Neural Coding via Transmission Delay Coincidence Detectors: An Embodied Approach
Bibliographic record
Abstract
This thesis aims to contribute to the field of cognition via a careful investigation at the mesoscopic level of neural organization and activity via spatiotemporal dynamics to subserve reactive and adaptive behaviour.In particular, coding via coincidence detection with prorogation delays is adopted as the primary mechanism to investigate complex neural dynamics.Initially hypothesized by Moshe Abeles in the early 1980's, coincidence detection enables neurones to respond by emitting an action potential (a spike) only when other input neurones spike in a precise order.In contrast to the traditional interpretation of neural behaviour whereby cells integrate inputs over long periods of time, coincidence detectors are sensitive to changes at millisecond or sub-millisecond time scales.However, networks of coincidence detectors that incorporate propagation delays remain poorly understood with respect to dynamics and functional application for cognitive tasks.After introducing the functional and biological evidence that motived this research, we explore existing work related to spatiotemporal coding and the neurone and network parameters that influence their behaviour.We then present a discrete neural network model that is used to expose the relationship between structural parameters and network dynamics.After applying these to a reactive light-seeking robot task, we find network parameters that enable dynamic memory storage of input spike patterns.Making use of these dynamics, we then introduce a method for decoding these memories in a modular network architecture and test this approach for robot control in memory maze tasks.Furthermore, the limits of the potentially high memory capacity of these networks is then tested by empirically evaluating both the noise tolerance and the memory capacity of these networks.To complement this empirical work, we develop a set of formal expressions which attempt to approximate analytically the amount of activity of these networks and the probability for any given spike pattern to be expressed by them.tireless thesis advisor Dr. Anthony White.Without his curiosity, interest, expertise, and willingness to review countless documents this thesis would not be before you today.Tony has provided significant practical, insightful, and scientific contributions to this work for which I will always be grateful.Tony has also donned the role of mentor and instilled energy into my work when I needed it the most.The itinerary of this doctoral degree has been a unique and immensely rewarding experience
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 | 0.002 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".