Maximizing information in neuron populations for neuromorphic spike encoding
Bibliographic record
Abstract
Abstract One of the ways neuromorphic applications emulate the processing performed by the brain is by using spikes as inputs instead of time-varying analog stimuli. Therefore, these time-varying stimuli have to be encoded into spikes, which can induce important information loss. To alleviate this loss, some studies use population coding strategies to encode more information using a population of neurons rather than just one neuron. However, configuring the encoding parameters of such a population is an open research question. This work proposes an approach based on maximizing the mutual information between the signal and the spikes in the population of neurons. The proposed algorithm is inspired by the information-theoretic framework of Partial Information Decomposition. Two applications are presented: blood pressure pulse wave classification, and neural action potential waveform classification. In both tasks, the data is encoded into spikes and the encoding parameters of the neuron populations are tuned to maximize the encoded information using the proposed algorithm. The spikes are then classified and the performance is measured using classification accuracy as a metric. Two key results are reported. First, adding neurons to the population leads to an increase in both mutual information and classification accuracy beyond what could be accounted for by each neuron separately, showing the usefulness of population coding strategies. Second, the classification accuracy obtained with the tuned parameters is near-optimal and it closely follows the mutual information as more neurons are added to the population. Furthermore, the proposed approach significantly outperforms random parameter selection, showing the usefulness of the proposed approach. These results are reproduced in both applications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".