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Brain Engineering Laboratory: Frequently Asked Questions
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<font color="#996666" face="Arial, Helvetica, sans-serif">
Q) What is "brain engineering"?
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<P>
The Brain Engineering Laboratory has as its goal a fundamental understanding
of the brain: its mechanisms, operation, and behaviors. There has been
explosive growth of information about the brain from a broad range of fields
including neuroanatomy, physiology, biochemistry and behavior, and tools from
mathematics, computer science and engineering are brought to bear to make
sense of the voluminous data. Our laboratory contributes to these fields and
uses these data in an integrative way to construct hypotheses of how the brain
operates to enable us to think, perceive, feel, and act. Inevitably, as a scientific
field arrives at an understanding of its object of study, we are able to use the
information in a proactive way: to construct synthetic models of the system, to
enhance its effectiveness, and to fix it when it breaks. For instance, as biological
systems have become increasingly understood, it has become possible to
diagnose diseases, to develop drugs to treat them, and to build devices that
mimic and can even supplant their operation, such as artificial hearts and limbs.
The fields of medicine and pharmacology have grown from these fundamental
biological findings. The future of brain science will be no less productive, and no
less dramatic in its effect on our understanding, and its influence on our day to
day lives.
<P>
<p>
<p>
<P>
<font color="#996666" face="Arial, Helvetica, sans-serif">
Q) How are "Brain Engineering" and "Neural Networks" related?
</font>
<font color="#999999" face="Arial, Helvetica, sans-serif">
<P>
A) The field of neural networks began as psychologists and artificial intelligence
researchers had been constructing computer models of human behavior, and
noted a number of key facts:
<DL>
<DT>
i)
<DD>Tasks that were easiest for humans were hardest for computers (e.g.,
perception, language, planning), and tasks that were easiest for computers (e.g.,
"expert" systems, medical diagnosis, game playing) were hardest for humans.
<DT>
ii)
<DD>Difficult tasks such as recognizing a complex image (e.g., a face) could be
accomplished by people in less than half a second; but brain cells (neurons)
could only respond about every 10 milliseconds (one hundredth of a second),
leaving time for only about 50-100 steps to accomplish recognition. No one yet
knows of a computer program that could carry out such a complex task in just
100 steps. Therefore, the fact that millions of neurons operate together, in
parallel with each other, may somehow be a key.
<DT>
iii)
<DD>Parallel computers were becoming possible to build, and all computers
were becoming much faster, enabling larger and more complex systems to be
built.
</dl>
<P>
This led to the design of software systems that borrowed characteristics from
brains, to explore new ways of computing. These systems, variously termed
"massively parallel" computing, "parallel distributed processing (PDP),"
"connectionism," "brain-style" or "brain-like" computation, "artificial neural
networks," or, eventually, just "neural networks," contained a few shared
elements: many simple processors (like neurons), which could only process
simple numbers (scalars), extensive connectivity among processors, parallel
operation of all processors.
<P>
Powerful statistical methods were developed within this framework, and the
results have become used in a range of applications. The question remained of
the relationship between these new methods, and the mechanisms actually being
carried out in the human brain. Were the "artificial neural network" systems
capturing the essence of brain computation, and only omitting characteristics that
had biological roles, but added no important computational power?
