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braincore.surrogate.slayer_grad

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braincore.surrogate.slayer_grad#

braincore.surrogate.slayer_grad(x, alpha=1.0)[source]#

Spike function with the slayer surrogate gradient function.

Forward function:

\[\begin{split}g(x) = \begin{cases} 1, & x \geq 0 \\ 0, & x < 0 \\ \end{cases}\end{split}\]

Backward function:

\[g'(x) = \exp(-\alpha |x|)\]
>>> import braincore as bc
>>> import matplotlib.pyplot as plt
>>> xs = jax.numpy.linspace(-3, 3, 1000)
>>> for alpha in [0.5, 1., 2., 4.]:
>>>   grads = bc.vector_grad(bc.surrogate.slayer_grad)(xs, alpha)
>>>   plt.plot(xs, grads, label=r'$\alpha$=' + str(alpha))
>>> plt.legend()
>>> plt.show()

(Source code)

Parameters:

  • x (jax.Array, Array) – The input data.

  • alpha (float) – Parameter to control smoothness of gradient

Returns:

out – The spiking state.

Return type:

jax.Array

References

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