cleaned up neurocog tutorials on plasticity

Author: Alexander Ororbia

docs/tutorials/neurocog/dynamic_synapses.md

@@ -1,4 +1,4 @@
-# Dynamic Synapses and Conductance
+# Lecture 4A: Dynamic Synapses and Conductance
 
 In this lesson, we will study dynamic synapses, or synaptic cable components in 
 ngc-learn that evolve on fast time-scales in response to their pre-synaptic inputs. 

docs/tutorials/neurocog/hebbian.md

@@ -1,4 +1,4 @@
-# Lecture 4A: Hebbian Synaptic Plasticity
+# Lecture 4B: Hebbian Synaptic Plasticity
 
 In ngc-learn, synaptic plasticity is a key concept at the forefront of its
 design in order to promote research into novel ideas and framings of how

docs/tutorials/neurocog/mod_stdp.md

@@ -1,4 +1,4 @@
-# Lecture 4C: Reward-Modulated Spike-Timing-Dependent Plasticity
+# Lecture 4D: Reward-Modulated Spike-Timing-Dependent Plasticity
 
 In this lesson, we will build on the notions of spike-timing-dependent 
 plasticity (STDP), covered [earlier here](../neurocog/stdp.md), to construct 
@@ -247,9 +247,8 @@ for i in range(T_max):
 ```
 
 which will run all three models simultaneously for `200` simulated milliseconds 
-and collect statistics of interest. We may then finally make several 
-plots of what happens under each STDP mode. First, we will plot the resulting  
-synaptic magnitude over time, like so:
+and collect statistics of interest. We may then finally make several plots of what happens under each STDP mode 
+(reproducing some key results in <b>[1]</b>. First, we will plot the resulting  synaptic magnitude over time, like so:
 
 ```python 
 import matplotlib.pyplot as plt
@@ -374,3 +373,8 @@ modulated STDP updates will only occur when the signal is non-zero; this is
 the advantage that MSTDP-ET offers over MSTDP as the synaptic change 
 dynamics persist (yet decay) in between reward presentation times and thus 
 MSTDP-ET will be more effective in cases when the reward signal is delayed.
+
+## References
+
+<b>[1]</b> Florian, Răzvan V. "Reinforcement learning through modulation of spike-timing-dependent synaptic plasticity." 
+Neural computation 19.6 (2007): 1468-1502.

docs/tutorials/neurocog/short_term_plasticity.md

@@ -1,4 +1,4 @@
-# Lecture 4D: Short-Term Plasticity
+# Lecture 4E: Short-Term Plasticity
 
 In this lesson, we will study how short-term plasticity (STP) <b>[1]</b> dynamics 
 -- where synaptic efficacy is cast in terms of the history of presynaptic activity -- 

docs/tutorials/neurocog/stdp.md

@@ -1,4 +1,4 @@
-# Lecture 4B: Spike-Timing-Dependent Plasticity
+# Lecture 4C: Spike-Timing-Dependent Plasticity
 
 In the context of spiking neuronal networks, one of the most important forms
 of adaptation that is often simulated is that of spike-timing-dependent