Building an artificial neural network with neurons
Bibliographic record
Abstract
Artificial neural networks are based on mathematical models of biological networks, but it is not clear how similar these two networks are. We have recently demonstrated that we can mechanically manipulate single neurons and create functioning synapses. Here, we build on this discovery and investigate the feasibility and time scales to build an artificial neural network with biological neurons. To achieve this, we characterized the dynamics and forces when pulling functional axonal neurites using a micromanipulation technique with maximum speeds about 300 times faster than the average natural growth rate of 0.0017μm/s. We find that the maximum force required to initiate and extend the neurites is about 1nN. The dynamics of the mechanical extension of the neurite is well described by many elastic springs and viscous dashpots in series. Interestingly, we find that the transport networks, specifically the actin network, lags behind the mechanically pulled structure. These insights could potentially open a new avenue to facilitate and encourage neuronal regrowth not relying on chemical queues. The extracted mechanical parameters and timescales characterize the neurite growth. We predict that it should be possible to use a magnetic trap to wire an artificial network such as a multi-layer perceptron in 17 hours. Once wired, we believe the biological neural network could be trained to process a hand-written digit using artificial neural network concepts applied to biological systems. We show how one could test the stability and robustness of this network by axotomizing (i.e. cutting) specific axons and reconnecting them using mechanical manipulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".