Influence of STDP rule choice and network connectivity on polychronous groups and cell ensembles in spiking neural networks
Bibliographic record
Abstract
Abstract Spike-timing dependent plasticity plays an important role in how biological neural networks modify themselves with experience. However, the relationship between STDP and memory is not fully understood. Previously, an important advancement in understanding the relationship between spike-timing dependent plasticity (STDP) and memory was made through cortical simulations. The proposed memory items, polychronous groups (PGs), combined the network anatomy with precise spike-timing relationships between the connected cells of the network. However, there are some challenges with this previous work. It is unclear how different STDP rules would impact the PG results and if the PG results are complementary with purely spike-pattern defined memory items called cell ensembles (CEs). Lastly, it is unclear how these results are affected by changes in network connectivity. We address these challenges by comparing the PGs and CEs detected in spiking neural network simulations of cortical and hippocampal CA1 networks with two different STDP rule implementations. We show that the PG and CE results differ greatly for the two different STDP rules and for the cortical and CA1 networks. Our results show an important disconnect between anatomically defined and spike-pattern defined memories in spiking neural network simulations illustrating that care must be taken when drawing conclusions on the relationship between STDP and memory in simulation studies.
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) | 0.000 | 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 | 0.000 | 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".