CNS: a GPU-based framework for simulating cortically-organized networks
Bibliographic record
Abstract
Computational models whose organization is inspired by the cortex are increasing in both number and popularity. Current instances of such models include convolutional networks, HMAX, Hierarchical Temporal Memory, and deep belief networks. These models present two practical challenges. First, they are computationally intensive. Second, while the operations performed by individual cells, or units, are typically simple, the code needed to keep track of network connectivity can quickly become complicated, leading to programs that are difficult to write and to modify. Massively parallel commodity computing hardware has recently become available in the form of general-purpose GPUs. This helps address the first problem but exacerbates the second. GPU programming adds an extra layer of difficulty, further discouraging exploration. To address these concerns, we have created a programming framework called CNS ('Cortical Network Simulator'). CNS models are automatically compiled and run on a GPU, typically 80-100x faster than on a single CPU, without the user having to learn any GPU programming. A novel scheme for the parametric specification of network connectivity allows the user to focus on writing just the code executed by a single cell. We hope that the ability to rapidly define and run cortically-inspired models will facilitate research in the cortical modeling community. CNS is available under the GNU General Public License.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".