Semblance Hypothesis
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 each elicits distinct motor outputs, 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 may need to reach the spines of different output neurons (e. g for turning the neck towards sound & whining). 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:
Step 1: Solve for Behavior
All the experiments have been correlated with behavior. Hence, it may become possible to solve the system for behavior. Since neural systems are nonlinear, stochastic, and dynamic, constraint satisfaction ideally occurs in a high-dimensional, nonlinear system. For taking a simplified approach, the expectation for solving the system for behavior can be heuristically viewed as 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:
Step 2: Evaluate the Solution for First-Person Properties
Here, first-person properties refer to internally generated, stimulus-specific experiential states that can guide behavior in the absence of the original stimulus. Examine whether the solution point or its immediate vicinity has an inherent property that can intrinsically generate:
3. What next?
The best next steps (given in the next pages of this website) are:
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). Is there a rigorous mathematical sense in which attention heads and IILSPs instantiate the same type of computation at an abstract level?
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.
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!
Site started 10/15/2008; Updated 05/04/2026