Semblance Hypothesis

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 equation1 with only one constant in it. What I will be interested is to see whether you can explain2 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!

In the same way, we have a machinery from which we need to discover the basic operational principle, which will allow us to understand it and fix its problems. In this attempt, the first step is to arrive at the basic operational principle using logical analysis of the findings from various levels and verify whether the derived principle holds true. Similar to the principle of EM that can be demonstrated using simple tools such as a U-shaped magnet, a conductor, and a battery, it may become possible to demonstrate the operational principle of the nervous system using a simple circuit.

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

3. Neurons and Synapses

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

5. Constraints to work with

6. Induction of units of inner sensation

7. Why do we need to sleep?

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)


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 can 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.


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