PyNEST examples

Simple networks

• One neuron example

• One neuron with noise

• Two neuron example

• Balanced neuron example

• Example of an AEIF neuron with multiple synaptic rise and decay time constants

• IAF neuron example with current generator

• Initial membrane voltage

• Multi-compartment neuron example

2D Spatially-structured networks

• Spatial networks: Pyramidal cells and interneurons

• Spatial networks: Convergent projection and rectangular mask, from target perspective

• Spatial networks: Convergent projection and rectangular mask, from source perspective

• Spatial networks: Circular mask and flat probability

• Spatial networks: Circular mask and flat probability, with edge wrap

• Spatial networks: 4x3 grid with pyramidal cells and interneurons

• Spatial networks: Gaussian probabilistic kernel

• Spatial networks: 4x3 grid

• Spatial networks: Freely placed neurons

• Spatial networks: 4x3 grid for three populations

3D Spatially-structured networks

• Spatial networks: A spatial network in 3D

• Spatial networks: A spatial network in 3D with exponential connection probabilities

• Spatial networks: A spatial network in 3D with Gaussian connection probabilities

NEST Sudoku solver

• Script controlling the simulation of a single game of Sudoku

• Generate a GIF of the network solving a Sudoku puzzle

NEST Pong game

• Application to train networks to play pong against each other

• Script to visualize a simulated Pong game.

Astrocytes

• A model using a single astrocyte with calcium dynamics

• A tripartite interaction between two neurons and one astrocyte

• A small neuron-astrocyte network

• Random balanced network with astrocytes with Bernoulli connectivity

• Random balanced network with astrocytes with fixed-indegree connectivity

Random balanced networks (Brunel)

• Random balanced network (alpha synapses) connected with NEST

• Random balanced network (delta synapses)

• Mean-field theory for random balanced network

• Random balanced network (exp synapses, multiple time constants)

• Use evolution strategies to find parameters for a random balanced network (alpha synapses)

Large scale cortical network models

• Cortical Microcircuit Model

• The mesocircuit model: A layered cortical network with distance-dependent connectivity

• The multi-area model: A network model of the visual cortex of the macaque monkey

• The HuMAM model: Multi-Scale Spiking Network Model of Human Cerebral Cortex

EI clustered network (Rostami et al)

EI-clustered circuit model

GLIF (from Allen institute)

• Conductance-based generalized leaky integrate and fire (GLIF) neuron example

• Current-based generalized leaky integrate and fire (GLIF) neuron example

• Current-based generalized leaky integrate and fire (GLIF) neuron with double alpha synaptic function

Compartmental neurons

• Constructing and simulating compartmental models with different receptor types

• Constructing and simulating compartmental models with active and passive dendrites

GIF (from Gerstner lab)

• Example network using generalized IAF neuron with postsynaptic conductances

• Population of GIF neuron model with oscillatory behavior

• Population rate model of generalized integrate-and-fire neurons

Rate neurons

• Network of linear rate neurons

• Rate neuron decision making

Hodgkin-Huxley

• Example using Hodgkin-Huxley neuron

• Numerical phase-plane analysis of the Hodgkin-Huxley neuron

Brody Hopfield

• Spike synchronization through subthreshold oscillation

Brette and Gerstner

• Testing the adapting exponential integrate and fire model in NEST (Brette and Gerstner Fig 2C)

• Testing the adapting exponential integrate and fire model in NEST (Brette and Gerstner Fig 3D)

Precise spiking

• Comparing precise and grid-based neuron models

• Artificial synchrony in discrete-time simulations

Campbell Siegert

• Campbell & Siegert approximation example

Decision making in recurrent network (after Wang 2002)

• Decision making in recurrent network with NMDA-dynamics

Gap junctions

• Gap Junctions: Two neuron example

• Gap Junctions: Inhibitory network example

Structural plasticity

• Structural Plasticity example

Synapse collection

• Synapse Collection usage example

Urbanczik

• Weight adaptation according to the Urbanczik-Senn plasticity

Clopath

• Clopath Rule: Spike pairing experiment

• Clopath Rule: Bidirectional connections

Short-term plasticity

• Short-term depression example

• Short-term facilitation example

• Example of the tsodyks2_synapse in NEST

E-prop plasticity examples

• Tutorial on learning to accumulate evidence with e-prop after Bellec et al. (2020)

• Tutorial on learning to generate sine waves with e-prop after Bellec et al. (2020)

• Tutorial on learning to generate handwritten text with e-prop after Bellec et al. (2020)

• Tutorial on learning to generate a lemniscate with e-prop after Bellec et al. (2020)

• Tutorial on learning to generate sine waves with e-prop

• Tutorial on learning N-MNIST classification with e-prop

Recording and analyzing networks

• Example of multimeter recording to file

• Plot weight matrices example

• IF curve example

• Pulse packet example

• Correlospinmatrix detector example

• Auto- and crosscorrelation functions for spike trains

Stimulating networks

• Sinusoidal poisson generator example

• Sinusoidal gamma generator example

• Repeated Stimulation

• Sensitivity to perturbation

• Intrinsic currents spiking

• Intrinsic currents subthreshold

Handling output

• Recording examples

• Store and restore a network simulation

• Music example

SONATA network

• NEST SONATA network

HPC benchmark

• Random balanced network HPC benchmark

Brette et al. 2007 Benchmarks

Brette et al. 2007 Benchmarks

Connection set algebra

• Using CSA for connection setup

• Using CSA with spatial populations