Model directory

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Network models

Network Models

Neurons, synapses, and devices

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Complete A-Z list of models

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Neurons

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

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

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

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

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

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

• eprop_readout – Current-based leaky integrate readout 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 or exponentially filtered 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

Synapses

• 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

• eprop_learning_signal_connection – Synapse model transmitting feedback learning signals for e-prop plasticity

• eprop_learning_signal_connection_bsshslm_2020 – Synapse model transmitting feedback learning signals for e-prop plasticity

• eprop_synapse – Synapse type for e-prop plasticity

• eprop_synapse_bsshslm_2020 – Synapse type for e-prop plasticity

• 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

• sic_connection – Synapse type for astrocyte-neuron 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_facetshw_synapse_hom – Synapse type for spike-timing dependent plasticity using homogeneous parameters

• 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_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

• weight_optimizer – Selection of weight optimizers

Devices

• 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

Learn more about …

• neuron models

• synapse models

• devices

• creating and customizing models with NESTML

• Model terminology

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What we mean by models

The term models in the context of NEST (and the field of computational neuroscience as a whole) is used with two different meanings:

1. Neuron and synapse models. These consist of a set of mathematical equations and algorithmic components that describe the characteristics and behavior of biological neurons and synapses. In NEST, the terms neuron and synapse models are also used for the C++ implementations of these conceptual entities. Most of the models in NEST are based on either peer-reviewed publications or text books like [1]. This is what we mean for models in our model directory. Note that devices are not models but are mechanisms to generate or read out signals, like spikes. We list them together with neurons and synapses since they are required to be able to produce and analyze neuron and synapse activity.

2. Network models. These models are created from individual neuron and synapse models using the different commands provided by the PyNEST API. Network models have a defined population and connectivity, with initial conditions, along with specific neuron and synapse models. We have a variety of examples for network models and specifically, large scale networks examples.

See also

See our glossary section on common abbreviations used for model terms. It includes alternative terms commonly used in the literature.

Create and customize models with NESTML

Check out NESTML, a domain-specific language for neuron and synapse models. NESTML enables fast prototyping of new models using an easy to understand, yet powerful syntax. This is achieved by a combination of a flexible processing toolchain written in Python with high simulation performance through the automated generation of C++ code, suitable for use in NEST Simulator.

See also

See the NESTML docs for installation details.

References

[1]

Dayan P and Abbott L (2001). Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. Cambridge, MA: MIT Press. https://pure.mpg.de/pubman/faces/ViewItemOverviewPage.jsp?itemId=item_300