Rewiring Neuronal Circuits: A New Method for Fast Neurite Extension and Functional Neuronal Connection
Bibliographic record
Abstract
Brain and spinal cord injury may lead to permanent disability and death because it is still not possible to regenerate neurons over long distances and accurately reconnect them with an appropriate target. Here a procedure is described to rapidly initiate, elongate, and precisely connect new functional neuronal circuits over long distances. The extension rates achieved reach over 1.2 mm/h, 30-60 times faster than the in vivo rates of the fastest growing axons from the peripheral nervous system (0.02 to 0.04 mm/h)28 and 10 times faster than previously reported for the same neuronal type at an earlier stage of development4. First, isolated populations of rat hippocampal neurons are grown for 2-3 weeks in microfluidic devices to precisely position the cells, enabling easy micromanipulation and experimental reproducibility. Next, beads coated with poly-D-lysine (PDL) are placed on neurites to form adhesive contacts and pipette micromanipulation is used to move the resulting bead-neurite complex. As the bead is moved, it pulls out a new neurite that can be extended over hundreds of micrometers and functionally connected to a target cell in less than 1 h. This process enables experimental reproducibility and ease of manipulation while bypassing slower chemical strategies to induce neurite growth. Preliminary measurements presented here demonstrate a neuronal growth rate far exceeding physiological ones. Combining these innovations allows for the precise establishment of neuronal networks in culture with an unprecedented degree of control. It is a novel method that opens the door to a plethora of information and insights into signal transmission and communication within the neuronal network as well as being a playground in which to explore the limits of neuronal growth. The potential applications and experiments are widespread with direct implications for therapies that aim to reconnect neuronal circuits after trauma or in neurodegenerative diseases.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".