Remove "rate-mode" from attractor notebook

- Removed "rate-mode" from attractor network

docs/examples/notebooks/06-chaotic-attractor.ipynb

@@ -277,7 +277,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The plots above show that with spiking neurons, the output of the network is, expectedly, more noisy (less precise) than the results of the rate-mode network. However, despite this, the chaotic attractor network in its current configuration is stable, and exhibits the expected behaviour of the Lorenz attractor.\n",
+    "The plots above show that with spiking neurons, the output of the network is, expectedly, more noisy (less precise) than the results of the non-spiking network. However, despite this, the chaotic attractor network in its current configuration is stable, and exhibits the expected behaviour of the Lorenz attractor.\n",
     "\n",
     "It should be noted that the output of the spiking Lorenz attractor network differs from that of the non-spiking network because of two factors. First, every time the Nengo simulator is created, the network parameters are regenerated (randomized). Second, because the dynamics of the Lorenz attractor are extremely sensitive to the initial conditions of the network (which is what makes it chaotic), even if the spiking and non-spiking networks were generated with the same parameters, the additional noise introduced by the spikes cause the spiking output to diverge from the trajectory of the non-spiking network."
    ]
@@ -290,7 +290,7 @@
     "\n",
     "Try playing around with the number of neurons in the FPGA ensemble as well as the synaptic time constant (`tau`) to see how it effects performance (e.g., observe how changing these numbers affect the stability of the oscillator)! Additionally, modify the parameters of the Lorenz attractor to see what effect it has on the shape of it. Be sure to run the simulation multiple times and observe if you can see any identical patterns (because the attractor is chaotic, every run should be different).\n",
     "\n",
-    "Explore the deterministic (yet chaotic) nature of the Lorenz attractor network, by constructing the `FpgaPesEnsembleNetwork` with the `seed` parameter, and re-running this notebook. You should observe because the Lorenz attractor is deterministic, with a pre-set seed, every run of the notebook will produce identical results. However, because the attractor is chaotic (very sensitive to the recurrent activity in the network), the additional noise introduced by the spikes cause it to follow a different trajectory from the rate-mode network."
+    "Explore the deterministic (yet chaotic) nature of the Lorenz attractor network, by constructing the `FpgaPesEnsembleNetwork` with the `seed` parameter, and re-running this notebook. You should observe because the Lorenz attractor is deterministic, with a pre-set seed, every run of the notebook will produce identical results. However, because the attractor is chaotic (very sensitive to the recurrent activity in the network), the additional noise introduced by the spikes cause it to follow a different trajectory from the non-spiking network."
    ]
   }
  ],