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Dear colleagues:<br>
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Please give your views and suggestions so that we can improve the
BMI web in its page: Why-Me? <br>
<br>
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<h4 align="center">I Am an Electrical Engineer</h4>
<p align="left">Electrical Engineering (EE) and Computational
Intelligence (CI) researchers typically have knowledge background
in connectionist (neural net) approach. They account for a major
force of research on control systems, communication systems, and
artificial neural nets. However, they typically do not have
sufficient background in automata theory and symbolic artificial
intelligence in CS. How do brain networks deal with abstraction
and reasoning that traditional neural nets do not perform well, as
Marvin Minsky 1991 correctly criticized? Furthermore, many EE
researchers do not have sufficient knowledge in biology,
psychology, and neuroscience. How can the brain-like networks
(Weng 2010) that use emergent representations and numeric
computations deal with general-purpose symbolic problems,
including abstraction and reasoning? Such new knowledge will
likely solve many currently open EE problems, such as
general-purpose nonlinear control, signal detection and
prediction, optimal nonlinear system approximation, and
brain-scale VLSI circuits. However, EE and CI researchers need to
first learn knowledge in CS, psychology, neuroscience and biology
before they can solve those open problems. Computational
understanding of brain-mind is expected to drastically change the
“landscape” of EE and Computational Intelligence. </p>
<h4 align="center">Why Learning Electrical Engineering?</h4>
<p align="left">When a human thinks, he often uses a language to
organizes his thought. As the language can be written as symbols
(e.g., English), it is natural for him to mistake his symbolic
ways of representing a problem as what brain does inside its
skull. Electrical engineering uses mathematical tools to describe
complex electrical and electronic systems, which are often not
symbolic in nature (e.g., radio and radar). The field of
electrical engineering has developed a series of methods and
mathematical tools to model, analyze, approximate, and implement
highly complex systems. The most successful type is a class of
systems called linear systems. For example, Kalman filter is a
linear dynamic system, which has vector input, vector output, and
a linear internal system that dynamically change through time
(called dynamic system). Although the brain is neither a Kalman
filter nor a nonlinear extension of Kalman filter, the system
knowledge studied in electrical engineering is a necessary
background for anybody who wants to understand biology (brain or
body), neuroscience, artificial intelligence, and a new kind of
mathematics that brain tells us. </p>
<pre class="moz-signature" cols="72">--
--
Juyang (John) Weng, Professor
Department of Computer Science and Engineering
MSU Cognitive Science Program and MSU Neuroscience Program
3115 Engineering Building
Michigan State University
East Lansing, MI 48824 USA
Tel: 517-353-4388
Fax: 517-432-1061
Email: <a moz-do-not-send="true" class="moz-txt-link-abbreviated" href="mailto:weng@cse.msu.edu">weng@cse.msu.edu</a>
URL: <a moz-do-not-send="true" class="moz-txt-link-freetext" href="http://www.cse.msu.edu/%7Eweng/">http://www.cse.msu.edu/~weng/</a>
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