a1526ee9692296243b5587b969e67278f01e8a98,bindsnet/network/nodes.py,DiehlAndCookNodes,step,#DiehlAndCookNodes#Any#Any#,383

Before Change

		self.s = (self.v >= self.thresh + self.theta) * (self.refrac_count == 0)

		// Refractoriness, voltage reset, and adaptive thresholds.
		self.refrac_count[self.s] = self.refrac
		self.v[self.s] = self.reset
		self.theta += self.theta_plus * self.s.float()
		
		// Choose only a single neuron to spike.

After Change

			| :code:`dt` (:code:`float`): Simulation time step.
		"""
		// Decay voltages and adaptive thresholds.
		self.v -= dt * self.decay * (self.v - self.rest)
		self.theta -= dt * self.theta_decay * self.theta
		
		// Decrement refractory counters.
		self.refrac_count[self.refrac_count != 0] -= dt

		// Check for spiking neurons.
		self.s = (self.v >= self.thresh + self.theta) * (self.refrac_count == 0)

		// Refractoriness, voltage reset, and adaptive thresholds.
		self.refrac_count = self.refrac_count.masked_fill(self.s, self.refrac)
		self.v = self.v.masked_fill(self.s, self.reset)
		self.theta += self.theta_plus * self.s.float()
		
		// Choose only a single neuron to spike.
		if torch.sum(self.s) > 0:

Instances