2a64a9d6d97a5c203bd58c0d039eb7e63c07e80f,snntoolbox/simulation/backends/inisim/ttfs.py,SpikeLayer,update_neurons,#SpikeLayer#,67

Before Change

        self.add_update([(self.refrac_until, new_refrac)])

        if self.spiketrain is not None:
            self.add_update([(self.spiketrain, self.time * k.cast(
                k.not_equal(output_spikes, 0), k.floatx()))])

        // Compute post-synaptic potential.
        psp = self.get_psp(output_spikes)

After Change

        return self.__class__.__name__

    def update_neurons(self):
        Update neurons according to activation function.

        // Update membrane potentials.
        new_mem = self.get_new_mem()

        // Generate spikes.
        if hasattr(self, "activation_str") \
                and self.activation_str == "softmax":
            output_spikes = self.softmax_activation(new_mem)
        else:
            output_spikes = self.linear_activation(new_mem)

        // Reset membrane potential after spikes.
        self.set_reset_mem(new_mem, output_spikes)

        // Store refractory period after spikes.
        if hasattr(self, "activation_str") \
                and self.activation_str == "softmax":
            // We do not constrain softmax output neurons.
            new_refrac = tf.identity(self.refrac_until)
        else:
            new_refrac = tf.where(tf.not_equal(output_spikes, 0),
                                  self.time + self.tau_refrac,
                                  self.refrac_until)
        self.refrac_until.assign(new_refrac)

        if self.spiketrain is not None:
            self.spiketrain.assign(self.time * tf.cast(
                tf.not_equal(output_spikes, 0), self._floatx))

        // Compute post-synaptic potential.
        psp = self.get_psp(output_spikes)

        return tf.cast(psp, self._floatx)

    def linear_activation(self, mem):
        Linear activation.

Instances