All about neurons in NEST

Guides on using neurons in NEST

Node management (neurons and devices)

• How to handle nodes (neurons and devices)

• Parametrization

Exact integration

Integrating neural models using exact integration

Precise spike times

Simulations with precise spike times

List of neuron models

• aeif_cond_alpha – Conductance based exponential integrate-and-fire neuron model

• aeif_cond_alpha_astro – Conductance based exponential integrate-and-fire neuron model with support for neuron-astrocyte interactions

• aeif_cond_alpha_multisynapse – Conductance based adaptive exponential integrate-and-fire neuron model

• aeif_cond_beta_multisynapse – Conductance based adaptive exponential integrate-and-fire neuron model

• aeif_cond_exp – Conductance based exponential integrate-and-fire neuron model

• aeif_psc_alpha – Current-based exponential integrate-and-fire neuron model

• aeif_psc_delta – Current-based adaptive exponential integrate-and-fire neuron model with delta synapse

• aeif_psc_delta_clopath – Adaptive exponential integrate-and-fire neuron

• aeif_psc_exp – Current-based exponential integrate-and-fire neuron model

• amat2_psc_exp – Non-resetting leaky integrate-and-fire neuron model with exponential PSCs and adaptive threshold

• cm_default – A neuron model with user-defined dendrite structure. Currently, AMPA, GABA or AMPA+NMDA receptors.

• eprop_iaf_adapt_bsshslm_2020 – Current-based leaky integrate-and-fire neuron model with delta-shaped postsynaptic currents and threshold adaptation for e-prop plasticity

• eprop_iaf_bsshslm_2020 – Current-based leaky integrate-and-fire neuron model with delta-shaped postsynaptic currents for e-prop plasticity

• eprop_readout_bsshslm_2020 – Current-based leaky integrate readout neuron model with delta-shaped postsynaptic currents for e-prop plasticity

• erfc_neuron – Binary stochastic neuron with complementary error function as activation function

• gauss_rate – Rate neuron model with Gaussian gain function

• gif_cond_exp – Conductance-based generalized integrate-and-fire neuron (GIF) model (from the Gerstner lab)

• gif_cond_exp_multisynapse – Conductance-based generalized integrate-and-fire neuron (GIF) with multiple synaptic time constants (from the Gerstner lab)

• gif_pop_psc_exp – Population of generalized integrate-and-fire neurons (GIF) with exponential postsynaptic currents and adaptation (from the Gerstner lab)

• gif_psc_exp – Current-based generalized integrate-and-fire neuron (GIF) model (from the Gerstner lab)

• gif_psc_exp_multisynapse – Current-based generalized integrate-and-fire neuron (GIF) model with multiple synaptic time constants (from the Gerstner lab)

• ginzburg_neuron – Binary stochastic neuron with sigmoidal activation function

• glif_cond – Conductance-based generalized leaky integrate and fire (GLIF) model (from the Allen Institute)

• glif_psc – Current-based generalized leaky integrate-and-fire (GLIF) models (from the Allen Institute)

• glif_psc_double_alpha – Current-based generalized leaky integrate-and-fire (GLIF) models with double alpha-function (from the Allen Institute)

• hh_cond_beta_gap_traub – Hodgkin-Huxley neuron with gap junction support and beta function synaptic conductances

• hh_cond_exp_traub – Hodgkin-Huxley model for Brette et al (2007) review

• hh_psc_alpha – Hodgkin-Huxley neuron model

• hh_psc_alpha_clopath – Hodgkin-Huxley neuron model with support for Clopath plasticity

• hh_psc_alpha_gap – Hodgkin-Huxley neuron model with gap-junction support

• ht_neuron – Neuron model after Hill & Tononi (2005)

• iaf_bw_2001 – Leaky integrate-and-fire-neuron model with conductance-based synapses and additional NMDA receptors with simplified dynamics.

• iaf_bw_2001_exact – Leaky integrate-and-fire-neuron model with conductance-based synapses and additional NMDA receptors.

