updates to museum doc for v3

Author: Alexander Ororbia

docs/museum/event_stdp_patches.md

@@ -0,0 +1,13 @@
+# Event-based Spike-Timing-Dependent Plasticity (Tavanaei et al.; 2018)
+
+In this exhibit, we create, simulate, and visualize the internally acquired receptive fields of the spiking neural 
+network (SNN) trained via event-based spike-timing-dependent plasticity (EV-STDP) over image patches. This 
+reproduces the SNN model originally proposed in (Tavanaei et al., 2018) [1].
+
+The model code for this exhibit can be found 
+[here](https://github.com/NACLab/ngc-museum/tree/main/exhibits/evstdp_patches).
+
+<!-- references -->
+## References
+<b>[1]</b> Tavanaei, Amirhossein, Timothée Masquelier, and Anthony Maida. "Representation learning using event-based 
+STDP." Neural Networks 105 (2018): 294-303.

docs/museum/harmonium.md

@@ -17,6 +17,7 @@ of detailed walkthroughs presented in the table of contents below.)
   sparse_coding
   pc_rao_ballard1999
   snn_dc
+  event_stdp_patches
   rl_snn
 
 .. toctree::

docs/museum/index.rst

@@ -1,12 +1,12 @@
-# Hierarchical Predictive Coding (Rao & Ballard)
+# Hierarchical Predictive Coding (Rao & Ballard; 1999)
 
-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].
+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
+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.
+<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.

docs/museum/pc_rao_ballard1999.md

@@ -1,4 +1,4 @@
-# Discriminative Predictive Coding
+# Discriminative Predictive Coding (Whittington & Bogacz; 2017)
 
 In this exhibit, we will see how a classifier can be created based on
 predictive coding. This exhibit model effectively reproduces some of the results

docs/museum/pcn_discrim.md

@@ -1,13 +1,10 @@
-# Reinforcement Learning through a Spiking Controller
+# Reinforcement Learning through a Spiking Controller (Chevtchenko et al.; 2020)
 
-In this exhibit, we will see how to construct a simple biophysical model for 
-reinforcement learning with a spiking neural network and modulated 
-spike-timing-dependent plasticity. 
-This model incorporates a mechanisms from several different models, including 
-the constrained RL-centric SNN of <b>[1]</b> as well as the simplifications 
-made with respect to the model of <b>[2]</b>. The model code for this
-exhibit can be found
-[here](https://github.com/NACLab/ngc-museum/tree/main/exhibits/rl_snn).
+In this exhibit, we will see how to construct a simple biophysical model for reinforcement learning with a spiking 
+neural network and modulated spike-timing-dependent plasticity. 
+This model incorporates a mechanisms from several different models, including the constrained RL-centric SNN of 
+<b>[1]</b> as well as some simplifications of the structures used within the SNN of <b>[2]</b>. The model code for this 
+exhibit can be found [here](https://github.com/NACLab/ngc-museum/tree/main/exhibits/rl_snn).
 
 ## Modeling Operant Conditioning through Modulation
 
@@ -123,10 +120,7 @@ RL-SNN model:
 
 <!-- References/Citations -->
 ## References
-<b>[1]</b> Chevtchenko, Sérgio F., and Teresa B. Ludermir. "Learning from sparse 
-and delayed rewards with a multilayer spiking neural network." 2020 International 
-Joint Conference on Neural Networks (IJCNN). IEEE, 2020. <br> 
-<b>[2]</b> Diehl, Peter U., and Matthew Cook. "Unsupervised learning of digit
-recognition using spike-timing-dependent plasticity." Frontiers in computational
-neuroscience 9 (2015): 99.
-
+<b>[1]</b> Chevtchenko, Sérgio F., and Teresa B. Ludermir. "Learning from sparse and delayed rewards with a multilayer 
+spiking neural network." 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. <br> 
+<b>[2]</b> Diehl, Peter U., and Matthew Cook. "Unsupervised learning of digit recognition using spike-timing-dependent 
+plasticity." Frontiers in computational neuroscience 9 (2015): 99.

docs/museum/rl_snn.md

@@ -1,14 +1,4 @@
-<!--
-Make a working code
-Results section jumps to complex examples without first showing a simple case
-No clear connection between the code section and the theoretical explanation
-Missing explanation of hyperparameter selection (threshold, max_iter, etc.)
-Some diagrams (like P1.png and P2.png) are too small to read clearly
-Flow diagrams lack clear directional indicators
-Inconsistent color schemes across visualizations
--->
-
-# Sparse Identification of Non-linear Dynamical Systems (SINDy) 
+# Sparse Identification of Non-linear Dynamical Systems (SINDy; Brunton et al.; 2016) 
 
 In this section, we will study, create, simulate, and visualize a model known as the sparse identification of non-linear dynamical systems (SINDy) [1], implementing it in NGC-Learn and JAX. After going through this demonstration, you will:
 

docs/museum/sindy.md

@@ -1,8 +1,8 @@
-# Spiking Neural Networks: Learning with Broadcast Feedback Alignment
+# Spiking Neural Networks: Learning with Broadcast Feedback Alignment (Samadi et al.; 2017)
 
 In this exhibit, we will see how one can train a spiking neural network model
 using surrogate functions and a credit assignment scheme called broadcast
-feedback alignment (BFA) <b>[1]</b>.
+feedback alignment (BFA) <b>[1]</b>. 
 This exhibit model effectively reproduces some of the results
 reported (Samadi et al., 2017) <b>[1]</b>. The model code for this
 exhibit can be found

docs/museum/snn_bfa.md

@@ -1,4 +1,4 @@
-# The Diehl and Cook Spiking Neuronal Network
+# The Diehl and Cook Spiking Neuronal Network (Diehl & Cook; 2015)
 
 In this exhibit, we will see how a spiking neural network model that adapts
 its synaptic efficacies via spike-timing-dependent plasticity can be created.

docs/museum/snn_dc.md

@@ -1,4 +1,4 @@
-# Sparse Coding and Iterative Thresholding
+# Sparse Coding and Iterative Thresholding (Olshausen & Field; 1996)
 
 In this exhibit, we create, simulate, and visualize the internally acquired filters/atoms of variants of a sparse coding system based on the classical model proposed by (Olshausen & Field, 1996) [1]. 
 After going through this demonstration, you will: