Building an artificial neural network with neurons
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it