Spiking Network Model Builder

Purpose

This skill encodes expert methodological knowledge for constructing biologically realistic spiking neural network simulations. A competent programmer without computational neuroscience training will get this wrong because:

• Neuron model choice determines what phenomena can emerge. A leaky integrate-and-fire (LIF) neuron cannot produce bursting, adaptation, or rebound spikes. If your phenomenon depends on these, you need an AdEx or Izhikevich model, not just a "more complex" model (Izhikevich, 2004).
• E/I balance is not optional. Cortical networks maintain a tight excitation/inhibition balance. Networks without proper E/I ratio produce either silence or epileptiform runaway activity, neither of which is biologically realistic (Brunel, 2000).
• Synaptic time constants encode biology. AMPA (fast, ~5 ms), NMDA (slow, ~100 ms), and GABA_A (~10 ms) receptors have fundamentally different dynamics. Using a single generic synapse model erases critical temporal structure (Dayan & Abbott, 2001).
• Time step selection affects correctness. Too-large integration steps cause LIF neurons to miss spikes and HH neurons to become numerically unstable. The correct step depends on the neuron model, not general ODE intuition (Rotter & Diesmann, 1999).
• Weight scaling must respect network size. Naive weight choices produce firing rates that change with network size. Balanced networks require 1/sqrt(N) scaling (Brunel, 2000).

When to Use This Skill

• Constructing a spiking neural network simulation for a research question
• Choosing a neuron model appropriate for the phenomenon of interest
• Setting biologically constrained connectivity parameters
• Implementing synaptic plasticity (STDP, homeostatic, etc.)
• Validating model outputs against known cortical statistics