placed pointer to rao-ballard1999 exhibit; updates to docs

Author: Alexander Ororbia

docs/museum/index.rst

@@ -5,18 +5,22 @@
 Model Exhibits
 ==============
 
-Models are presented in ngc-learn's model museum in the form of "exhibits",
-which are effectively model-specific walkthroughs and analyses, based on the
-relevant, referenced publicly available ngc-learn simulation code.
+Models are presented in ngc-learn's model museum in the form of "exhibits", which are effectively model-specific walkthroughs and analyses, based on the relevant, referenced publicly available ngc-learn simulation code.
 
 .. toctree::
   :maxdepth: 1
-  :caption: Neuromimetic Models
+  :caption: Neuroscience Models
 
-  pcn_discrim
   sparse_coding
+  pc_rao_ballard1999
   snn_dc
+  rl_snn
+
+.. toctree::
+  :maxdepth: 1
+  :caption: NeuroAI / Neuro-mimetic Models
+
+  pcn_discrim
   snn_bfa
   harmonium
   sindy
-  rl_snn

docs/museum/model_museum.md

@@ -1,20 +1,9 @@
 # The Model Museum
 
-There is an ever-growing galaxy of neurobiological models and credit assignment
-processes [1, 2]. One of ngc-learn's aims, in the spirit of scientific
-reproducibility, is to capture a snapshot of as many of these
-biomimetic/neuro-mimetic models as possible, in the form of a digital "museum".
-This museum is further designed with the notion of exhibits and exhibitors,
-aiding to facilitate credit assignment and respectful citation to the ideas and
-the work of those that have helped to lay the foundations for the progress
-observed today. Recently, we have separated out the model museum into its own
-particular maintained repository called
-[ngc-museum](https://github.com/NACLab/ngc-museum), where you can find and
-access/run historical models and agents built with ngc-learn to perform
-different experimental tasks.
+There is an ever-growing galaxy of neurobiological models and credit assignment processes [1, 2]. One of ngc-learn's aims, in the spirit of scientific reproducibility, is to capture a snapshot of as many of these biomimetic/neuro-mimetic models as possible, in the form of a digital "museum".
+This museum is further designed with the notion of exhibits and exhibitors, aiding to facilitate credit assignment and respectful citation to the ideas and the work of those that have helped to lay the foundations for the progress observed today. Recently, we have separated out the model museum into its own particular maintained repository called [ngc-museum](https://github.com/NACLab/ngc-museum), where you can find and access/run historical models and agents built with ngc-learn to perform different experimental tasks.
 
-Please refer to the [table of contents](../museum/index.rst) for walkthroughs on
-using and running various historical models in the museum.
+Please refer to the [table of contents](../museum/index.rst) for walkthroughs and guidance on using and running various historical model exhibits in the museum.
 
 ## References
 <b>[1]</b> Ororbia, Alexander G. "Brain-inspired machine intelligence: A survey

docs/museum/pc_rao_ballard1999.md

@@ -0,0 +1,12 @@
+# Hierarchical Predictive Coding (Rao & Ballard)
+
+In this exhibit, we create, simulate, and visualize the
+internally acquired receptive fields of the predictive coding model originally proposed in (Rao & Ballard, 1999) [1].
+
+The model code for this
+exhibit can be found
+[here](https://github.com/NACLab/ngc-museum/tree/main/exhibits/pc_recon).
+
+<!-- references -->
+## References
+<b>[1]</b> Rao, Rajesh PN, and Dana H. Ballard. "Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects." Nature neuroscience 2.1 (1999): 79-87.