Expand on advance usage docs.

docs/advanced_usage.md

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+# Advanced usage
+
+Advanced users can access all the underlying functions of `jpc.make_pc_step` as 
+well as additional features. A custom PC training step looks like the following:
+```py
+import jpc
+
+# 1. initialise activities with a feedforward pass
+activities = jpc.init_activities_with_ffwd(model=model, input=x)
+
+# 2. run inference to equilibrium
+equilibrated_activities = jpc.solve_inference(
+    params=(model, None), 
+    activities=activities, 
+    output=y, 
+    input=x
+)
+
+# 3. update parameters at the activities' solution with PC
+param_update_result = jpc.update_params(
+    params=(model, None), 
+    activities=equilibrated_activities,
+    optim=param_optim,
+    opt_state=param_opt_state,
+    output=y, 
+    input=x
+)
+
+# updated model and optimiser
+model = param_update_result["model"]
+param_optim = param_update_result["optim"]
+param_opt_state = param_update_result["opt_state"]
+```
+which can be embedded in a jitted function with any other additional 
+computations. One can also use any Optax optimiser to equilibrate the inference
+dynamics by replacing the function in step 2, as shown below.
+```py
+activity_optim = optax.sgd(1e-3)
+
+# 1. initialise activities
+...
+
+# 2. infer with gradient descent
+activity_opt_state = activity_optim.init(activities)
+
+for t in range(T):
+    activity_update_result = jpc.update_activities(
+        params=(model, None),
+        activities=activities,
+        optim=activity_optim,
+        opt_state=activity_opt_state,
+        output=y,
+        input=x
+    )
+    # updated activities and optimiser
+    activities = activity_update_result["activities"]
+    activity_optim = activity_update_result["optim"]
+    activity_opt_state = activity_update_result["opt_state"]
+
+# 3. update parameters at the activities' solution with PC
+...
+```
+JPC also comes with some analytical tools that can be used to study and 
+potentially diagnose issues with PCNs (see [docs
+](https://thebuckleylab.github.io/jpc/api/Analytical%20tools/) 
+and [example notebook
+](https://thebuckleylab.github.io/jpc/examples/linear_net_theoretical_energy/)).