Error-Driven Plasticity

This independent research repository contains a small, deterministic synthetic model for exploring how an error-like variable and a response-like variable can be represented in software.

Research status

Completed work	Outcome
Seed-controlled synthetic observation generator	Implemented; equal seeds generate repeatable in-memory sequences.
Synthetic association summary	Implemented for generated values and guarded against insufficient observations or zero variation.
Boundary and unit tests	Passed for the defined synthetic behaviors and prohibited-path checks.
Empirical plasticity analysis	Not implemented; the repository contains no observed data, benchmark, figure, or empirical outcome.

Current conclusion: the repository provides a working synthetic conceptual model, not evidence that error-driven plasticity occurs in a biological system. It has no empirical success or failure result to report.

Contents

Path	Purpose
src/synthetic_model.py	Standard-library synthetic model utilities.
tests/test_public_boundary.py	Tests for the synthetic summary and boundary rules.
tools/check_public_boundary.py	Checks tracked paths and text for public-boundary violations.
docs/	Research status, methods scope, deferred directions, and contribution guidance.

Getting started

The retained model uses only the Python standard library.

from src import SyntheticPlasticityModel

model = SyntheticPlasticityModel(seed=7)
observations = model.generate_observations(count=12)
summary = model.summarize_association(observations)

The generated values are illustrative synthetic values, not measurements.

Validation

python tools/check_public_boundary.py
python -m unittest discover -s tests -v

Keywords

Synthetic modeling, error-driven learning, plasticity, independent research, data-free software, repository boundaries.

Contributing

Contributions are welcome. Useful work includes transparent synthetic-model design, deterministic tests, documentation, numerical validation, and carefully scoped empirical-evaluation proposals. See Contributing and the research status.

Documentation

• Introduction for new readers
• Extended introduction for readers with no neuroscience background
• Methods for new contributors
• Current results and discussion
• Research status and plan
• Methods scope
• Deferred and dropped directions
• Release boundary