139,000 neurons on a laptop, from first principles. The simulation engine assembles anatomy (parcellation), connectivity (edge lists), and physiology (cell and synapse models) into a running whole-brain LIF simulation — no Brian2, no NEST, just pure NumPy.

Lesson 11 — Whole-Brain LIF Simulation

The simulation uses the Shiu et al. formulation: current-based LIF with exponential synaptic conductances, sparse connectivity matrix, and vectorised Euler integration. Key design choices:

  • Pure NumPy — no external simulator dependency; every equation is visible and modifiable
  • Sparse matrices — scipy.sparse CSR format for the 50M-synapse connectivity
  • Stimulus protocols — current injection, optogenetic activation, sensory input patterns
  • Analysis tools — raster plots, firing rate histograms, population synchrony measures

Lesson 15 — Brunel Phase Diagram

Where does the mushroom body sit in dynamical regime space? The Brunel (2000) framework classifies networks by two axes: synchrony (synchronous vs. asynchronous) and balance (excitation-dominated vs. inhibition-stabilised). By varying external drive and inhibitory gain, we map the MB’s operating point.

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