## Neuroscience for Engineers

## (Page under construction)

This page was started due to the following reasons. Most of the higher brain functions such as perception and memory are first-person inner sensations. Since empirical research to study the generation of these inner sensations cannot be carried out within the biological systems, it is necessary to theoretically derive the mechanism, verify it by testing its predictions, compare circuitries of remote animal species and undertake the gold standard test of its replication in engineered systems. In contrast to biology, engineering and physical sciences deal with virtual items all the time. In this context, it is possible to adapt some of the theoretical methods used in those fields of science. Once a level of confidence is reached, then it will be possible to provide a theoretically fitting mechanism to the engineers for its replication in engineered systems. To achieve this, it is necessary to derive a mechanism that can explain all the features of the system observed at various levels and test whether we can triangulate different findings using the derived mechanism. The features of various loss of function states also provide valuable information towards the verification. Even with this, engineers often would like to look at how the mechanism was derived before even thinking of going to replicate it! Therefore, this page aims to explain the derivation of a probable mechanism to the engineers. This is an essential step and serves two purposes. One, it helps us to get a clear understanding of the experimental steps that are necessary to solve the system and secondly, it provides necessary motivation to undertake the gold standard test of its replication in engineered systems.

“A problem
well stated is a problem half-solved.” - *Charles Kettering*

So let us examine some examples.
Let us see how scientists from basic sciences will approach a complex
problem.
A
mathematician will say "*Find out all the equations and
show me your system of equations*. *Make sure *
to include all
the
(non-redundant)
equations so
that all the variables are included at least once.
Once you are ready,*
try to solve the system*." Since
physicists
carry out
this
approach
all the time,
we can examine how physicist and an engineer
will approach this case.
A
physicist will
say "*You can come up *
with
anything,
preferably an equation^{1}
with only one constant in it. What I will be interested
is to see whether you can explain^{2} all the findings from various levels
with your
equation without
changing that constant*. Call me when you are done*."
Here, one is expected to use all the available information ("equations" in
mathematician's terms) and figure out a solution for the system that
will enable formulating an equation for the system. So this requires
solving the puzzle that can find interconnections between large number
of features of the system to arrive at the correct
solution. An engineer will say, "*Show me
your sketch of the plan. I will verify everything and I want to see it running in an
engineered system*."
Again, the engineer
is examining how various elements are fitting together. All the above
approaches have one message in common - In order to solve any system, we
need to reach a stage where we can interconnect all the elements within it.

The nature of the problem
can be explained
using
a similar example from
the physical and engineering
fields. Imagine that you were educated
up to
the
graduate level in
physics and engineering but without providing
with any information
about electromagnetism (EM)
at any stage of your education.
You only know
the
qualities of
direct current.
You are
not given any access to books describing
EM.
Basically, you were
brought up without exposure to any knowledge about EM. Now you
are
given an
electric fan and
access to a hydroelectric power generator.
You can open both of them and study them to discover
the basic principle of operations
that you don't know yet.
Now you have to
travel in a backward direction towards the basic principle.
Along the way you have to reduce the functions to achieve a
common principle that can explain both the
electric fan and the power generator. How
to assess
whether you are moving in the right direction? One method is to find
whether you
have found out the need
for using
brushes in the
current generator at the hydroelectric power station. If you
figure this out,
then one
can reasonably
be
sure that you have come up with the
alternating
nature of the current. You will be making several correlations and will
be using them to figure out the basic operations. Only when you are able
to explain every one of your findings in an inter-connectable manner,
you will be able to say that you have discovered the mechanism. Various
observations that you will be making provide valuable pieces of the
puzzle that you will be using to adjust your hypothesis several times
towards arriving at a solution. Sometimes, you will find that several of
the pieces of the puzzle are fitting together initially; but soon you
realize the need to dismantle them since you have
another piece of the puzzle that won't fit in any manner in the
remaining space. Eventually, you are likely to arrive at the basic principle of EM even though you
may call it by some other name! If you derive the basic principle
correctly, then you will be trying to examine whether a current carrying
conductor gets defected in a magnetic field and you will
also
verify
whether a current starts to flow in a conductor cutting
a magnetic field.
This is a perfect end!

Here, I will focus on answering the following questions. 1) How neurons generate and transmit potentials? 2) How it is different from electric current? 3) What features makes it possible to translate to electronic circuits? 4) How to explain the seemingly complex brain functions in a simple way and the feasibility to replicate it? 5) How a circuit mechanism was derived by the semblance hypothesis? 6) How can we compare electromagnetism with that of the induction of inner sensations? 7) How does this function relate to the basic electronic circuit principles?