<P>
Our lab and others investigate detailed designs of real brain areas, taking
advantage of an explosion of new data and insights from the growing fields of
neurobiology. We have found that hitherto ignored details of the anatomical
wiring diagrams and physiological operating mechanisms of brain circuits
suggest powerful algorithms that differ substantially from those in neural
networks, and indeed were unexpected from psychological or neuroscience
studies. For example, our models of superficial cortical layers perform the
unexpectedly complex task of hierarchical clustering <i><font
color="#9999ff">[Ambros-Ingerson, J., Granger, R., and Lynch, G. (1990). Simulation of
paleocortex performs hierarchical clustering. Science, 247: 1344-1348]<font
color="#999999"> </i>; our cortical deep-layer models perform a type of hash
coding <i> <font color="#9999ff">[Aleksandrovsky, B., Whitson, J., Garzotto, A, Lynch,
G., Granger, R. (1996). An algorithm derived from thalamocortical circuitry stores and retrieves
temporal sequences. IEEE Comp.Soc.Press, Proc. Int’l Conf. Pattern Recog., 4: 550-554]<font
color="#999999"> </i>; hippocampal field CA3 performs time dilation <i> <font
color="#9999ff">[Granger, R., Wiebe, S., Taketani, M., Ambros-Ingerson, J., Lynch, G.
(1997). Distinct memory circuits comprising the hippocampal region. Hippocampus, 6: 567578]<font color="#999999"> </i>; field CA1 performs sequence chaining <i> <font
color="#9999ff">[Granger, R., Whitson, J., Larson, J. and Lynch, G. (1994). Non-Hebbian
properties of LTP enable high-capacity encoding of temporal sequences. Proc. Nat'l. Acad. Sci.,
91: 10104-10108]<font color="#999999"> </i>; the basal ganglia carry out a form of
reinforcement learning, and so on. Each of these algorithms, emergent from
different brain structures, adds to the "tool kit" or instruction set of processes that
might be carried out by the brain, and the resulting models have many attractive
abilities and unusually low computational costs compared to other methods,
including neural network methods.
<P>
</font>
 
<P>
<font color="#996666" face="Arial, Helvetica, sans-serif">
Q) How do you know that the brain areas you study are actually carrying out the
computational operations that you think they are?
</font>
<font color="#999999" face="Arial, Helvetica, sans-serif">
<P>
A) Of course, we don't know; we and all scientists generate hypotheses from the
extant data, and continue to test the hypotheses against new data as it arises.
We study particular brain circuits "bottom up", hoping that a circuit's natural
operation will suggest the computation that it is carrying out. As we construct
simplified models, we try to be alert to biological features that, if added in, are
consistent with (or even enhance) the hypothesized functions, or are
inconsistent. We also attempt as much as possible to identify predictions arising
from the models that can be tested via biological or behavioral means, though it
is rare that models are able to make specific enough predictions, or that such
predictions are testable with any current methods. Nonetheless, as new
biological data occur, we continue to check the model against the known
constraints, to either strengthen the model or modify it, or, if necessary, discard a
refuted model for a particular brain structure, and begin again.
<P>
</font>
 
<P>
<font color="#996666" face="Arial, Helvetica, sans-serif">
Q) What experimental predictions of your models have you tested, either
behaviorally or biologically?
</font>
<font color="#999999" face="Arial, Helvetica, sans-serif">
<P>
A) In the olfactory system, we have studied behavioral <i> <font
color="#9999ff">[Granger, R., Staubli, U., Powers, H., Otto, T., Ambros-Ingerson, J., and
Lynch, G. (1991). Behavioral tests of a prediction from a cortical network simulation. Psychol.
