Source code for nengo_dl.neurons

from nengo.neurons import LIFRate
from nengo.params import NumberParam
import numpy as np

[docs]class SoftLIFRate(LIFRate): """LIF neuron with smoothing around the firing threshold. This is a rate version of the LIF neuron whose tuning curve has a continuous first derivative, due to the smoothing around the firing threshold. It can be used as a substitute for LIF neurons in deep networks during training, and then replaced with LIF neurons when running the network [1]_. Parameters ---------- sigma : float Amount of smoothing around the firing threshold. Larger values mean more smoothing. tau_rc : float Membrane RC time constant, in seconds. Affects how quickly the membrane voltage decays to zero in the absence of input (larger = slower decay). tau_ref : float Absolute refractory period, in seconds. This is how long the membrane voltage is held at zero after a spike. References ---------- .. [1] Eric Hunsberger and Chris Eliasmith (2015): Spiking deep networks with LIF neurons. Notes ----- Adapted from """ sigma = NumberParam('sigma', low=0, low_open=True) def __init__(self, sigma=1., **lif_args): super(SoftLIFRate, self).__init__(**lif_args) self.sigma = sigma self._epsilon = 1e-15 @property def _argreprs(self): args = super(SoftLIFRate, self)._argreprs if self.sigma != 1.: args.append("sigma=%s" % self.sigma) return args
[docs] def rates(self, x, gain, bias): J = gain * x J += bias out = np.zeros_like(J) self.step_math(dt=1, J=J, output=out) return out
[docs] def step_math(self, dt, J, output): """Compute rates in Hz for input current (incl. bias)""" x = J - 1 y = x / self.sigma valid = y < 34 y_v = y[valid] np.exp(y_v, out=y_v) np.log1p(y_v, out=y_v) y_v *= self.sigma x[valid] = y_v x += self._epsilon output[:] = 1. / ( self.tau_ref + self.tau_rc * np.log1p(1. / x))