Impact of Synaptic Strength on Propagation of Asynchronous Spikes in Biologically Realistic Feed-Forward Neural Network
Bibliographic record
Abstract
We consider the problem of reliable information propagation in the brain using biologically realistic models of spiking neurons. Biological neurons use action potentials, or spikes, to encode information. Information can be encoded by the rate of asynchronous spikes or by the (precise) timing of synchronous spikes. Reliable propagation of synchronous spikes is well understood in neuroscience and is relatively easy to implement by biologically-realistic models of neurons. However, reliable propagation of rate-modulated asynchronous spikes is poorly understood and remains difficult to implement by those models. In this paper, we formulate how a multi-layered feedforward neural network (mlFNN) comprising biologically-plausible model neurons enables propagation of time-varying asynchronous spikes. Gradient descent algorithm is developed to estimate the connectivity between neurons (i.e., synaptic weights) in mlFNN. Furthermore, we propose an abstract network model to replicate information propagation in mlFNN with substantially less complexity in estimating synaptic weights. The abstract model has a great implication for neuromorphic computing, as it can be implemented in neuromorphic circuits with less complexity, less energy, and more speed. Simulation results demonstrate that (i) the mlFNN with optimal synapses transmits asynchronous spikes reliably, and (ii) the abstract network model reproduces information propagation performed by mlFNN with high accuracy (coding fraction = 0.97 ± 0.02). We anticipate that this study will facilitate the design and implementation of biologically realistic mlFNN in neuromorphic circuits as well as cross-fertilizations between the fields of neuromorphic engineering, computational neuroscience and artificial intelligence.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".