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phase-brain-optimize

The brain-optimize phase covers parameter exploration and model fitting — sweeping a parameter space to map model behaviour, or optimising parameters to match experimental data.

Approach

  • Identify the goal early: exploratory sweep vs targeted fitting to experimental data
  • Write optimize-plan.md before any code — parameters, bounds, cost function, algorithm, convergence criterion
  • Always run a minimal smoke test (2–3 evaluations) before launching a full search
  • Prefer algorithms suited to the problem: grid search for low-dimensional spaces; differential evolution or Bayesian optimisation for high-dimensional or expensive simulations

Relevant skills

  • neuroflow:neuroflow-core — read first; defines the command lifecycle and .neuroflow/ write rules

Workflow hints

  • All optimisation scripts and raw results go to output_path (models/optimize/), not inside .neuroflow/
  • Save optimize-plan.md and post-run summaries to .neuroflow/brain-optimize/
  • Log algorithm choice and cost function rationale in .neuroflow/reasoning/brain-optimize.json
  • Common libraries: DEAP, Optuna, scipy.optimize, BluePyOpt, scikit-optimize

Slash command

/neuroflow:brain-optimize — runs this workflow as a slash command.