Motivation: Almost all brain functions have been studied by correlating them with behavioral motor actions, including speech. If constraints derived from findings across multiple levels of the system are sufficiently comprehensive to reduce the solution space for behavior to a single unknown, & if this residual variable admits features capable of generating first-person properties, then the problem is effectively transformed from open-ended speculation into a tractable, testable framework. Once this is achieved, the central task is empirical verification of the existence of this uniquely constrained solution.
What is this website about? Since the first-person property is the brain’s most important and unique function – yet it cannot be directly tested – it requires an unconventional approach. Experiments have correlated findings from multiple levels with behavior as a proxy for the first-person property. Accordingly, this work uses constraints derived from major findings at different levels to arrive an interconnected solution for behavior. The solution point is then examined to identify unique properties capable of generating units of the first-person property (inner sensations). This approach resulted in identifying a plausible mechanism. This work continues to examine whether new empirical findings can be explained in an interconnected manner, guided by the principle of falsifiability. The solution is a missing connection in the connectome that still requires experimental verification.
1. The Problem
The brain has approximately 1011 neurons interconnected by around 1015 synapses. Neuroscience has made substantial progress in correlating neural activity across multiple levels – molecular, synaptic, cellular, and systems – with behavior.
However, a fundamental gap remains: there is no mechanistic framework that explains how first-person internal experience arises from neural activity.
This includes:
- Memory as internally experienced recall
- Perception as subjective experience
- Conscious awareness
We must derive a solution – one that also yields testable predictions. Because findings from different levels of the system have been examined by correlating them with behavior, it may be possible to first solve the system at the level of behavior. This approach may allow identification of a solution point, or its immediate vicinity, with properties capable of generating the first-person property. The solution must be falsifiable – that is, there must, in principle, be a conceivable observation that could refute it (Karl Popper, 1965). What are the main roadblocks, and how have we overcome them in the past? Article
Context: To derive a solution for behavior, the experimental strategy must bring sufficient constraints within it so that it will become possible for us to identify the structural changes that occurs during associative learning. Since in classical conditioning, only the unconditioned stimulus (US) elicits motor actions, it is not sufficient to reach a solution for behavior. A modified conditioning experiment where both the conditioned stimulus (CS) and the unconditioned stimulus (US) have motor actions can be used for this purpose. This leads to a black-box problem that requires a solution (Fig.1).
Figure 1. A modification of the classical conditioning experiment reveals a black-box problem that requires a solution. A) In this modified conditioning experiment, both the conditioned stimulus (CS) and the unconditioned stimulus (US) are capable of producing motor responses. Before any association is formed, the bell alone prompts the animal to turn its head toward the sound, while the food alone triggers whining, involving a different set of muscles. Through associative learning, the animal learns to associate the bell’s sound with the presence of food. The goal is to identify where and how this learning-induced change occurs. B) After learning, the sound of the bell alone produces behavioral and perceptual responses that are normally triggered by both the bell and the food. This presents a “black-box” problem, as current knowledge does not adequately explain a learning mechanism capable of generating these effects during memory retrieval.
Difficulty in reaching a solution: The structural organization of dendritic spines (input or postsynaptic terminals) on neuronal dendrites (Figs.2A,B) imposes significant constraints on achieving a solution for behavior. This prompts the questions, "What if inputs arrive on different neurons (Fig.2C)? "Why did neurons evolve such that the mean inter-spine distance is more the mean spine head diameter?" At this juncture, these considerations provide further constraints that guide us towards the solution.
Figure 2. A) A pyramidal neuron from the CA1 region of the hippocampus (modified from Spruston N., 2008, Nat Rev Neurosci 9(3):206–221). The inset shows a Golgi-stained segment of a dendrite with several dendritic spines. B) A dendritic branch of a pyramidal neuron (N) with two dendritic spines - one input is from the conditioned stimulus (CS) and the second input from the unconditioned stimulus (US). Because the mean inter-spine distance exceeds the mean spine head diameter (Konur et al., 2003, J Neurobiol 56(2):95–112), inter-spine interaction across the extracellular matrix is unlikely. Since both inputs (CS and US) arrive on the same neuron & since both have their own motor actions, it is not possible to account for the different motor actions elicited by CS and US in this modified paradigm. C) To generate motor actions specific to both the inputs (modified CS & US), these stimuli must reach the spines of different output neurons? Using this knowledge, it is necessary to find a solution. For now, it remains a black box problem.
