Merge over further doc edits/clean-up from main (#120)

ago109 · Alexander Ororbia · rxng8 · web-flow · commit 45d711c51129 · 2025-07-30T15:14:27.000-04:00
* minor edit to math in hh-lesson doc * Fix workflow, numpy install, and pytest bug in github action workflows (#117) * Update pyproject.toml * Update python-package-conda.yml * Update python-package-conda.yml * Update python-package-conda.yml * Update python-package-conda.yml * Update python-package-conda.yml * Update python-package-conda.yml * Update python-package-conda.yml * minor nudge/cleanup to minor patched version 2.0.1 * minor nudge/cleanup to minor patched version 2.0.3 * Merged back minor doc fix back to main (for syncing purposes) (#119) * Nudge of release to minor patched version 2.0.3 (#118) * nudge of doc to 2.0.2 (#115) Co-authored-by: Alexander Ororbia <ago@hal3.cs.rit.edu> * minor edit to math in hh-lesson doc * Fix workflow, numpy install, and pytest bug in github action workflows (#117) * Update pyproject.toml * Update python-package-conda.yml * Update python-package-conda.yml * Update python-package-conda.yml * Update python-package-conda.yml * Update python-package-conda.yml * Update python-package-conda.yml * Update python-package-conda.yml * minor nudge/cleanup to minor patched version 2.0.1 * minor nudge/cleanup to minor patched version 2.0.3 --------- Co-authored-by: Alexander Ororbia <ago@hal3.cs.rit.edu> Co-authored-by: Viet Dung Nguyen <60036798+rxng8@users.noreply.github.com> * fixed typo/error in doc evolving_synapses.md --------- Co-authored-by: Alexander Ororbia <ago@hal3.cs.rit.edu> Co-authored-by: Viet Dung Nguyen <60036798+rxng8@users.noreply.github.com> * minor clean-up in model_basics docs * minor fixes/cleanup of docs --------- Co-authored-by: Alexander Ororbia <ago@hal3.cs.rit.edu> Co-authored-by: Viet Dung Nguyen <60036798+rxng8@users.noreply.github.com>
diff --git a/docs/tutorials/model_basics/evolving_synapses.md b/docs/tutorials/model_basics/evolving_synapses.md
@@ -29,14 +29,13 @@ dkey, *subkeys = random.split(dkey, 6)
 
 ## create simple system with only one F-N cell
 with Context("Circuit") as circuit:
-  a = RateCell(name="a", n_units=1, tau_m=0.,
-               act_fx="identity", key=subkeys[0])
-  b = RateCell(name="b", n_units=1, tau_m=0.,
-               act_fx="identity", key=subkeys[1])
-
-  Wab = HebbianSynapse(name="Wab", shape=(1, 1), eta=1.,
-                       sign_value=-1., weight_init=dist.constant(value=1.),
-                       w_bound=0., key=subkeys[3])
+  a = RateCell(name="a", n_units=1, tau_m=0., act_fx="identity", key=subkeys[0])
+  b = RateCell(name="b", n_units=1, tau_m=0., act_fx="identity", key=subkeys[1])
+
+  Wab = HebbianSynapse(
+    name="Wab", shape=(1, 1), eta=1., sign_value=-1., weight_init=dist.constant(value=1.),
+    w_bound=0., key=subkeys[3]
+  )
 
   # wire output compartment (rate-coded output zF) of RateCell `a` to input compartment of HebbianSynapse `Wab`
   Wab.inputs << a.zF
diff --git a/docs/tutorials/model_basics/model_building.md b/docs/tutorials/model_basics/model_building.md
@@ -21,12 +21,11 @@ dkey, *subkeys = random.split(dkey, 4)
 
 ## create simple dynamical system: a --> w_ab --> b
 with Context("model") as model:
-   a = RateCell(name="a", n_units=1, tau_m=0.,
-                act_fx="identity", key=subkeys[0])
-   b = RateCell(name="b", n_units=1, tau_m=20.,
-                act_fx="identity", key=subkeys[1])
-   Wab = HebbianSynapse(name="Wab", shape=(1, 1),
-                        weight_init=dist.constant(value=1.), key=subkeys[2])
+   a = RateCell(name="a", n_units=1, tau_m=0., act_fx="identity", key=subkeys[0])
+   b = RateCell(name="b", n_units=1, tau_m=20., act_fx="identity", key=subkeys[1])
+   Wab = HebbianSynapse(
+      name="Wab", shape=(1, 1), weight_init=dist.constant(value=1.), key=subkeys[2]
+   )
 ```
 
 Next, we will want to wire together the three components we have embedded into
diff --git a/docs/tutorials/neurocog/hodgkin_huxley_cell.md b/docs/tutorials/neurocog/hodgkin_huxley_cell.md
@@ -77,8 +77,6 @@ essentially probability values:
 `m` ($\mathbf{m}_t$)  for the probability of sodium channel subunit activation, and 
 `h` ($\mathbf{h}_t$)  for the probability of sodium channel subunit inactivation.  
 
-neurons and muscle cells. It is a continuous-time dynamical system.
-
 Formally, the core dynamics of the H-H cell can be written out as follows:
 
 $$
diff --git a/docs/tutorials/neurocog/simple_leaky_integrator.md b/docs/tutorials/neurocog/simple_leaky_integrator.md
@@ -10,7 +10,7 @@ integrator components, the simplified leaky integrate-and-fire (SLIF).
 With our JSON configuration in place, go ahead and create a Python script,
 i.e., `run_slif.py`, to write your code for this part of the tutorial.
 
-Now let's go ahead and set up the controller for this lesson's simulation,
+Now let's go ahead and set up the controller/context for this lesson's simulation,
 where we will a dynamical system with only a single component,
 specifically the simplified LIF (sLIF), like so:
 
@@ -55,14 +55,14 @@ with Context("Model") as model:
 ```
 
 This node has quite a few compartments and constants but only a handful are important
-for understanding how this model governs spiking/firing rates during
-a controller's simulation window. Specifically, in this lesson, we will focus on
+for understanding how this model governs spiking/firing rates within its simulation window. 
+Specifically, in this lesson, we will focus on 
 its electrical current `j` (formally labeled here as $\mathbf{j}_t$),
 its voltage `v` (formally labeled: $\mathbf{v}_t$), its spike emission
 (or action potential) `s` (formally $\mathbf{s}_t$), and its refractory
 variable/marker (formally $\mathbf{r}_t$). The subscript $t$ indicates
 that this compartment variable takes on a certain value at a certain time step
-$t$ and we will refer to the ngc-learn controller's integration time constant,
+$t$ and we will refer to the ngc-learn context's integration time constant,
 the amount of time we move forward by, as $\Delta t$. The constants or
 hyper-parameters we will be most interested in are the cell's membrane resistance
 `R_m` (formally $R$ with its capacitance $C$ implied), its membrane time
@@ -198,7 +198,7 @@ its synaptic current over time - we will not, however, cover this functionality
 in this walkthrough.)-->
 
 In effect, given the above, every time the `sLIF`'s `.advanceState()` function is
-called within a simulation controller (`Controller()`), the above Euler integration of
+called within a simulation controller context (`Context()`), the above Euler integration of
 the membrane potential differential equation is happening each time step. Knowing this,
 the last item required to understand ngc-learn's `sLIF` node's computation is
 related to its spike $\mathbf{s}_t$. The spike reading is computed simply by
