Model directory¶
NEST has over 100 models, choose an option for finding the one you need!
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Neurons
aeif_cond_alpha – Conductance based 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_clopath – Adaptive exponential integrate-and-fire neuron
aeif_psc_exp – Current-based exponential integrate-and-fire neuron model
erfc_neuron – Binary stochastic neuron with complementary error function as activation function
ginzburg_neuron – Binary stochastic neuron with sigmoidal activation function
hh_cond_exp_traub – Hodgkin-Huxley model for Brette et al (2007) review
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_chs_2007 – Spike-response model used in Carandini et al. 2007
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_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_delta – Leaky integrate-and-fire model with delta-shaped input currents
iaf_psc_exp – Leaky integrate-and-fire neuron model with exponential-shaped input currents
iaf_psc_exp_multisynapse – Leaky integrate-and-fire neuron model with multiple ports
mcculloch_pitts_neuron – Binary deterministic neuron with Heaviside activation function
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
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
eprop_synapse_bsshslm_2020 – Synapse type for e-prop plasticity
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_hom_w – Synapse type for static connections with homogeneous weight
stdp_dopamine_synapse – Synapse type for dopamine-modulated spike-timing dependent plasticity
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)
tsodyks_synapse_hom – Synapse type with short term plasticity using homogeneous parameters
urbanczik_synapse – Synapse type for a plastic synapse after Urbanczik and Senn
Devices
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
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
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
poisson_generator – Generate spikes with Poisson process statistics
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.
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
Learn more about …¶
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:
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.
Network models. These models are created from individual neuron and synapse models using the different commands provided by the PyNEST API. Examples for such network models are the microcircuit model or the multi-area model). You can also explore all our example networks.
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.