Tutorial 22: Choosing the Right Neuron Model

August 25, 2026 · View on GitHub

SC-NeuroCore currently exposes 159 lazy-loaded Python model classes across 154 Python model source modules, with 187 Rust PyO3 model wrappers in the optional engine. This guide helps you pick the right model for your application.

Decision Tree

What is your goal?
│
├─ FPGA deployment → FixedPointLIFNeuron (bit-true Q8.8 RTL match)
│
├─ GPU training (PyTorch) → LIFCell, AdExCell, or ConvSpikingNet
│
├─ Biophysical realism
│  ├─ Ion channels → HodgkinHuxleyNeuron (4 ODEs, Nobel 1963)
│  ├─ Cortical L5 → HayL5PyramidalNeuron (3-compartment, BAC firing)
│  ├─ Fast-spiking → WangBuzsakiNeuron (gamma oscillations)
│  ├─ Thalamic relay → DestexheThalamicNeuron (T-current, burst/tonic)
│  ├─ Hippocampal → TraubMilesNeuron (CA3 pyramidal)
│  └─ Cerebellar → DeSchutterPurkinjeNeuron (P-type Ca)
│
├─ Bursting behaviour
│  ├─ Chaotic → HindmarshRoseNeuron (3D, chaotic bursting)
│  ├─ Parabolic → PlantR15Neuron (Aplysia)
│  ├─ Square-wave → ShermanRinzelKeizerNeuron (pancreatic beta)
│  └─ Respiratory → ButeraRespiratoryNeuron (pre-Botzinger)
│
├─ Simple + fast
│  ├─ 1 ODE → LapicqueNeuron, QuadraticIFNeuron
│  ├─ 2 ODEs → Izhikevich, AdExNeuron, FitzHughNagumoNeuron
│  ├─ Source Heaviside caricature → McKeanNeuron
│  ├─ SC triangular compatibility → SCTriangularMcKeanNeuron
│  └─ Map (O(1)) → RulkovMapNeuron, ChialvoMapNeuron
│
├─ Adaptive firing rate
│  ├─ Source non-resetting MAT* → MATNeuron
│  ├─ SC RK4/reset compatibility → SCResettingMATNeuron
│  ├─ Source one-timescale MAT(1) → NonResettingLIFNeuron
│  ├─ SC affine-rest compatibility → SCNonResettingAdaptiveLIFNeuron
│  ├─ Sampled APSDM source encoder → SigmaDeltaNeuron
│  ├─ SC bipolar accumulator compatibility → SCSigmaDeltaAccumulatorNeuron
│  ├─ Source metabolic eLIF → EnergyLIFNeuron
│  ├─ SC normalized energy compatibility → SCNormalizedEnergyLIFNeuron
│  ├─ Other threshold adaptation → HomeostaticLIFNeuron, GLIFNeuron
│  ├─ Source universal rate adaptation → BendaHerzNeuron
│  ├─ SC stochastic rate adaptation → SCStochasticRateAdaptationNeuron
│  ├─ Conductance LIF adaptation → SFANeuron
│  └─ Reward-modulated → EPropALIFNeuron (with eligibility traces)
│
├─ Population / neural mass (EEG, fMRI)
│  ├─ EEG → JansenRitUnit, WendlingNeuron (epilepsy)
│  ├─ Decision-making → WongWangUnit (attractor dynamics)
│  ├─ Whole-brain → LarterBreakspearNeuron (TVB compatible)
│  └─ Exact mean-field → ErmentroutKopellPopulation
│
├─ Hardware-specific
│  ├─ Intel Loihi → LoihiCUBANeuron, Loihi2Neuron
│  ├─ IBM TrueNorth → TrueNorthNeuron
│  ├─ SpiNNaker → SpiNNakerLIFNeuron, SpiNNaker2Neuron
│  ├─ BrainScaleS → BrainScaleSAdExNeuron
│  ├─ DYNAP-SE → DPINeuron
│  └─ BrainChip Akida → AkidaNeuron
│
├─ ML / training-oriented
│  ├─ Learnable tau → ParametricLIFNeuron (PLIF, Fang 2021)
│  ├─ Learnable gates → GatedLIFNeuron (NeurIPS 2022)
│  ├─ Fully learnable → LearnableNeuronModel (all params learnable)
│  ├─ Liquid networks → LiquidTimeConstantNeuron, ClosedFormContinuousNeuron
│  └─ Parallel processing → ParallelSpikingNeuron (no sequential dependency)
│
├─ Stochastic / statistical
│  ├─ Rate-based input → PoissonNeuron, InhomogeneousPoissonNeuron
│  ├─ Stochastic threshold → EscapeRateNeuron, GalvesLocherbachNeuron
│  ├─ Noise-driven → StochasticIFNeuron (Ornstein-Uhlenbeck)
│  ├─ GLM encoding → GLMNeuron (stimulus + history filters)
│  └─ Regular spiking → GammaRenewalNeuron
│
└─ Glial / non-neuronal
   └─ Astrocyte Ca2+ → AstrocyteModel (IP3 receptor dynamics)

