brian2-simulation

name	brian2-simulation
description	Use this Skill to simulate spiking neural networks with Brian2: LIF/AdEx neurons, STDP, recurrent networks, raster plots, and mean-field analysis.
tags	["neuroscience","computational-neuroscience","brian2","spiking-networks","neural-simulation"]
version	1.0.0
authors	[{"name":"Rosetta Skills Contributors","github":"@xjtulyc"}]
license	MIT
platforms	["claude-code","codex","gemini-cli","cursor"]
dependencies	{"python":["brian2>=2.5","numpy>=1.24","matplotlib>=3.7","scipy>=1.11","pandas>=2.0"]}
last_updated	2026-03-17
status	stable

Brian2 Spiking Neural Network Simulation

One-line summary: Simulate biologically plausible spiking neural networks with Brian2: LIF, AdEx, Hodgkin-Huxley neurons, STDP plasticity, and recurrent network dynamics.

---

When to Use This Skill

• When simulating integrate-and-fire or conductance-based neuron models
• When studying spike-timing-dependent plasticity (STDP)
• When implementing recurrent excitatory-inhibitory networks
• When generating raster plots and firing rate analyses
• When fitting mean-field equations to network simulations
• When comparing neural coding hypotheses (rate vs. temporal)

Trigger keywords: Brian2, spiking neural network, integrate-and-fire, LIF, AdEx, Hodgkin-Huxley, STDP, synaptic plasticity, raster plot, neural simulation

---

Background & Key Concepts

Leaky Integrate-and-Fire (LIF) Model

$$ \tau_m \frac{dV}{dt} = -(V - V_\text{rest}) + R \cdot I(t) $$

When $V \geq V_\text{thresh}$: fire a spike, reset $V = V_\text{reset}$.

Parameters: $\tau_m$ = membrane time constant, $R$ = membrane resistance, $V_\text{rest}$ = resting potential.

Adaptive Exponential (AdEx) Model

Adds spike-triggered adaptation:

$$ C \frac{dV}{dt} = -g_L(V - E_L) + g_L \Delta_T e^{(V-V_T)/\Delta_T} - w + I $$ $$ \tau_w \frac{dw}{dt} = a(V - E_L) - w $$

Spike-Timing-Dependent Plasticity (STDP)

Synaptic weight changes based on spike timing:

$$ \Delta w = \begin{cases} A_+ e^{-|\Delta t|/\tau_+} & \text{if } t_\text{post} > t_\text{pre} \ -A_- e^{-|\Delta t|/\tau_-} & \text{if } t_\text{post} < t_\text{pre} \end{cases} $$

---

Environment Setup

Install Dependencies

pip install brian2>=2.5 numpy>=1.24 matplotlib>=3.7 scipy>=1.11 pandas>=2.0

Verify Installation

import brian2 as b2
print(f"Brian2 version: {b2.__version__}")
b2.start_scope()
G = b2.NeuronGroup(10, "dv/dt = -v/(10*ms) : 1", threshold="v>0.9", reset="v=0")
print("Brian2 functional")

---

Core Workflow