Optimizing Neuromorphic Spike Encoding of Dynamic Stimulus Signals Using Information Theory
Bibliographic record
Abstract
Neuromorphic systems use spike representations of stimuli as inputs. These systems should ensure that the spikes carry a maximum amount of information on the signals that they encode. There is a pressing need to better understand how to maximize the information encoded into spikes, as it can have important implications for the outcome of the applications in which the spike representations are used. This work proposes the use of information theory, specifically the information rate, to maximize the information that a spike train carries on the signal that is encoded. The method consists of varying the encoding parameters to produce spike trains of different densities, and then estimating the information rate between the signal and the spike train over the entire range of spike densities. This allows to find an estimate of the spike density that maximizes the information rate, and therefore the optimal encoding parameters. The method is applied to the encoding of two stimuli (Brownian motion and speech) with a Leaky Integrate-and-Fire neuron. The proposed approach is fast and general, as it can be used with any dynamic stimulus input and any spike encoding technique. It offers a rigorous solution to the problem of spike encoding optimization and allows the separation of the encoding stage from task-specific applications that use spikes as inputs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".