Signal Processing and Computational Neuro-
Bibliographic record
Abstract
• Explore the brain code: synchronization and sequence of spikes for signal processing & recognition; • Integrate speech processing with auditory perception: – The auditory features are multiple, simultaneous, and time structured; – There is no disjunction between analysis and recognition; – The auditory objects have a structure. • Develop new signal processing and pattern recognition technics: – Polysensoriality and sensory substitution: visual and auditory interac-tions; – Source separation and cocktail party processing. J. ROUAT, 30 April 09, McGill •First •Prev •Next •Last •Go Back •Full Screen •Close •Quit Rate and synchronization coding in the brain Rate coding Many neurons should respond to conjunctions of properties (orientation, motion and color in vision) (tonotopic frequency, amplitude modulation, transient in audition). With a rate code the number of neurons should be quite large to encode all targets potentially shown to the sensory systems. Their is an explosion of the feature combinations and the spatial organization of the characteristics are lost. Synchronization Synchronization by coincidence Synchronization of pulses without oscilla-tory behavior: coincidence detector in the auditory system for fast computation. Synchronization with oscillatory neuronal assemblies Oscillatory rhythms for memory, perception, etc. One hypothesis: A non stimulated brain (brain at rest) exhibits oscillations in large networks of oscillatory neurons. A stimulation is then a perturbation of this oscillatory mode [1] a. aBuzsáki, 2006
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".