Glossary¶

Units of measure¶

C_m¶: Capacitance of the membrane in picofarads (pF).
capacitance¶: Picofarads (pF).
conductance¶: Nanosiemens (nS).
current¶: Picoamperes (pA).
E_ex¶: Excitatory reversal potential in Millivolts (mV).
E_in¶: Inhibitory reversal potential in Millivolts (mV).
E_K¶: Potassium reversal potential in millivolts (mV).
E_L¶: Resting membrane potential in Millivolts (mV).
E_Na¶: Sodium reversal potential in Millivolts (mV).
frequency¶: Frequency in Hertz (Hz). Note that spike rates are often better expressed in terms of spikes per second (spks/s or s^-1).
g_K¶: Potassium peak conductance in nanosiemens (nS).
g_L¶: Leak conductance in nanosiemens (nS).
g_Na¶: Sodium peak conductance in nanosiemens (nS).
I_e¶: Constant input current in picoamperes (pA).
spike rates¶: The number of spikes that occurred in a certain time interval, usually expressed in terms of spikes per second (spks/s or s^-1).
t_ref¶: Duration of refractory period in milliseconds (ms).
t_spike¶: Point in time of last spike in milliseconds (ms).
tau_m¶: Membrane time constant in milliseconds (ms).
time¶: Time in milliseconds (ms).
V_m¶: Membrane potential in Millivolts (mV).
V_min¶: Absolute lower value for the membrane potential in Millivolts (mV).
V_reset¶: Reset potential of the membrane in Millivolts (mV).
V_th¶: Spike threshold in Millivolts (mV).
voltage¶: Millivolts (mV).

Terms for models in NEST¶

aeif¶: Adaptive exponential integrate-and-fire. Also known in other sources as AdEx.
cm¶: Compartmental model.
cond¶: Conductance-based. Also known in other sources as COBA.
ex¶: Excitatory.
gif¶: Generalized integrate-and-fire. From the Gerstner lab.
glif¶: Generalized leaky integrate-and-fire. From the Allen institute.
hh¶: Hodgkin Huxley.
ht¶: Hill and Tononi.
iaf¶: Integrate-and-fire. Also known in other sources as IF.
in¶: Inhibitory.
pp¶: Point process.
psc¶: Post-synaptic current (current-based). Also known in other sources as CUBA.
psp¶: Post-synaptic potential.
sfa¶: Spike-frequency adaptation.
st¶: Short-term plasticity.
stdp¶: Spike-timing dependent plasticity.

Model selector keywords¶

The model selector uses keywords (tags) to categorise models.

Synapse keywords¶

chemical: Unidirectional spike transmission from presynaptic to postsynaptic neuron.
electrical: Bidirectional voltage-based transmission.
abstract: Non-biological models, often used for rate-based simulations.
rate: Rate-coded transmission (continuous signals).
learning: Learning signal connections for e-prop.
functional: Synapses with dynamic functional properties.
static: Static synapses with no plasticity.
stochastic: Stochastic spike transmission where neurotransmitter release is probabilistic.
stp: Short-term plasticity.
stdp: Spike-timing dependent plasticity.
3-factor: 3-factor plasticity rules (e.g., Clopath, Urbanczik, Vogels-Sprekeler).
astrocyte: Astrocyte coupling mode.

Neuron keywords¶

neuron: A model of a biological neuron. NEST implements point neurons, multi-compartment neurons, rate neurons, and binary neurons.
integrate-and-fire: Neuron model that integrates synaptic input until the membrane potential reaches a threshold, at which point a spike is fired and the potential is reset.
current-based: Models post-synaptic responses as changes in current. The response is independent of the neuronal state.
conductance-based: Models post-synaptic responses as changes in conductance. The response depends on the membrane potential, capturing more realistic synaptic behavior.
hard threshold: Neuron fires deterministically when the membrane potential reaches a fixed threshold. Does not model the intrinsic dynamics of spike generation.
soft threshold: Neuron models the voltage-dependent conductances underlying spike generation, producing dynamics that mimic the action potential waveform.
adaptation: Neuron has a mechanism that reduces excitability after spiking, such as an adaptive threshold or spike-triggered hyperpolarizing current.
adaptive threshold: A spike threshold that increases temporarily after each spike and decays back to baseline, modelling spike-frequency adaptation.
compartmental model: Neuron subdivided into multiple compartments representing different morphological parts (soma, dendrites), with inputs received and coupled across compartments.
binary: Neuron with two or three discrete states (On/Off). The simplest threshold activation models, used in theoretical neuroscience and disease modelling.
precise: Neuron model that calculates exact spike times rather than grid-constrained spike times, at higher computational cost.
parrot: Auxiliary neuron that repeats all incoming spikes. Used for testing, benchmarking, or creating shared spike input patterns.
stochastic: Neuron that does not fire deterministically; spike times are drawn from a point process with a firing rate determined by the membrane potential.
point process: Stochastic neuron model where spike times are described by a point process with a time-dependent firing rate.
Hodgkin-Huxley: A conductance-based neuron model based on Hodgkin and Huxley (1952), A quantitative description of membrane current and its application to conduction and excitation in nerve, The Journal of Physiology 117. See also Hodgkin-Huxley.
Clopath plasticity: A voltage-based STDP plasticity rule based on Clopath et al. (2010), Connectivity reflects coding: a model of voltage-based STDP with homeostasis, Nature Neuroscience 13:3.
Hill-Tononi plasticity: A thalamocortical neuron model based on Hill and Tononi (2005), Modeling sleep and wakefulness in the thalamocortical system, Journal of Neurophysiology 93:1671–1698.
e-prop plasticity: A learning rule for recurrent spiking networks based on Bellec et al. (2020), A solution to the learning dilemma for recurrent networks of spiking neurons, Nature Communications 11:3625.

