Semblance Hypothesis

After more than a decade of examination by adhering to best available scientific methods1-6, mounting evidence forces me to regard semblance hypothesis as a theory. Despite several open invitations to disprove the hypothesis through both this website and a large number of scientific presentations and peer-reviewed publications, no objections were received. This is a theory of nervous system functions that provides testable predictions (pdf with methods to test them). I sincerely hope that scientific community will use the time-tested method of "testing the predictions of a theory"7 with an aim to disprove it. Please explain the importance of this to your community leaders and policy makers. I thank all those who have supported me during several difficult times of its development - Kunjumon Vadakkan, dated 21st March, 2020


                  1. Strobel N. Method for finding scientific truth. Website

                  2. Strobel N. What is a scientific theory? Website

                  3. Goodstein D (2007) A testable prediction. Nature Phys. 3:827 Article

                  4. Lee AS, Briggs RO, Dennis AR (2014) Crafting theory to satisfy the requirements of explanation. Article

                  5. Lee AS, Hovorka DS. (2015) Crafting theory to satisfy the requirements of interpretation. Article

                  6. Dutailly JC (2017) Chapter 1. What is science? Theoretical Physics. p1-24.

                  7. Bialek W (2018) Perspectives on theory at the interphase of physics and biology. Rep. Prog Phys. 81(1):0126001 Article


Lessons from related fields

    This page examines how methods used in other fields of science can be utilized to understand the operational mechanism of the nervous system. Specific instances from the fields of molecular biology, immunology, physics and mathematics that can be used to overcome the current difficulties in understanding the nervous system are explained.


How to understand the operational mechanism of the nervous system functions?

What lessons can be used from other fields of research?

It is generally considered that it is very difficult to understand the operational mechanism of the nervous system. This is primarily because brain functions are being studied by a large number of levels and there are a large number of important findings at all those levels. Bringing all those findings together is very difficult in the current circumstances. But if we look positively, all those findings are providing us with constraints that can be used to derive the operational mechanism of the system. But a huge systematic approach is necessary towards this. Unlike the situation before the discovery of the structure of DNA where Watson and Crick were anxiously waiting for obtaining constraints from a different level, in neuroscience we are overwhelmed with too many constraints that can be used to solve the system. A list of constraints from findings from different levels is given in Table 2 on the front page of this website. The mechanism should be able to fulfill all those constraints. Only the correct solution can explain disparate constraints. A sample list includes the following - explain all the observed correlations between learning and LTP induction,  explain the learning-induced change that can remain for different duration for generating working, short and long-term memories and explain why the system needs sleep. In summary, we should be taking advantage of this situation.


Difficulty to undertake hypothesis building that can encompass the findings from different levels and at the same time conduct experiments to verify the predictions resulting from such work is conceivable. Previous hypotheses could not provide a solution with inter-connectable explanations for different features of the nervous system from different levels and make testable predictions. However, we have carried out a very large number of experiments at different levels and collected a huge amount of data. Even after these achievements we are confident that we do not understand the operational mechanism of the nervous system. Why is the situation not changing? How can we break this impasse? When we say that we do not understand the system, what it clearly says is that we do not understand its most important function of generation of first-person internal sensations within it. This is due to the fact that third-person experimenters do not have access to the first-person internal sensations generated within it. This provides us an implicit confidence about the difficulties in discovering its operational mechanism. Since we already know the problem, we must make it explicit and seek for a solution immediately. If we want to continue the conservative approach that we have been following in science, this is the next step that we must embrace. 

Many scientists do not even discuss about the first-person internal sensation due to the implicit difficulties in studying this property. This prevents further progress in solving the system. Since solution for the system is expected to provide nothing less than the gold standard test of its replication in engineered system, it is necessary to understand the operational mechanism that generates internal sensations. The mechanism for this function within this evolved system is expected to occur from a simple operation capable of evading our attention. Is there a way forward? Let us take the case of memory and examine the problem. Memories are virtual first-person inner sensations that can be viewed as hallucinations (a sensory experience of something in its absence) (Minsky (1980) Article). Therefore, we need to find out a learning-induced changes that can be used by one of the associatively learned stimuli to induce memories as hallucinations. Now we have a challenge. Hallucination is a first-person property towards which our (third-person) sensory systems do not have access. Where can we get help from? Are there any basic science field that has dealt with items towards which our sensory systems do not have access?

Using constraints from all the findings from different levels of the nervous system, a solution-point can be derived. If successful, this location is expected to have all the required features of the system that can generate first-person inner sensations as hallucinations.

When a method similar to solving a system of linear equations is applied in solving the nervous system, the following requirements have to be taken into account - a) incorporate all the variables, including those containing first-person internal sensations into the equations, and b) do not add any redundant equations. These indicate that we have to try to solve the system using only the non-redundant equations and should be seeking new experiments only if we cannot solve the system. In the latter instance, we may be able to predict the nature of the new equation/s (experiments and their results) to be selected towards reaching the solution.

