diff --git a/docs/museum/eirnn_song_etal2016.md b/docs/museum/eirnn_song_etal2016.md
new file mode 100644
index 00000000..691be89d
--- /dev/null
+++ b/docs/museum/eirnn_song_etal2016.md
@@ -0,0 +1,12 @@
+# Excitatory-Inhibitory Recurrent Neural Network (Song et al.; 2016)
+
+In this exhibit, we create, simulate, and visualize the dynamics and adaptation of the excitatory-inhibitory 
+recurrent neural network (EI-RNN) originally proposed in (Song et al., 2016) [1]. 
+
+The model code for this exhibit can be found 
+[here](https://github.com/NACLab/ngc-museum/tree/main/exhibits/ei_rnn).
+
+<!-- references -->
+## References
+<b>[1]</b> Song, H. F., Yang, G. R., & Wang, X. J. Training excitatory-inhibitory recurrent neural networks 
+for cognitive tasks: a simple and flexible framework. PLoS computational biology, 12(2), e1004792 (2016).
diff --git a/docs/museum/index.rst b/docs/museum/index.rst
index a0b58557..21541a04 100644
--- a/docs/museum/index.rst
+++ b/docs/museum/index.rst
@@ -16,6 +16,7 @@ of detailed walkthroughs presented in the table of contents below.)
 
   sparse_coding
   pc_rao_ballard1999
+  eirnn_song_etal2016
   snn_dc
   event_stdp_patches
   rl_snn
diff --git a/ngclearn/components/neurons/graded/leakyNoiseCell.py b/ngclearn/components/neurons/graded/leakyNoiseCell.py
index 9ccf4f8e..ab6245ed 100755
--- a/ngclearn/components/neurons/graded/leakyNoiseCell.py
+++ b/ngclearn/components/neurons/graded/leakyNoiseCell.py
@@ -71,7 +71,6 @@ def __init__(
     ):
         super().__init__(name, **kwargs)
 
-
         self.tau_x = tau_x
         self.sigma_pre = sigma_pre ## a pre-rectification scaling factor
         self.sigma_post = sigma_post ## a post-rectification scaling factor
@@ -110,7 +109,6 @@ def advance_state(self, t, dt):
         key, skey = random.split(self.key.get(), 2)
         eps_post = random.normal(skey, shape=self.x.get().shape)  ## post-rectifier distributional noise
 
-        #x = _run_cell(dt, self.j_input.get(), self.j_recurrent.get(), self.x.get(), eps, self.tau_x, self.sigma_rec, integType=self.intgFlag)
         _step_fns = {
             0: step_euler,
             1: step_rk2,
@@ -152,13 +150,16 @@ def help(cls): ## component help function
             "states":
                 {"x": "Update to continuous noisy, leaky dynamics; value at time t"},
             "outputs":
-                {"r": "A linear rectifier applied to rate-coded dynamics; f(z)"},
+                {"r": "A linear rectifier applied to rate-coded dynamics; f(z)",
+                 "r_prime": "Temporal derivative applied to rate-coded dynamics; f'(z)"},
         }
         hyperparams = {
             "n_units": "Number of neuronal cells to model in this layer",
             "batch_size": "Batch size dimension of this component",
             "tau_x": "State time constant",
-            "sigma_pre": "The non-zero degree/scale of (pre-rectification) noise to inject into this neuron"
+            "act_fx": "Type of rectification function to use",
+            "sigma_pre": "The non-zero degree/scale of pre-rectification noise to inject into this neuron",
+            "sigma_post": "The non-zero degree/scale of post-rectification noise to inject into this neuron"
         }
         info = {cls.__name__: properties,
                 "compartments": compartment_props,