66268ffdc847e64216114871bd4169ad7872966a,snntoolbox/simulation/target_simulators/pyNN_target_sim.py,SNN,simulate,#SNN#,134

Before Change

    def simulate(self, **kwargs):

        if self._poisson_input:
            rates = kwargs[str("x_b_l")].flatten()
            for neuron_idx, neuron in enumerate(self.layers[0]):
                neuron.rate = rates[neuron_idx] / self.rescale_fac * 1000
        elif self._dataset_format == "aedat":
            raise NotImplementedError
        else:
            constant_input_currents = kwargs[str("x_b_l")].flatten()

After Change

        self.sim.run(self._duration - self._dt,
                     callbacks=[MyProgressBar(self._dt, self._duration)])
        print("\nCollecting results...")
        output_b_l_t = self.get_recorded_vars(self.layers)

        return output_b_l_t

Instances