Model directory

The model directory is organized and autogenerated by keywords (e.g., adaptive threshold, conductance-based etc.). Models that contain a specific keyword will be listed under that word. For more information on models, see our intro to NEST models.

Clopath Plasticity

• aeif_psc_delta_clopath – Adaptive exponential integrate-and-fire neuron

• clopath_synapse – Synapse type for voltage-based STDP after Clopath

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

Hill-Tononi Plasticity

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

• ht_synapse – Synapse with depression after Hill & Tononi (2005)

Hodgkin-Huxley

• 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

MUSIC

• music_cont_in_proxy – A device which receives continuous data from MUSIC

• music_cont_out_proxy – A device which sends continuous data from NEST to MUSIC

• music_event_in_proxy – A device which receives spikes from MUSIC

• music_event_out_proxy – Device to forward spikes to remote applications using MUSIC

• music_message_in_proxy – A device which receives message strings from MUSIC

• music_rate_in_proxy – A device which receives rate data from MUSIC

• music_rate_out_proxy – Device to forward rates to remote applications using MUSIC

Adaptive Threshold

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

• 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

Binary

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

• ginzburg_neuron – Binary stochastic neuron with sigmoidal activation function

• mcculloch_pitts_neuron – Binary deterministic neuron with Heaviside activation function

Compartmental Model

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

Conductance-Based

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

• 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

• 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)

• glif_cond – Conductance-based generalized leaky integrate and fire (GLIF) model (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

• 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

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

Continuous Delay

• cont_delay_synapse – Synapse type for continuous delays

Current-Based

• 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

• 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)

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

• 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

• iaf_psc_alpha – Leaky integrate-and-fire neuron model

• 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 – Current-based leaky integrate-and-fire neuron model with delta-shaped postsynaptic 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 PSCs

• 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

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

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

Detector

• correlation_detector – Device for evaluating cross correlation between two spike sources

• correlomatrix_detector – Device for measuring the covariance matrix from several inputs

• correlospinmatrix_detector – Device for measuring the covariance matrix from several inputs

• spin_detector – Device for detecting binary states in neurons

Device

• ac_generator – Produce an alternating current (AC) input

• correlation_detector – Device for evaluating cross correlation between two spike sources

• correlomatrix_detector – Device for measuring the covariance matrix from several inputs

• correlospinmatrix_detector – Device for measuring the covariance matrix from several inputs

• dc_generator – Provide a direct current (DC) input

• gamma_sup_generator – Simulate the superimposed spike train of a population of gamma processes

• inhomogeneous_poisson_generator – Provides Poisson spike trains at a piecewise constant rate

• mip_generator – Create spike trains as described by the MIP model

• multimeter – Sampling continuous quantities from neurons

• music_cont_in_proxy – A device which receives continuous data from MUSIC

• music_cont_out_proxy – A device which sends continuous data from NEST to MUSIC

• music_event_in_proxy – A device which receives spikes from MUSIC

• music_event_out_proxy – Device to forward spikes to remote applications using MUSIC

• music_message_in_proxy – A device which receives message strings from MUSIC

• music_rate_in_proxy – A device which receives rate data from MUSIC

• music_rate_out_proxy – Device to forward rates to remote applications using MUSIC

• noise_generator – Generate a Gaussian white noise current

• poisson_generator – Generate spikes with Poisson process statistics

• poisson_generator_ps – Simulated neuron firing with Poisson process statistics - precise spike timing version with arbitrary dead times

• ppd_sup_generator – Simulate the superimposed spike train of a population of Poisson processes with dead time

• pulsepacket_generator – Generate sequence of Gaussian pulse packets

• sinusoidal_gamma_generator – Generates sinusoidally modulated gamma spike trains

• sinusoidal_poisson_generator – Generate sinusoidally modulated Poisson spike trains

• spike_dilutor – Repeat incoming spikes with a certain probability

• spike_generator – Generate spikes from an array with spike-times

• spike_recorder – Collecting spikes from neurons

• spike_train_injector – Neuron that emits prescribed spike trains.