Video presentations

1.
A
testable hypothesis of brain functions

2.
How to study inner sensations? Examples from mathematics

4.
List of third person findings and the derivation of the solution for
the nervous system

6.
Induction of units of inner sensation

8. A potential mechanism for neurodegeneration

9. LTP: An explanation by semblance hypothesis

10. *
A framework for
consciousness*

11. A potential mechanism of anaesthetic agents

(Will continue)

Notes

1. What is there in an equation? An explanation - pdf.

*2. A physicist's way of explaining explanation -
Video.
*
*This presentation naturally
leads to the question, “How can we make
*
hard-to-vary* assertions about the mechanism of brain functions?”
“How can we seek good explanations – the ones that can’t be easily
varied while still
explaining?”
We
c**an
attain the underlined stage only after solving the system,
which is implicit
(In other words, good explanations can come only when the**
correct underlying solution
become possible - that may not be made
explicit while still explaining!)**.
So how
can
we reach a state where
can we solve the system
that
will allow us to continue explaining?
Explanation for the seasons became
possible as we made enough observations, including the tilt of Earth’s
axis. As we made more observations, we were putting them together to
make sense of all those observations. In the case the nervous system, we
have already made very large number of observations at several levels
such as biochemistry, cell biology, electrophysiology, systems
neuroscience, behavior, psychology, consciousness studies, and imaging
studies. At this juncture, our priority should be to attempt to put
those observations together to make
*
*hard-to-vary *
*explanations - an indication that will suggest arriving at a solution
for the system. **
While undertaking this, we
should be prepared to use
unknown factor (*an unseen thing or a factor with an unseen
property or a biological feature that can explain a property that cannot be directly sensed by our sensory systems) in our
attempt to interconnect all the findings.
We can get a sense of it by the following
examples.

A*
system of
algebraic equations having a
unique solution provides an example how
the solution binds the equations within that system of equations.
When we are finding the solution, we are finding how the solution allows
interconnections between the equations, which is the underlying deep
principle (and the beauty) behind a solvable system of equations
(Two methods here:
Video1,
Video2).
Note that a system of algebraic equations having n number of variables
requires a minimum of only n number of equations to solve the system
(for details see video2). It is most likely that observations from the
biological systems won't behave like perfect equations and therefore we
will require much more than n number of observations. But we have the
freedom to choose those fitting ones to solve the system from very large
number of observations. At this stage one may ask, "How can this be
carried out using biological observations?" For each observation, we have
been making causal observations from few other levels. We can assign variables
to these observations and build short equations interconnecting the
causal observations. Next step is to put all the equations to solve the
system. To achieve this, we need to know all the equations and assign
all the variables at appropriate locations within them. While doing
this, we are allowed to keep one unknown variable representing a change
at the correct level that can be formed during learning from which
virtual units of inner sensations can be induced during memory
retrieval. We have the freedom to assign different values to the latter
until we can solve the system. Knowing that we will have a
unique solution helps to narrow down the candidate mechanisms. It will
also give us information about the possible features of the unknown
variable.*

*In this attempt any redundant
equations however informative they may be, do not contribute towards solving the system.
Discovering complex equations containing large number of variables (from
among the n number of variables) also do not contribute towards solving
the system. Given these facts, it is reasonable to assume that experiments
in different fields of brain sciences have reached a saturated phase
(for the purpose of solving the system) in that most often experimental
results explaining correlations or causations between the observations made
from different
levels provide only redundant equations.
At this stage, we should use the
non-redundant findings from various levels
to find the solution.
In
case we do not have sufficient number of equations (observations)
to incorporate certain variables,
attempts to solve the system will reveal it. If we become successful, then the unknown factor (whose
value need to be derived by a combination of induction & trial and error
methods) and its
properties that allow us to interconnect all the findings from various
levels should provide the solution. The
solution can be verified by a) examining all the previous observations
(method of retrodiction) and b) testing its predictions.
Once we know all the variables,
then we will be able to make very large number of new equations.
At that stage, we will be able
to
answer the
question satisfying the
specific condition at the
underlined part,
“How can we seek good
explanations – the ones that can’t be easily varied while still
explaining?” We should make an effort in this
direction at this point of time. If we keep
waiting, new
information from all the levels
will continue to
get accumulated and it will become very
hard to make
such an attempt. *

References

Minsky M (1980) K-lines: a theory of memory. Cognitive Science. 4:117–133
__
Article__

*McDonnell et al (2014) Engineering
Intelligent Electronic Systems Based on Computational Neuroscience.
Proceedings of the IEEE | Vol. 102, No. 5, May 2014
Article*

Vadakkan K.I (2014) An electronic circuit model of the
inter-postsynaptic functional LINK designed to study the formation of
internal sensations in the nervous system.
Advances
in Artificial Neural Systems.
*
Article*

*
Vadakkan K.I*
(2011)
Processing semblances induced through inter-postsynaptic functional
LINKs, presumed biological parallels of K-lines proposed for building
artificial intelligence. Frontiers in Neuroengineering. 4:8
PubMed