Other abbreviations¶

MAM¶: Multi-area model.
mpi¶: Message passing interface.
rng¶: Random number generator.
vp¶: Virtual process.
wfr¶: Waveform relaxation method.

Commonly used terms in NEST¶

absolute refractory period¶

Interval directly following a spike emission in which the sender neuron cannot fire again.

alpha function¶

Instance of a synaptic response.

autapse¶

A neuron connected to itself.

axon¶

The output structure of a neuron.

Clopath¶

Refering to the Clopath plasticity rule.

coefficient of variation¶

Standard deviation divided by the mean.

dendritic arbor¶

Dendritic trees formed to create new synapses.

depressing window¶

A function that determines how synaptic modification depends on spike-timing (STDP).

depression¶

Mechanism of making a synapse weaker by decreasing the weight.

Opposite to facilitation.

distal dendrite¶

The part of the dentrite that is furthest away from the soma.

eligibility trace¶

A property of a synapse, which allows it to be modified for a period of time when some constraints are satisfied.

events¶

Spikes are encoded as events in NEST.

facilitation¶

Mechanism of making a synapse stronger by increasing the weight.

Opposite to depression.

Gaussian white noise¶

A random process with a mean of zero.

Hodgkin-Huxley¶

A mathematical model that describes how action potentials in neurons can be generated and how they propagate.

indegree¶

Amount of connections to post-synaptic cells.

multapse¶

A neuron that has (multiple) synapses with another neuron.

multimeter¶

A device to record analog quantities (e.g., membrane voltage) of a neuron over time.

non-renewal process¶

Point process with adapting threshold eta(t).

outdegree¶

Amount of connections from pre-synaptic cells.

plasticity¶

The ability of a network to grow or reorganize.

point neuron¶

A simple neuron model where its soma along with the membrane potential dynamics are modeled as a resistance–capacitance circuit.

Point process¶

A temporal point process is a mathematical model for a time series of discrete events.

propagator¶

Matrix used in a numerically integrated dynamical system.

See exact integration page for further information.

proximal dendrite¶

The part of the dentrite which is closest to the soma.

refractoriness¶

The time before a new action potential can take place.

refractory period¶

A time period in which neurons cannot fire. This is due to depolarization.

refractory time¶

A time period in which neurons cannot fire due to depolarization.

renewal process¶

Spike-time statistical analysis.

reversal potential¶

The membrane potential at which a neuron causes no net current flow.

rheobase¶

The minimal current that is required to generate a spike.

shotnoise¶

Fluctuations in ion channels as a result of ionic migration through an open channel.

soma¶

Cell body of the neuron.

spike train¶

A sequence of actions potentials. Usually seen as events in integrate-and-fire models.

spike-frequency adaptation¶

After stimulation, neurons show a reduction in the firing frequency of their spike response following an initial increase.

spike-timing dependent plasticity¶

STDP, a form of plasticity which adjusts the connection strength between neurons based on the relative timing of a neurons output and input spikes.

static_synapse¶

Synapse with a fixed weight.

stdp_synapse¶

Synapse with spike-timing dependent plasticity.

subthreshold dynamics¶

Non-spiking backgound activity of the synapses.

synaptic efficacy¶

The extent to which a pre-synaptic neuron affects a post-synaptic neuron.

synaptic response kernel¶

Shape of post-synaptic response, commonly an alpha, delta-pulse, or exponential function.

time constant¶

The time it takes for a signal to rise or decay in milliseconds (ms).

See membrane time constant (tau_m) and synaptic time constant (tau_syn) in the model documentation.