• spin_detector – Device for detecting binary states in neurons

• step_current_generator – Provide a piecewise constant DC input current

• step_rate_generator – Provide a piecewise constant input rate

• volume_transmitter – Support node for neuromodulated synaptic plasticity

• weight_recorder – Recording weights from synapses

Gap Junction

• gap_junction – Synapse type for gap-junction connections

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

Generator

• ac_generator – Produce an alternating current (AC) input

• dc_generator – Provide a direct current (DC) input

• gamma_sup_generator – Simulate the superimposed spike train of a population of gamma processes

• inhomogeneous_poisson_generator – Provides Poisson spike trains at a piecewise constant rate

• mip_generator – Create spike trains as described by the MIP model

• noise_generator – Generate a Gaussian white noise current

• poisson_generator – Generate spikes with Poisson process statistics

• poisson_generator_ps – Simulated neuron firing with Poisson process statistics - precise spike timing version with arbitrary dead times

• ppd_sup_generator – Simulate the superimposed spike train of a population of Poisson processes with dead time

• pulsepacket_generator – Generate sequence of Gaussian pulse packets

• sinusoidal_gamma_generator – Generates sinusoidally modulated gamma spike trains

• sinusoidal_poisson_generator – Generate sinusoidally modulated Poisson spike trains

• spike_dilutor – Repeat incoming spikes with a certain probability

• spike_generator – Generate spikes from an array with spike-times

• spike_train_injector – Neuron that emits prescribed spike trains.

• step_current_generator – Provide a piecewise constant DC input current

• step_rate_generator – Provide a piecewise constant input rate

• volume_transmitter – Support node for neuromodulated synaptic plasticity

Instantaneous

• rate_connection_instantaneous – Synapse type for instantaneous rate connections

Instantaneous Rate

• diffusion_connection – Synapse type for instantaneous rate connections between neurons of type siegert_neuron

Integrate-And-Fire

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

• 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

• 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)

• 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)

• 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 neuron model

• 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 – Current-based leaky integrate-and-fire neuron model with delta-shaped postsynaptic 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 PSCs

• 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

• izhikevich – Izhikevich neuron model

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

Neuron

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

• 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.

• 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

• 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_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 neuron model

• 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 – Current-based leaky integrate-and-fire neuron model with delta-shaped postsynaptic 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 PSCs

• 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

• 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

Parrot

• parrot_neuron – Neuron that repeats incoming spikes

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

Point Process

• 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

Precise

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

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

• 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

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

• poisson_generator_ps – Simulated neuron firing with Poisson process statistics - precise spike timing version with arbitrary dead times

Rate

• gauss_rate – Rate neuron model with Gaussian gain function

• lin_rate – Linear rate model

• music_rate_in_proxy – A device which receives rate data from MUSIC

• music_rate_out_proxy – Device to forward rates to remote applications using MUSIC

• rate_connection_delayed – Synapse type for rate connections with delay

• rate_connection_instantaneous – Synapse type for instantaneous rate connections

• 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

• step_rate_generator – Provide a piecewise constant input rate

• tanh_rate – rate model with hyperbolic tangent non-linearity

• threshold_lin_rate – Rate model with threshold-linear gain function

Recorder

• multimeter – Sampling continuous quantities from neurons

• spike_recorder – Collecting spikes from neurons

• weight_recorder – Recording weights from synapses

Short-Term Plasticity

• quantal_stp_synapse – Probabilistic synapse model with short term plasticity

• tsodyks2_synapse – Synapse type with short term plasticity

• tsodyks_synapse – Synapse type with short term plasticity

• tsodyks_synapse_hom – Synapse type with short term plasticity using homogeneous parameters

Spike

• music_event_in_proxy – A device which receives spikes from MUSIC

• music_event_out_proxy – Device to forward spikes to remote applications using MUSIC

• spike_generator – Generate spikes from an array with spike-times

• spike_recorder – Collecting spikes from neurons

• spike_train_injector – Neuron that emits prescribed spike trains.