Is there another feature available to identify this location of major function within a system?

In the nervous system, it is known that only a minute fraction of inputs to a cortical neuron with tens of thousands of inputs can fire that neuron. This extreme degeneracy of inputs that can fire a neuron (Vadakkan (2018) Article), indicates that the operational mechanism of the system is associated with these inputs. Since these inputs weaken as they propagate towards the neuronal cell body, the operational mechanism most likely be occurring at the origin of these inputs. 

Usually we find initial difficulties in identifying the location of redundancy/ combinatorial elements/ redundancy in a system as evident from the cases of codons and variable regions of immunoglobulins. In the case of the nervous system, we know where the redundancy is. We were not utilizing this finding since we could not derive a potential mechanism for generating internal sensations (hallucinations). Furthermore, we have been examining behavior in lieu of internal sensations.

Using different constraints from the findings of the nervous system, it was possible to derive a solution-point at the location of origin of inputs (dendritic spine head region). More specifically, an interaction between the abutted spines during learning called inter-postsynaptic functional LINK (IPL) was derived. The properties of the IPL junction allow reactivation of the IPL from a lateral direction by a cue stimulus to induce a hallucination that the inter-LINKed spine is receiving a sensory input from the environment (Vadakkan (2013) Article).

How do we know that the hypothesis is correct? What is the most important deciding factor that determines the derivation of a correct hypothesis? How does basic science view such properties like generation of internal sensations that third-person examiners cannot sense using their sensory systems?

The solution was examined for its ability to explain a very large number of findings and satisfy the constraints offered by them (Table 1 on the front page of this website). It was possible to provide explanations for all of them. For example, both associative learning and induction of first-person inner sensations take place only when the system operates at a narrow range of frequency of oscillating potentials (Vaz et al. (2019) Article). Induction of internal sensations is a function of frequency of oscillating potentials.

In the nervous system, triangulation can be applied as follows. It will become possible to explain memory, sleep, and long-term potentiation using the solution only if the solution is accurate. Otherwise, it will not become possible for us to provide an interconnected explanation. Similarly, the solution enables triangulating large number of findings from both normal and pathological conditions. Since the solution derived by using constraints from all the findings have reached at inter-postsynaptic LINKs (IPLs) between spines that belong to different neurons, we have to examine its presence within the system while conducting electron microscopic and other advanced microscopic studies. Otherwise, we will be wasting our precious time.

It is necessary to replicate the mechanism in an engineered system as a gold standard test.

The induction of first-person inner sensations as hallucinations within the nervous system started occurring by an accidental coincidence at some stage of evolution. It started providing a survival advantage to those nervous systems having this property. This evolved property is expected to occur through a simple mechanism that we are not yet familiar with. It should have a unique property capable of evading our attention easily. It should be present universally within the nervous systems of all the animals.

Since the solution derived by using constraints from all the findings have reached at inter-postsynaptic LINKs (IPLs) between spines that belong to different neurons, we have to examine its presence within the system while conducting electron microscopic and other advanced microscopic studies.
Knowledge of the IPLs also cautions us not to disrupt the IPLs while undertaking techniques for advanced imaging. For example, any artificial expansion of extracellular matrix space will break the IPLs. This will prevent viewing its presence. 

We should give priority to test the predictions made by any new hypothesis that can provide inter-connectable explanations for findings from different levels and that can make testable predictions. This is a cost-effective approach that can make rapid progress in the field. It should be possible to explain the mechanism of generation of first person inner sensations compatible with the explanations of the mechanisms of at least some of the physical properties. In this regard, generation of internal sensations occurring at physiological time-scales can be viewed as an engineering problem. Therefore, to fully explore this property involvement of physical and engineering sciences is inevitable. 


If we look at the discovery of DNA structure, we can see that there was a systematic approach by applying logic at each and every step along the way to explore the mechanism. A similar approach needs to be applied in the case of the nervous system. The composition of DNA was known by 1950. The question at that time was how the molecules are arranged within the DNA structure that enables all its properties. For examplethe long DNA molecule should get tightly packed within the chromosome (even though interphase chromosomes are not as tightly packed as in metaphase). At the same time, sufficient unwinding should take place at the exon regions for their translation to mRNA. It was also necessary to explain unwinding of DNA for its replication. It means, a structural framework (a model) was needed to explain all its functions. This is important since a correct structure is essential when experimental findings reach important crossroads for determining a path for future research work. Nearly a dozen hypotheses were put forward. What we have heard most is the one by Watson and Crick (authors clearly mentioned in their last paragraph that their work is a hypothesis) (Watson and Crick (1953) Article). The helical structure enables coiling and supercoiling of the DNA. One can imagine that it will take a huge amount of energy to unwind the supercoiled areas to expose the exons for translation. But this didn't dissuade Watson and Crick from putting forward their hypothesis, since they knew that the constraints provided by a large number of observations must be leading them to the correct solution. It is to be noted that the enzymes that are involved in both translation and replication were discovered later! DNA polymerase was discovered only in 1956 and RNA polymerase was discovered only in 1960. So the question is, "How can constraints provide this much confidence?" In fact, Watson and Crick used constraints from one additional level - X ray crystallographic picture of DNA, which is completely different from the level of biochemical findings. Including constraints from just one additional, but contrasting, level to derive the structure was so powerful that it led them to the correct model.

