P-CRITICAL: a reservoir autoregulation plasticity rule for neuromorphic hardware
Bibliographic record
Abstract
Abstract Backpropagation algorithms on recurrent artificial neural networks require an unfolding of accumulated states over time. These states must be kept in memory for an undefined period of time which is task-dependent and costly for edge devices. This paper uses the reservoir computing paradigm where an untrained recurrent pool of neurons is used as a preprocessor for temporally structured inputs and with a limited number of training data samples. These so-called reservoirs usually require either extensive fine-tuning or neuroplasticity. We propose a new local and unsupervised plasticity rule named P-CRITICAL designed for automatic reservoir tuning that translates well to physical and digital neuromorphic processors. The spiking neuronal architecture implementation is simulated on the Loihi research chip from Intel and on a conventional CPU. Comparisons on state-of-the-art machine learning datasets are given. Improved performance on visual and auditory tasks are observed. There is no need to a priori tune the reservoir when switching between tasks, making this approach suitable for physical implementations. Furthermore, such plastic behaviour of the reservoir is a key to end-to-end energy-efficient neuromorphic-based machine learning on edge devices.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".