Parsing Sequentially Presented Commands in a Large-Scale Biologically Realistic Brain Model
Bibliographic record
Abstract
We present a neural mechanism for interpreting and executing visually presented commands.These are simple verb-noun commands (such as WRITE THREE) and can also include conditionals ([if] SEE SEVEN, [then] WRITE THREE).We apply this to a simplified version of our large-scale functional brain model "Spaun", where input is a 28x28 pixel visual stimulus, with a different pattern for each word.Output controls a simulated arm, giving hand-written answers.Cortical areas for categorizing, storing, and interpreting information are controlled by the basal ganglia (action selection) and thalamus (routing).The final model has ~100,000 LIF spiking neurons.We show that the model is extremely robust to neural damage (40% of neurons can be destroyed before performance drops significantly).Performance also drops for visual display times less than 250ms.Importantly, the system also scales to large vocabularies (~100,000 nouns and verbs) without requiring an exponentially large number of neurons.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".