Spike-Timing-Dependent Plasticity

• clopath_synapse – Synapse type for voltage-based STDP after Clopath

• jonke_synapse – Synapse type for spike-timing dependent plasticity with additional additive factors.

• stdp_dopamine_synapse – Synapse type for dopamine-modulated spike-timing dependent plasticity

• stdp_nn_pre_centered_synapse – Synapse type for spike-timing dependent plasticity with presynaptic-centered nearest-neighbour spike pairing scheme

• stdp_nn_restr_synapse – Synapse type for spike-timing dependent plasticity with restricted symmetric nearest-neighbour spike pairing scheme

• stdp_nn_symm_synapse – Synapse type for spike-timing dependent plasticity with symmetric nearest-neighbour spike pairing scheme

• stdp_pl_synapse_hom – Synapse type for spike-timing dependent plasticity with power law

• stdp_synapse – Synapse type for spike-timing dependent plasticity

• stdp_synapse_facetshw_hom – Synapse type for spike-timing dependent plasticity using homogeneous parameters

• stdp_synapse_hom – Synapse type for spike-timing dependent plasticity using homogeneous parameters

• stdp_triplet_synapse – Synapse type with spike-timing dependent plasticity (triplets)

• urbanczik_synapse – Synapse type for a plastic synapse after Urbanczik and Senn

• vogels_sprekeler_synapse – Synapse type for symmetric spike-timing dependent plasticity with constant depression

Static

• bernoulli_synapse – Static synapse with stochastic transmission

• static_synapse – Synapse type for static connections

• static_synapse_hom_w – Synapse type for static connections with homogeneous weight

Stimulation Backend

• Stimulation backend mpi - Receive stimulation parameters via MPI

Synapse

• bernoulli_synapse – Static synapse with stochastic transmission

• clopath_synapse – Synapse type for voltage-based STDP after Clopath

• cont_delay_synapse – Synapse type for continuous delays

• diffusion_connection – Synapse type for instantaneous rate connections between neurons of type siegert_neuron

• gap_junction – Synapse type for gap-junction connections

• ht_synapse – Synapse with depression after Hill & Tononi (2005)

• jonke_synapse – Synapse type for spike-timing dependent plasticity with additional additive factors.

• quantal_stp_synapse – Probabilistic synapse model with short term plasticity

• rate_connection_delayed – Synapse type for rate connections with delay

• rate_connection_instantaneous – Synapse type for instantaneous rate connections

• static_synapse – Synapse type for static connections

• static_synapse_hom_w – Synapse type for static connections with homogeneous weight

• stdp_dopamine_synapse – Synapse type for dopamine-modulated spike-timing dependent plasticity

• stdp_nn_pre_centered_synapse – Synapse type for spike-timing dependent plasticity with presynaptic-centered nearest-neighbour spike pairing scheme

• stdp_nn_restr_synapse – Synapse type for spike-timing dependent plasticity with restricted symmetric nearest-neighbour spike pairing scheme

• stdp_nn_symm_synapse – Synapse type for spike-timing dependent plasticity with symmetric nearest-neighbour spike pairing scheme

• stdp_pl_synapse_hom – Synapse type for spike-timing dependent plasticity with power law

• stdp_synapse – Synapse type for spike-timing dependent plasticity

• stdp_synapse_facetshw_hom – Synapse type for spike-timing dependent plasticity using homogeneous parameters

• stdp_synapse_hom – Synapse type for spike-timing dependent plasticity using homogeneous parameters

• stdp_triplet_synapse – Synapse type with spike-timing dependent plasticity (triplets)

• tsodyks2_synapse – Synapse type with short term plasticity

• tsodyks_synapse – Synapse type with short term plasticity

• tsodyks_synapse_hom – Synapse type with short term plasticity using homogeneous parameters

• urbanczik_synapse – Synapse type for a plastic synapse after Urbanczik and Senn

• vogels_sprekeler_synapse – Synapse type for symmetric spike-timing dependent plasticity with constant depression