Spiking Network Model Builder

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
• Selecting simulation software and numerical parameters

Do NOT use this skill for:

• Rate-based neural network models (use standard ML/deep learning frameworks)
• Detailed compartmental modeling of single neurons (use NEURON with morphological data)
• Analyzing experimental neural data (see neural-population-analysis-guide)

Research Planning Protocol

Before executing the domain-specific steps below, you MUST:

1. State the research question -- What specific question is this analysis/paradigm addressing?
2. Justify the method choice -- Why is this approach appropriate? What alternatives were considered?
3. Declare expected outcomes -- What results would support vs. refute the hypothesis?
4. Note assumptions and limitations -- What does this method assume? Where could it mislead?
5. Present the plan to the user and WAIT for confirmation before proceeding.

For detailed methodology guidance, see the research-literacy skill.

⚠️ Verification Notice

This skill was generated by AI from academic literature. All parameters, thresholds, and citations require independent verification before use in research. If you find errors, please open an issue.

Step 1: Select a Neuron Model

Neuron Model Decision Tree

What firing properties does your model need?
|
+-- "Just spikes, basic rate coding, large networks"
| --> Leaky Integrate-and-Fire (LIF)
| Simplest; fastest simulation; no adaptation or bursting
|
+-- "Spike initiation sharpness matters"
| --> Exponential IF (EIF)
| Adds realistic spike onset; still single-variable
|
+-- "Spike-frequency adaptation or bursting"
| --> Adaptive Exponential IF (AdEx)
| Two variables; can produce regular spiking, bursting,
| intrinsic oscillations, adaptation
|
+-- "Diverse firing patterns with minimal complexity"
| --> Izhikevich model
| Four parameters; 20+ firing patterns; fast to simulate
|
+-- "Biophysically detailed ion channel dynamics"
 --> Hodgkin-Huxley (HH)
 Four variables; channel-level accuracy; slow to simulate
 Use only when ion channel pharmacology is relevant

Neuron Model Parameters

Leaky Integrate-and-Fire (LIF)

Exponential Integrate-and-Fire (EIF)

Adaptive Exponential IF (AdEx)

AdEx firing patterns by parameter regime (Brette & Gerstner, 2005; Naud et al., 2008):

Izhikevich Model

The model uses two variables (v, u) with four parameters (a, b, c, d) (Izhikevich, 2003):

Hodgkin-Huxley (HH)

Use only when biophysical detail is required. See references/hh-parameters.md for the full parameter set. Key values (Hodgkin & Huxley, 1952):

• g_Na = 120 mS/cm^2, E_Na = 50 mV
• g_K = 36 mS/cm^2, E_K = -77 mV
• g_L = 0.3 mS/cm^2, E_L = -54.4 mV
• C_m = 1 uF/cm^2

Step 2: Configure Synapses

Synaptic Time Constants

Conductance-Based vs. Current-Based Synapses

Domain judgment: Current-based synapses are appropriate for most network-level studies. Switch to conductance-based when the research question involves voltage-dependent effects (NMDA Mg2+ block, shunting inhibition) or when accurate I-V relationships matter (Brunel, 2000; Dayan & Abbott, 2001).

Short-Term Plasticity: Tsodyks-Markram Model

The Tsodyks-Markram (TM) model captures short-term facilitation and depression (Tsodyks & Markram, 1997):

Step 3: Configure Network Connectivity

Excitatory/Inhibitory Balance

Weight Scaling for Balanced Networks

For a balanced network to produce biologically realistic asynchronous irregular (AI) firing (Brunel, 2000):

• Excitatory weight: J_E = J_0 / sqrt(N_E * p), where N_E is excitatory population size and p is connection probability
• Inhibitory weight: J_I = -g * J_E, where g > 1 (typically g = 4--8 for the AI regime)
• External drive: Poisson input to maintain target firing rates

Domain judgment: The ratio g = J_I/J_E (relative inhibitory strength) determines the network regime. g < 4 produces synchronous regular firing; g = 4--8 produces the biologically realistic asynchronous irregular (AI) state; g >> 8 produces very low firing rates or silence (Brunel, 2000).

Network Size

Step 4: Implement Plasticity Rules

Spike-Timing-Dependent Plasticity (STDP)

Standard pair-based STDP parameters (Bi & Poo, 1998; Song et al., 2000):

Domain judgment: The asymmetry |A_-| > A_+ is critical. Without it, STDP drives all weights to their maximum value (runaway potentiation). The slight depression bias ensures stable weight distributions (Song et al., 2000). Additional stabilization mechanisms (weight dependence, homeostatic plasticity) are often needed in practice.

Rate-Based Plasticity: BCM Rule

The Bienenstock-Cooper-Munro (BCM) rule provides a stable, rate-based plasticity rule (Bienenstock et al., 1982):

• Learning rule: dw/dt = eta * y * (y - theta_m) * x
• Sliding threshold: theta_m = E[y^2], ensuring stability
• Where y = postsynaptic rate, x = presynaptic rate, eta = learning rate

Homeostatic Plasticity

For long simulations with STDP, add homeostatic mechanisms to prevent runaway dynamics:

• Synaptic scaling: Multiplicatively scale all incoming weights to maintain target firing rate (Turrigiano et al., 1998)
• Intrinsic plasticity: Adjust neuronal excitability (threshold or adaptation) to maintain target rate (Desai et al., 1999)
• Target firing rate: 1--5 Hz for excitatory cortical neurons (expert consensus based on in vivo recordings)

Step 5: Set Simulation Parameters

Integration Time Step