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All your inputs have been taken into account at the current BMI web
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Please further give your views and suggestions so that we can
improve the BMI web in its page: Why-Me? <br>
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<h4 align="center">I Am a Neuroscientist</h4>
<p align="left">The genomic equivalence principle (Purves et al.
2004) seems to imply two basic principles of the brain
computation, <em>emergence</em> and <em>in-place</em>. The <em>emergence</em>
means that all brain areas and their functions emergence from
experience (Sur & Leamey 2001, Elman et al. 2007, Weng 2010),
not completely pre-defined by the genome. In particular, each
neurons seems to represent a cluster in its sensorimotor input
space (containing both sensors and effectors), instead of
precisely a feature or object in the extra-body environment (e.g.,
not edge orientations in V1, not faces in IT). The <em>in-place</em>
means that the brain learning is <em>cell-centered</em> — each
cell in-place is responsible for not only its computation but also
its learning. However, even if we agree on these facts, currently
few neuroscientists believe and understand that such low-level
cell mechanisms are sufficient to give rise to the rich complex
brain regions, brain wiring, and brain-mind behaviors through
experience in the modern societies. Indeed, such low-level cell
mechanisms seem sufficient! The process of brain development and
adaptation is highly quantitative in nature. Therefore, knowledge
in EE, CS, and mathematics is necessary for neuroscience
researchers to go beyond the current mode of phenomenology — a
path any science must go through from phenomena to causality. A
deeper, computational causality is likely to guide and improve
experimental designs by experimentalists in neuroscience research.
</p>
<h4 align="center">Why Learning Neuroscience?</h4>
<p align="left">Unfortunately, many psychologists are satisfied with
an account for observed brain's external behaviors, but have not
spent sufficient efforts to study the literature about details
inside the brain. Many EE researchers contend with artificial
neural networks without studying whether their networks are
consistent with the neuroscience literature. Many researchers in
artificial intelligence motivate their work by what the brain can
do superficially, but not how the brain does it inside the skull.
It is true that much of the literature in neuroscience is
concerned with many biological details, which do not directly tell
us how the brain works. However, such rich details in neuroscience
provide important constraints for us to rethink the traditional
models in biology, Psychology, CS, EE, and mathematics. For
example, in psychology, there are models for sensitization,
habituation, classical conditioning, instrumental conditioning,
extinction, blocking, homeostasis, cognitive learning, language
understanding, and so on. However, each of such models is based on
a different symbolic model, but the brain uses a single framework
to do them all! It seems time for us to study how a single
brain-model does them all and integrate all. Many well-known
leaders in neural network research and neuroscience modeling still
regard such a research goal to be a fantasy at this stage of
knowledge. An applicant who has received BMI 6DC will think
otherwise.</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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