Neural network models of the tactile system develop first-order units with spatially complex receptive fields
Bibliographic record
Abstract
First-order tactile neurons have spatially complex receptive fields.Here we use machinelearning tools to show that such complexity arises for a wide range of training sets and network architectures.Moreover, we demonstrate that this complexity benefits network performance, especially on more difficult tasks and in the presence of noise.Our work suggests that spatially complex receptive fields are normatively good given the biological constraints of the tactile periphery. ResultsFirst-order tactile neurons in the hairless skin of the human hand have distal axons that branch in the skin and form many transduction sites [1-3], yielding spatially complex receptive fields with many highly sensitive zones [4,5] (Fig 1A).We have recently shown that this arrangement permits first-order tactile neurons to signal high-level features of touched objects such as the orientation of a touched edge [4,6,7], a capacity previously considered a hallmark of processing in the somatosensory cortex [8-10].Here we leverage machine learning tools to investigate why complex receptive fields arise and what computational benefits they yield.We show that complex receptive fields arise under a wide range of training sets and biologically realistic network constraints.We also show that complex receptive fields benefit network performance, especially on more complex discrimination tasks and in the presence of noise.We abstracted the tactile processing pathway with a four-layer feedforward neural network (Fig 1B and 1C).The input layer of our network consisted of 784 units, representing mechanoreceptors distributed over a small patch of skin.In this arrangement, the weight matrix between the input and first hidden layer-which we call W (1) -represents the receptive fields of first-order tactile neurons.Our network was trained on a range of stimuli including single
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".