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1.0.0
GETTING STARTED
Installation
Fundamentals of Sinabs
A quick (and over-simplified) introduction to spiking neurons
Quickstart Sinabs
Python versions, pyenv and pipenv
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
SingleSpike
MultiSpike
MaxSpike
Surrogate gradient functions
MultiGaussian
Heaviside
SingleExponential
PeriodicExponential
TUTORIALS
Training by backpropagation through time (BPTT)
Converting an ANN to an SNN
sinabs Tutorial 使用入门
HOW TOS
Minimise the number of synaptic operations
Change activations in spiking layers
PLUGINS
API REFERENCE
network
layers
IAF
IAFSqueeze
IAFRecurrent
LIF
LIFSqueeze
LIFRecurrent
ExpLeak
ExpLeakSqueeze
ALIF
ALIFRecurrent
ExpLeak
ExpLeakSqueeze
SpikingMaxPooling2dLayer
SumPool2d
Img2SpikeLayer
Sig2SpikeLayer
StatefulLayer
SqueezeMixin
Cropping2dLayer
FlattenTime
UnflattenTime
NeuromorphicReLU
QuantizeLayer
activation
SingleSpike
MultiSpike
MaxSpike
MembraneReset
MembraneSubtract
SingleExponential
PeriodicExponential
Heaviside
MultiGaussian
from_torch
SynOpCounter
ABOUT
About this project
How is Sinabs different?
Contributing to sinabs
Release notes
repository
open issue
suggest edit
.rst
.pdf
TUTORIALS
TUTORIALS
#
Training by backpropagation through time (BPTT)
Converting an ANN to an SNN
sinabs Tutorial 使用入门
