuroscience. Articles since its publications provide much greater detail and are very useful for those interested in going deeper, but On Intelligence serves very well as an introduction to the concepts. The ideas expressed in On Intelligence are important both for scientific advancement and for philosophical consideration. While one could argue that perhaps there are other forms of intelligence or ways to produce intelligence, Hawkins does a good job in arguing what intelligence is in terms of mammalian brains and what the basic neocortical unit does. While Hawkins brings these ideas together in an orderly framework, he does give credit to the many neuroscientists responsible for the various components and underlying ideas that make it possible. These ideas as a whole until recently have not been sufficiently discussed in the neuroscience community in my opinion, and I believe they will aid (and in fact already have aided) greatly in advancing our understanding of the brain and creating real “artificial” intelligence that isn’t actually artificial at all.

The balance between addressing the expert and lay audiences did at …

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