Crate oldies_nest 

NEST-RS: NEST Simulator Revival

Revival of the NEST simulator (https://www.nest-simulator.org/) NEST = NEural Simulation Tool Originally created by Marc-Oliver Gewaltig and Markus Diesmann

NEST is designed for large-scale spiking neural network simulations with efficient parallelization and precise spike timing.

Key features:

• Node-based architecture (neurons, devices, connections)
• Precise spike timing with grid/off-grid modes
• Efficient connection management with synapse types
• Built-in parallelization support
• Recording devices (spike detectors, multimeters)

Structs

AeifCondAlphaParams

Parameters for aeif_cond_alpha (AdEx)

BernoulliParams

Bernoulli synapse parameters

Connection

Connection (edge)

ConnectionSpec

Connection specification

ContinuousData

Recorded continuous data

DcGeneratorParams

DC generator parameters

HhPscAlphaParams

Parameters for hh_psc_alpha (Hodgkin-Huxley)

IafCondAlphaParams

Parameters for iaf_cond_alpha

IafCondExpParams

Parameters for iaf_cond_exp

IafPscAlphaParams

Parameters for iaf_psc_alpha

IafPscDeltaParams

Parameters for iaf_psc_delta

IafPscExpParams

Parameters for iaf_psc_exp

IzhikevichParams

Parameters for Izhikevich neuron

Kernel

NEST kernel (simulation state)

KernelParams

Simulation parameters

MultimeterParams

Multimeter parameters

NodeCollection

Collection of node IDs (like NEST’s NodeCollection)

NodeState

Node state variables

NoiseGeneratorParams

Noise generator parameters

PoissonGeneratorParams

Poisson generator parameters

SpikeData

Recorded spike events

SpikeGeneratorParams

Spike generator parameters

StdpParams

STDP parameters

TsodyksMarkramParams

Tsodyks-Markram parameters

VogelsSprekelerParams

Vogels-Sprekeler parameters

Enums

ConnectivityRule

Connection rule

DelayDistribution

Delay distribution

NestError

NeuronModel

NEST neuron model types

SynapseModel

NEST synapse model types

WeightDistribution

Weight distribution

Functions

balanced_network

Create a balanced random network (Brunel 2000)

connect

Connect neurons

create

Create neurons

cv_isi

Calculate coefficient of variation of ISI

get_kernel_status

Get kernel status

get_spike_data

Get spike data from spike detector

get_status

Get node status (parameters)

mean_firing_rate

Calculate mean firing rate from spike data

reset_kernel

Initialize the kernel

set_kernel_status

Set kernel status

set_status

Set node status

simulate

Run simulation

spike_correlation

Calculate correlation coefficient between spike trains

Type Aliases

NodeId

Global node identifier

Result