Simultaneous unsupervised and supervised learning of cognitive functions in biologically plausible spiking neural networks
Bibliographic record
Abstract
We present a novel learning rule for learning transformations of sophisticated neural representations in a biologically plausible manner.We show that the rule, which uses only information available locally to a synapse in a spiking network, can learn to transmit and bind semantic pointers.Semantic pointers have previously been used to build Spaun, which is currently the world's largest functional brain model (Eliasmith et al., 2012).Two operations commonly performed by Spaun are semantic pointer binding and transmission.It has not yet been shown how the binding and transmission operations can be learned.The learning rule combines a previously proposed supervised learning rule and a novel spiking form of the BCM unsupervised learning rule.We show that spiking BCM increases sparsity of connection weights at the cost of increased signal transmission error.We also demonstrate that the combined learning rule can learn transformations as well as the supervised rule and the offline optimization used previously.We also demonstrate that the combined learning rule is more robust to changes in parameters and leads to better outcomes in higher dimensional spaces, which is critical for explaining cognitive performance on diverse tasks.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".