• iaf_chs_2007 – Spike-response model used in Carandini et al. 2007

• iaf_chxk_2008 – Conductance-based leaky integrate-and-fire neuron model supporting precise spike times used in Casti et al. 2008

• iaf_cond_alpha – Simple conductance based leaky integrate-and-fire neuron model

• iaf_cond_alpha_mc – Multi-compartment conductance-based leaky integrate-and-fire neuron model

• iaf_cond_beta – Simple conductance based leaky integrate-and-fire neuron model

• iaf_cond_exp – Simple conductance based leaky integrate-and-fire neuron model

• iaf_cond_exp_sfa_rr – Conductance based leaky integrate-and-fire model with spike-frequency adaptation and relative refractory mechanisms

• iaf_psc_alpha – Leaky integrate-and-fire model with alpha-shaped input currents

• iaf_psc_alpha_multisynapse – Leaky integrate-and-fire neuron model with multiple ports

• iaf_psc_alpha_ps – Current-based leaky integrate-and-fire neuron with alpha-shaped postsynaptic currents using regula falsi method for approximation of threshold crossing

• iaf_psc_delta – Leaky integrate-and-fire model with delta-shaped input currents

• iaf_psc_delta_ps – Current-based leaky integrate-and-fire neuron model with delta-shaped postsynaptic currents - precise spike timing version

• iaf_psc_exp – Leaky integrate-and-fire neuron model with exponential-shaped input currents

• iaf_psc_exp_htum – Leaky integrate-and-fire model with separate relative and absolute refractory period

• iaf_psc_exp_multisynapse – Leaky integrate-and-fire neuron model with multiple ports

• iaf_psc_exp_ps – Current-based leaky integrate-and-fire neuron with exponential-shaped postsynaptic currents using regula falsi method for approximation of threshold crossing

• iaf_psc_exp_ps_lossless – Current-based leaky integrate-and-fire neuron with exponential-shaped postsynaptic currents predicting the exact number of spikes using a state space analysis

• iaf_tum_2000 – Leaky integrate-and-fire neuron model with exponential PSCs and integrated short-term plasticity synapse

• ignore_and_fire – Ignore-and-fire neuron model for generating spikes at fixed intervals irrespective of inputs

• izhikevich – Izhikevich neuron model

• lin_rate – Linear rate model

• mat2_psc_exp – Non-resetting leaky integrate-and-fire neuron model with exponential PSCs and adaptive threshold

• mcculloch_pitts_neuron – Binary deterministic neuron with Heaviside activation function

• parrot_neuron – Neuron that repeats incoming spikes

• parrot_neuron_ps – Neuron that repeats incoming spikes - precise spike timing version

• pp_cond_exp_mc_urbanczik – Two-compartment point process neuron with conductance-based synapses

• pp_psc_delta – Point process neuron with leaky integration of delta-shaped PSCs

• rate_neuron_ipn – Base class for rate model with input noise

• rate_neuron_opn – Base class for rate model with output noise

• rate_transformer_node – Rate neuron that sums up incoming rates and applies a nonlinearity specified via the template

• siegert_neuron – model for mean-field analysis of spiking networks

• sigmoid_rate – Rate neuron model with sigmoidal gain function

• sigmoid_rate_gg_1998 – rate model with sigmoidal gain function

• spike_train_injector – Neuron that emits prescribed spike trains.

• tanh_rate – rate model with hyperbolic tangent non-linearity

• threshold_lin_rate – Rate model with threshold-linear gain function

Neuron model naming conventions

Neuron model names in NEST combine abbreviations that describe the dynamics and synapse specifications for that model. They may also include the author’s name of a model based on a specific paper.

For example, the neuron model name

iaf_cond_beta

corresponds to an implementation of a spiking neuron using integrate-and-fire dynamics with conductance-based synapses. Incoming spike events induce a postsynaptic change of conductance modeled by a beta function.

As an example for a neuron model name based on specific paper,

hh_cond_exp_traub

implements a modified version of the Hodgkin Huxley neuron model based on Traub and Miles (1991)