Quick Comparison by Speed

SpeedModelsUse case
O(1) per stepMcCullochPitts, Rulkov, Chialvo, MedvedevLargest networks
1 ODELIF, IF, QIF, ExpIF, Lapicque, ThetaStandard SNN
2 ODEsIzhikevich, AdEx, FHN, Morris-Lecar, SRMRich dynamics
3 ODEsHindmarsh-Rose, FitzHugh-Rinzel, PernarowskiBursting
4+ ODEsHH, Connor-Stevens, DestexheBiophysical detail
6+ ODEsHay L5, Jansen-Rit, Marder STGMulti-compartment / neural mass

Quick Start Examples

Simple LIF network

from sc_neurocore.neurons import StochasticLIFNeuron

neuron = StochasticLIFNeuron()
for t in range(1000):
    spike = neuron.step(input_current=1.5)

Biophysical Hodgkin-Huxley

from sc_neurocore.neurons import HodgkinHuxleyNeuron

hh = HodgkinHuxleyNeuron()
for t in range(1000):
    spike = hh.step(current=10.0)  # 10 uA/cm^2

Cortical L5 pyramidal with BAC firing

from sc_neurocore.neurons.models.hay_l5 import HayL5PyramidalNeuron

l5 = HayL5PyramidalNeuron()
for t in range(1000):
    spike = l5.step(current_soma=5.0, current_tuft=2.0)

Hardware deployment (Intel Loihi)

from sc_neurocore.neurons import LoihiCUBANeuron

loihi = LoihiCUBANeuron(v_threshold=500)
spike = loihi.step(weighted_input=200)

Population-level EEG simulation

from sc_neurocore.neurons import JansenRitUnit

jr = JansenRitUnit()
eeg_signal = [jr.step(p_ext=220.0) for _ in range(10000)]

Model Categories

1. Integrate-and-Fire Family (26 models)

The workhorse of SNN simulation. Fast, analytically tractable.

LIF, FixedPointLIF, HomeostaticLIF, IF, ALIF, Lapicque, ExpIF, QIF, SecondOrderLIF, FractionalLIF, GLIF, MAT, SFA, StochasticIF, EscapeRate, PerfectIntegrator, NLIF, COBA, AdaptiveThreshold, PLIF, NonResetting, GatedLIF, SigmaDelta, KLIF, ILIF, IntegerQIF

2. Biophysical / Conductance-Based (12 models)

Ion-channel-level detail. For understanding spike generation mechanisms.

HodgkinHuxley, ConnorStevens, WangBuzsaki, PinskyRinzel, Destexhe, HuberBraun, GutkinErmentrout, TraubMiles, GolombFS, MainenSejnowski, Pospischil, DeSchutterPurkinje

3. Multi-Compartment (4 models)

Separate soma and dendrite dynamics. For dendritic computation.

HayL5Pyramidal, BoothRinzel, Dendrify, TwoCompartmentLIF

4. Adaptive (4 models)

Spike-frequency adaptation and threshold dynamics.

AdEx, Izhikevich, MihalasNiebur, BendaHerz, SCStochasticRateAdaptation (count-neutral compatibility identity)

5. Oscillatory / Qualitative (7 models)

Phase-plane dynamics, bifurcation analysis.

FitzHughNagumo, MorrisLecar, HindmarshRose, ResonateAndFire, Theta, FitzHughRinzel, TermanWang

6. Bursting (6 models)

Periodic bursts of spikes followed by quiescence.

Chay, ButeraRespiratory, ShermanRinzelKeizer, PlantR15, BertramPhantom, Pernarowski

7. Map-Based / Discrete (6 models)

Discrete-time iteration, no ODE solver needed. Fastest option.

Rulkov, Chialvo, CourageNekorkin, Medvedev, IbarzTanaka, Cazelles

8. Population / Neural Mass (6 models)

Model whole brain regions, not individual neurons.

WilsonCowan, JansenRit, WongWang, ErmentroutKopell, AmariField, Wendling, LarterBreakspear

9. Hardware-Specific (9 models)

Match the behaviour of specific neuromorphic chips.

LoihiCUBA, Loihi2, TrueNorth, BrainScaleSAdEx, SpiNNakerLIF, SpiNNaker2, DPI, Akida, SigmaDelta

10. Stochastic / Statistical (5 models)

Probabilistic spike generation.

Poisson, InhomogeneousPoisson, GalvesLocherbach, GLM, GammaRenewal

11. Synaptic (3 models)

Detailed synaptic current dynamics.

Alpha, Synaptic (dual-exponential), TsodyksMarkram

12. ML / Modern (8 models)

Designed for gradient-based training.

ParametricLIF, GatedLIF, LearnableNeuronModel, LiquidTimeConstant, ClosedFormContinuous, ParallelSpiking, EPropALIF, SuperSpike

13. Rate / Abstract (4 models)

Non-spiking, continuous-valued models.

McCullochPitts, SigmoidRate, ThresholdLinearRate, WilsonHR

14. Other (2 models)

McKean (source Heaviside caricature), SC triangular McKean-like recurrence, Astrocyte (glial Ca2+)

Next Steps