Sci., 2: 116-118]<font color="#999999"> </i> and physiological predictions
<i><font color="#9999ff">[McCollum, J., Larson, J., Otto, T., Schottler, F. Granger, R., and
Lynch, G. (1991). Short-latency single-unit processing in olfactory cortex. J. Cog. Neurosci., 3:
293-299]<font color="#999999"> </i>; in the hippocampus we have identified a sequencedependent form of long-term potentiation <i> <font color="#9999ff">[Granger, R., Whitson,
J., Larson, J. and Lynch, G. (1994). Non-Hebbian properties of LTP enable high-capacity
encoding of temporal sequences. Proc. Nat'l. Acad. Sci., 91: 10104-10108]<font
color="#999999"> </i>; and in behavioral tests of human visual thalamocortical processing,
we have recently verified predictions from a thalamocortical model <i>[Granger et al.,
submitted]<font color="#999999"> </i>. In addition, we have done extensive behavioral
studies of the nature of glutamatergic neurotransmitter receptors in the process of learning, via
specific pharmacological manipulation of these receptors in animals <i> <font
color="#9999ff">[Granger, R., Deadwyler, S., Davis, M., Moskowitz, B., Kessler, M., Rogers,
G., and Lynch, G. (1996). Facilitation of glutamate receptors reverses an age-associated memory
impairment in rats. Synapse, 22: 332-337]<font color="#999999"> </i> and in young and
aged human subjects <i> <font color="#9999ff">[Lynch, G., Kessler, M., Rogers, G.,
Ambros-Ingerson, J., Granger, R. and Schehr, R. (1996). Psychological effects of a drug that
facilitates brain AMPA receptors. Int J Clinical Psychopharmacol, 11: 13-19; Ingvar, M.,
Ambros-Ingerson, J., Davis, M., Granger, R., Kessler, M., Rogers, G, Schehr, R., and Lynch., G.
(1997). Enhancement by an ampakine of memory encoding in humans. Exper. Neurol., 146:
553-559; Lynch, G., Granger, R., Davis, M., Ambros-Ingerson, J., Kessler, M., Schehr, R.
(1997). Evidence that a positive modulator of glutamate receptors improves recall in elderly
human subjects. Experimental Neurol., 145: 89-92]<font color="#999999"> </i>.
<P>
</font>
 
<P>
<font color="#996666" face="Arial, Helvetica, sans-serif">
Q) If your models are actually doing what brain circuits do, then do they turn out
to be useful methods for applications?
</font>
<font color="#999999" face="Arial, Helvetica, sans-serif">
<P>
A) The algorithms derived from various brain areas have turned out to be so
unexpectedly effective and efficient that they have found use in a variety of realworld applications, ranging from military to industrial to medical uses. As
examples, the Navy has used our systems to process signals from radar and
other signal detectors <i> <font color="#9999ff">[Kowtha, V., Satyanarayana, P.,
Granger, R., and Stenger, D. (1994). Learning and classification in a noisy environment by a
simulated cortical network. Proc. Third Ann. Comp. & Neural Systems Conf., Boston: Kluwer,
pp. 245-250]<font color="#999999"> </i>; and a hardware and software system derived from
our cortical models has been used to analyze EEG data in normal and early Alzheimer's subjects,
as a potential device for aiding clinicians in the early detection of Alzheimer's Disease <i> <font
color="#9999ff">[Benvenuto, J., Jin, Y., Casale, M., Lynch, G., Granger, R. (2002).
Identification of diagnostic evoked response potential segments in Alzheimer’s Disease. Exper.
Neurology, 176: 269-276; Granger, R. (2001). Method and computer program product for
assessing neurological conditions and treatments using evoked response potentials. U.S. Patent #
6,223,074 (54 claims)]<font color="#999999"> </i>.
<P>
(See the "Applications" page on this site.)
<P>
</font>
 
<P>
<font color="#996666" face="Arial, Helvetica, sans-serif">
Q) What does "brain engineering" tell us about everyday thought?
</font>
<font color="#999999" face="Arial, Helvetica, sans-serif">
<P>
If each sub-component of the brain has a particular engineering function, then
what does that imply for the real use of the brain, which is thinking? In other
words, as we come to understand more about the mechanisms of the brain, will
that help us understand how we think? The answer is yes.
<P>
When we "think" about something, like getting ready for work in the morning, we
are actually using a large number of distinct brain engines, all in concert, and it is
their combined activity that is thought. Each of the different brain areas modeled
gives rise to a different constituent activity of thought, and areas working together
can produce computational algorithms that are different from either of the parts
independently. The overall goal of brain engineering is to understand the nature
of thought in terms of its constituent brain processes. Significant strides have
been taken, but this is a goal that may take many, many years to achieve.
<P>
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