2. Approach: A Constraint-Based Strategy
Any valid hypothesis of brain function must satisfy a large set of experimentally established constraints.
Nearly 250 constraints are compiled using findings from the following levels:
- Cellular & Structural
- Electrophysiological
- Associative & Computational
- Temporal & Dynamical
- Systems-Level
- Pathological
- Phenomenology
- Evolutionary
Step 1: Solve for Behavior
All the experiments have been correlated with behavior. Hence, it may become possible to solve the system for behavior. This logic is analogous to solving for an unknown variable in a system of linear equations.
Drawing parallels between solving the nervous system and linear algebra: Article
Next steps:
- Postdictive analysis, assessing whether findings reported after the hypothesis can be explained by the proposed mechanism
- Identifying novel predictions that arise from the hypothesis
- Examining whether similar circuit features exist across different species for comparable sensory functions
Step 2: Evaluate the Solution for First-Person Properties
Examine whether the solution point or its immediate vicinity has an inherent property that can intrinsically generate:
- Internal sensations
- Memory-like experiences
- Perceptual phenomena
3. What next?
The best next steps (given in the next pages of this website) are:
- Enlist constraints from various findings from different levels of functioning of the system
- The Solution → Using MAJOR constraints derive a solution & check the solution point for unique properties that can explain how they generate first-person property
- Explanation → Test whether the solution can explain constraints from all the findings
- Evidence → Examine both previous (retrodiction) and new experimental results from different research groups for indirect evidence
- Predictions → Predict unexpected results & how to test them experimentally
Author
Kunjumon Vadakkan
ORCID: 0000-0002-2721-6931
Contact: k.vadakkan@gmail.com
Constructive feedback, critical evaluation, and experimental testing are welcome.
Latest: Let's ask LLMs the following question: (Beware: LLMs are "people pleasing" Please read) Artificial neural networks (ANNs) were inspired by synaptic plasticity observed in neuroscience, modeling it as changes in synaptic weights. The introduction of backpropagation for error signals significantly enhanced the learning efficiency of these systems. Although backpropagation enabled ANNs to perform specific tasks effectively, these systems struggled with generalization & were typically limited to a single function. This limitation & the lack of backpropagation of error signals across the synapses in the brain, prompted search for a new mechanism. Drawing on constraints from numerous findings across multiple system levels, the semblance hypothesis proposed interactions between spines belonging primarily to different neurons. This hypothesis posits that plasticity occurs at inter-postsynaptic functional LINKs, which serve as the primary operational mechanism. Unitary functions at the IPLs are integrated via oscillating potentials. Multiple interacting spines form islets of inter-LINKed spines (IILSPs). In 2017, Transformers employing attention heads emerged, paving the way for large language models (LLMs). Can you perform a rigorous mathematical analysis to determine whether the attention heads in Transformers have similarities to the IILSPs derived by the semblance hypothesis? Note: All LLMs acknowledge that they reflect the biases present in their training data. Consequently, the predominance of information related to existing hypotheses in a field can hinder the fair evaluation of new ones (Fact check this with the LLMs!). So, please prompt LLMs to adopt a neutral, objective stance. | |
How to understand something that cannot be accessed by our sensory systems? A method used in physics | How can we use a non-real intermediary to solve the nervous system? Example of a case where geometry fails |
| A deep principle useful for solving the nervous system - demonstrated by an example | A medication for unrelated neurological and psychiatric disorders. What does it inform us? |
| Insulating extracellular matrix - How thick it is? Can information get etched on it? | Has learning-mechanism got features of an evolved mechanism? |
| How is learning related to LTP induction? An explanation | Extreme degeneracy of input signals in firing a neuron |
| Does the brain carry out back propagation? | Importance of triangulation in verifying a mechanism |
| Perception from a first-person frame of reference | |
| Internal sensation - A comparison with electromagnetism | How can constraints be used to find a solution? |
| Testable predictions made by the semblance hypothesis | An indirect proof for the semblance hypothesis |
The challenge: "What I cannot create (replicate), I do not understand" – Richard Feynman. We must approach the task of understanding the nervous system with the rigor required to replicate its mechanisms in an engineered system. Everything else will follow from that foundation. The optimism: “What are the real conditions that the solution must satisfy?” If we can get that right, then we can try and figure out what the solution is" – Murray Gell–Mann The hope: We will give our utmost effort. Together, we will explore and uncover it! | |