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  • GETTING STARTED
    • Installation
    • Fundamentals of Sinabs
    • A quick (and over-simplified) introduction to spiking neurons
    • Quickstart Sinabs
    • Python versions, pyenv and pipenv
  • Speck
    • Overview
    • The Basics
    • Advanced
    • Dangers
    • Dynapcnn Visualizer
    • Specksim
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      • Chip Errata(Hardware Bugs)
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      • Save Samna Config As Binary
      • How to add support for a new chip
      • How To Leak The Neurons On The Devkit
  • SINABS GALLERY
    • Neuron models
      • Exponential Leaky Layer (ExpLeak)
      • Adaptive Leaky Integrate and Fire (ALIF)
      • Integrate and Fire (IAF)
      • Leaky Integrate and Fire (LIF)
    • Spike functions
      • MultiSpike
      • SingleSpike
      • MaxSpike
    • Surrogate gradient functions
      • Gaussian
      • Heaviside
      • MultiGaussian
      • SingleExponential
      • PeriodicExponential
  • TUTORIALS
    • Training by backpropagation through time (BPTT)
    • Converting an ANN to an SNN
    • Training NMNIST for deployment on Speck using EXODUS
    • Scaling parameters for rate-coded conversion to SNNs
    • Import a model from NIR and deploy it to SPECK
    • sinabs Tutorial 使用入门
    • Hardware Tutorials
      • Quick Start With N-MNIST
      • Visualize DVS Input
      • Play With Speck’s DVS
      • How To Leak The Neurons On The Devkit
      • Power Monitoring
      • Spike Count Visualization
      • Using Readout Layer
  • HOW TOS
    • Training an ANN with fewer SynOps
    • Training an SNN with fewer SynOps and monitoring the firing rate
    • Add custom hooks to monitor network properties
    • Change activations in spiking layers
  • PLUGINS
  • API REFERENCE
    • network
    • layers
      • StatefulLayer
      • SqueezeMixin
      • IAF
      • IAFSqueeze
      • IAFRecurrent
      • LIF
      • LIFSqueeze
      • LIFRecurrent
      • ALIF
      • ALIFRecurrent
      • ExpLeak
      • ExpLeakSqueeze
      • SpikingMaxPooling2dLayer
      • SumPool2d
      • Img2SpikeLayer
      • Sig2SpikeLayer
      • Cropping2dLayer
      • Repeat
      • FlattenTime
      • UnflattenTime
      • NeuromorphicReLU
      • QuantizeLayer
    • activation
      • SingleSpike
      • MultiSpike
      • MaxSpike
      • MembraneReset
      • MembraneSubtract
      • SingleExponential
      • PeriodicExponential
      • Heaviside
      • Gaussian
      • MultiGaussian
    • from_torch
    • hooks
    • SynOpCounter
    • utils
    • nir
    • sinabs.backend.dynapcnn
      • chip_factory
      • config_builder
      • crop2d
      • discretize
      • dvs_layer
      • dynapcnn_network
      • dynapcnn_layer
      • exceptions
      • flipdims
      • io
      • mapping
      • utils
      • dynapcnn_visualizer
      • specksim
  • ABOUT
    • About this project
    • How is Sinabs different?
    • Contributing to sinabs
    • Release notes
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  • .rst

SINABS GALLERY

Contents

  • Neuron models
  • Spike functions
  • Surrogate gradient functions

SINABS GALLERY#

Neuron models#

sphx_glr_auto_examples_layers_plot_exp_leaky.py

Exponential Leaky Layer (ExpLeak)

sphx_glr_auto_examples_layers_plot_alif.py

Adaptive Leaky Integrate and Fire (ALIF)

sphx_glr_auto_examples_layers_plot_iaf.py

Integrate and Fire (IAF)

sphx_glr_auto_examples_layers_plot_lif.py

Leaky Integrate and Fire (LIF)

Spike functions#

sphx_glr_auto_examples_spike_fns_plot_multispike.py

MultiSpike

sphx_glr_auto_examples_spike_fns_plot_singlespike.py

SingleSpike

sphx_glr_auto_examples_spike_fns_plot_maxspike.py

MaxSpike

Surrogate gradient functions#

sphx_glr_auto_examples_surrogate_grad_fns_plot_gaussian.py

Gaussian

sphx_glr_auto_examples_surrogate_grad_fns_plot_heaviside.py

Heaviside

sphx_glr_auto_examples_surrogate_grad_fns_plot_multigaussian.py

MultiGaussian

sphx_glr_auto_examples_surrogate_grad_fns_plot_singleexponential.py

SingleExponential

sphx_glr_auto_examples_surrogate_grad_fns_plot_periodicexponential.py

PeriodicExponential

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Neuron models

Contents
  • Neuron models
  • Spike functions
  • Surrogate gradient functions

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