All about synapses and connections in NEST

Guides on using synapses in NEST

Managing connections

• Connectivity concepts

• Synapse specification

• Connection generator interface

• Working with connections

Synapse description

Describing the synapse types, including plasticity and their dynamics

• Types of synapses

• Synapse dynamics

Weight normalization

Weight normalization

Gap Junctions

Simulations with gap junctions

List of synapse models

• 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

Naming conventions for synapse models

Synapse models include the word synapse or connection as the last word in the model name.

Synapse models may begin with the author name (e.g., clopath_synapse) or process (e.g., stdp_synapse).