When a branch of science accumulates a large number of information, then it is necessary to have separate areas for both theory and experiments. This is because, the theorists often have to spend huge amount of time to synthesize a hypothesis for a basic governing mechanism/principle of the system using constraints from a large number of findings and verify their theoretical fitness. Physics is an example. When a system exhibits disparate features, it is necessary to derive a solution to make further progress. The only reason why we should consider a hypothesis is when the solution was developed by taking into account constraints from all the findings of the system. Building a hypothesis is a challenging task. A hypothesis is expected to make predictions, which is a primary requirement for any hypothesis. The idea here is that rejecting a hypothesis quickly is cost-effective than trying to prove it correct. Hypotheses that cannot explain all the properties of the system in an inter-connectable manner can be rejected prima facie. Those that do not provide predictions will remain untestable.  

The first-person internal sensation can be compared to different fundamental properties of physics that cannot be further split into its components. For example, electromagnetism (EM). EM field is generated when a current carrying conductor cuts a magnetic field. We accept it as a basic property, given the current state of our knowledge. We then try to derive an equation that can explain this property using all the variables that control this property. James Clerk Maxwell formulated an equation for the EM radiation.  

When deriving a new, unknown principle, in many instances physics uses the basic principle of solving a system of linear equations having a unique solution. This necessitates incorporating all the non-redundant equations within the system and then take into account all the variables to find the solution. The solution point binds all the equations within the system of linear equations. Before starting the procedure to solve the system, it is better to remove all the non-redundant equations. This is because non-redundant equations do not add any value in solving the system. 

In situations where limited resources have to be used for an infinite number of conditions, then nature often makes use of the power of combinatorial mechanisms by using unitary mechanisms. A typical example is the combinatorial mechanism for generating immunoglobulin gene variations for generating nearly 1011 (close to the number of humans that have ever lived!) different antibodies with specificities to respond to that many different antigens arriving from the envioronment (Tonegawa (1983) Article; (2001) Review).

At one stage of genetics research, one gene-one polynucleotide was found to be an ideal hypothesis. But it was necessary to explain how each amino acid within a polynucleotide is coded. Using different sets of experiments it was possible to identify that a triplet codon is enough to code for an amino acid. It was also found that for twenty amino acids and the stop codon (20+1), there were 43 (=64) theoretically possible codons, explaining degeneracy of the codons. The logical thinking that took place at that time can be found this article PubMed

From the above examples, one can make a reasonable conclusion that if degeneracy or a combinatorial mechanism is present at a lower level, then it is most likely that unitary elements for a candidate mechanism for the operation of that system is residing at this level. In other words, the elements of a system at the level that exhibits degeneracy are most likely associated with the generation of the unitary operational mechanism of that system. At this level, one can also expect to find features for integrating these operational units. 

To arrive at a correct hypothesis, the most crucial point is that it is necessary to take into account the constraints offered by all the findings of the system while formulating the hypothesis. It is for this reason that those who are engaged in theoretical work spend a lot of time understanding different fields within physics so that they will be able to arrive at the correct solution especially when trying to unify two major disparate findings within a system.

In addition, the method of triangulation can be applied to determine the accuracy of the solution. These provide proof for the mechanism.

Replication is the gold standard test for understanding any property of a given system. Keeping this as the final aim allows maintaining rigorous criteria during each and every stage of the derivation.

In principle, there should be no obstacle to duplicate the causal structure of the human brain (Watch how philosopher John Searle is stressing this between 36.40 to 38.40 min).

If a new mechanism can be derived that is in agreement with all the observations made at different levels (in an inter-connectable manner), then the newly derived mechanism should be correct. But verifiable predictions are a must for testing any new hypothesis. See how rigorous is the argument made by physicist Nima Arkani-Hamad (Watch from 43.29 to 47.00).

Any progress in neuroscience will benefit physics. This is because understanding the mechanisms of perception will influence how measurements are made in physics. Physicists are constantly watching how progress is being made in neuroscience. Here is a good read