31st Annual Computational Neuroscience Meeting: CNS*2022
Bibliographic record
Abstract
The space-time dynamics of interactions in neural systems are often described using terminology of information processing, or distributed computation, in particular with reference to information being stored, transferred and modified in these systems.In this talk, I will introduce an information-theoretic framework -information dynamics -that we use to model each of these operations on information within a complex system, and their dynamics in space and time.Not only does this framework quantitatively align with natural qualitative descriptions of neural information processing, it provides multiple complementary perspectives on how, where and why a system is exhibiting complexity.Specifically, I will describe tools we have produced to enable quantitative analysis of such information processing in brain dynamics, including both theoretical advances (such as how to measure information flows between spike trains) and software toolkits (including JIDT and IDTxl).I will then review what these tools enable us to reveal about dynamics in brains.This will include characterizing behavioral regimes and responses in terms of information processing; revealing the space-time dynamics of information processing during cognitive tasks; and how we can model effective network structure in terms of information flows.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.217 | 0.115 |
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".