Minor merge (typo in integration doc)

.github/workflows/python-package-conda.yml

@@ -10,25 +10,19 @@ jobs:
 
     steps:
     - uses: actions/checkout@v4
-    - name: Set up Python 3.10
-      uses: actions/setup-python@v3
+
+    - name: Install Miniconda
+      uses: conda-incubator/setup-miniconda@v2
       with:
-        python-version: '3.10'
-    - name: Add conda to system path
+        auto-update-conda: true
+        python-version: 3.11
+        environment-name: test
+
+    - name: Install dependencies
       run: |
-        # $CONDA is an environment variable pointing to the root of the miniconda directory
-        echo $CONDA/bin >> $GITHUB_PATH
-    - name: Install current library and dependencies
-      run: |
-        pip install -e .
-    # - name: Lint with flake8
-    #   run: |
-    #     conda install flake8
-    #     # stop the build if there are Python syntax errors or undefined names
-    #     flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-    #     # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-    #     flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
-    - name: Test with pytest
+        conda install -n test numpy=1.26 pytest pip
+        conda run -n test pip install -e .
+
+    - name: Run tests
       run: |
-        conda install pytest
-        pytest
+        conda run -n test pytest

README.md

@@ -32,7 +32,7 @@ ngc-learn requires:
 1) Python (>=3.10)
 2) NumPy (>=1.22.0)
 3) SciPy (>=1.7.0)
-4) ngcsimlib (>=1.0.0), (visit official page <a href="https://github.com/NACLab/ngc-sim-lib">here</a>)
+4) ngcsimlib (>=1.0.1), (visit official page <a href="https://github.com/NACLab/ngc-sim-lib">here</a>)
 5) JAX (>=0.4.28) (to enable GPU use, make sure to install one of the CUDA variants)
 <!--
 5) scikit-learn (>=1.3.1) if using `ngclearn.utils.density`
@@ -42,7 +42,7 @@ ngc-learn requires:
 -->
 
 ---
-ngc-learn 2.0.0 and later require Python 3.10 or newer as well as ngcsimlib >=1.0.0.
+ngc-learn 2.0.3 and later require Python 3.10 or newer as well as ngcsimlib >=1.0.1.
 ngc-learn's plotting capabilities (routines within `ngclearn.utils.viz`) require
 Matplotlib (>=3.8.0) and imageio (>=2.31.5) and both plotting and density estimation
 tools (routines within ``ngclearn.utils.density``) will require Scikit-learn (>=0.24.2).
@@ -75,7 +75,7 @@ Python 3.11.4 (main, MONTH  DAY YEAR, TIME) [GCC XX.X.X] on linux
 Type "help", "copyright", "credits" or "license" for more information.
 >>> import ngclearn
 >>> ngclearn.__version__
-'2.0.0'
+'2.0.3'
 ```
 
 <i>Note:</i> For access to the previous Tensorflow-2 version of ngc-learn (of
@@ -122,7 +122,7 @@ $ python install -e .
 </pre>
 
 **Version:**<br>
-2.0.2 <!--1.2.3-Beta--> <!-- -Alpha -->
+2.0.3 <!--1.2.3-Beta--> <!-- -Alpha -->
 
 Author:
 Alexander G. Ororbia II<br>

docs/installation.md

@@ -9,7 +9,7 @@ requires that you ensure that you have installed the following base dependencies
 your system. Note that this library was developed and tested on Ubuntu 22.04 (and earlier versions on 18.04/20.04).
 Specifically, ngc-learn requires:
 * Python (>=3.10)
-* ngcsimlib (>=1.0.0), (<a href="https://github.com/NACLab/ngc-sim-lib">official page</a>)
+* ngcsimlib (>=1.0.1), (<a href="https://github.com/NACLab/ngc-sim-lib">official page</a>)
 * NumPy (>=1.22.0)
 * SciPy (>=1.7.0)
 * JAX (>= 0.4.28; and jaxlib>=0.4.28) <!--(tested for cuda 11.8)-->
@@ -78,7 +78,7 @@ Python 3.11.4 (main, MONTH  DAY YEAR, TIME) [GCC XX.X.X] on linux
 Type "help", "copyright", "credits" or "license" for more information.
 >>> import ngclearn
 >>> ngclearn.__version__
-'2.0.2'
+'2.0.3'
 ```
 
 <i>Note</i>: If you do not have a JSON configuration file in place (see tutorials

docs/requirements.txt

@@ -8,4 +8,4 @@ matplotlib>=3.8.0
 jax>=0.4.28
 jaxlib>=0.4.28
 imageio>=2.31.5
-ngcsimlib>=1.0.0
+ngcsimlib>=1.0.1

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])
+  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])
+  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
@@ -50,7 +49,7 @@ with Context("Circuit") as circuit:
 
   ## create and compile core simulation commands  
   evolve_process = (JaxProcess()
-                    >> a.evolve)
+                    >> Wab.evolve)
   circuit.wrap_and_add_command(jit(evolve_process.pure), name="evolve")
 
   advance_process = (JaxProcess()

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

docs/tutorials/neurocog/hodgkin_huxley_cell.md

@@ -77,12 +77,10 @@ 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:
 
 $$
-\tau_v \frac{\partial \mathbf{v}_t}{\partial t} &= \mathbf{j}_t - g_Na * \mathbf{m}^3_t * \mathbf{h}_t * (\mathbf{v}_t - v_Na) - g_K * \mathbf{n}^4_t * (\mathbf{v}_t - v_K) - g_L * (\mathbf{v}_t - v_L) \\
+\tau_v \frac{\partial \mathbf{v}_t}{\partial t} &= \mathbf{j}_t - g_{Na} * \mathbf{m}^3_t * \mathbf{h}_t * (\mathbf{v}_t - v_{Na}) - g_K * \mathbf{n}^4_t * (\mathbf{v}_t - v_K) - g_L * (\mathbf{v}_t - v_L) \\
 \frac{\partial \mathbf{n}_t}{\partial t} &= \alpha_n(\mathbf{v}_t) * (1 - \mathbf{n}_t) - \beta_n(\mathbf{v}_t) * \mathbf{n}_t \\
 \frac{\partial \mathbf{m}_t}{\partial t} &= \alpha_m(\mathbf{v}_t) * (1 - \mathbf{m}_t) - \beta_m(\mathbf{v}_t) * \mathbf{m}_t \\
 \frac{\partial \mathbf{h}_t}{\partial t} &= \alpha_h(\mathbf{v}_t) * (1 - \mathbf{h}_t) - \beta_h(\mathbf{v}_t) * \mathbf{h}_t

docs/tutorials/neurocog/integration.md

@@ -194,7 +194,7 @@ which should yield you a plot like the one below:
 
 <img src="../../images/tutorials/neurocog/ode_method_comparison.jpg" width="500" />
 
-As you might observe, RK-4 give the best approximation of the solution. In addition, 
+As you might observe, RK-4 gives the best approximation of the solution. In addition, 
 when the integration step size is held constant, Euler integration
 does quite poorly over just a few steps while RK-2 and Heun's method do much better
 at approximating the analytical equation. In the end, the type of numerical integration method employed can

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

history.txt

@@ -80,3 +80,8 @@ History
   * integration of reinforce-synapse, block/partitioned synapse component ("patched-synapse")
   * basic unit-tests (pytest framework) integrated to support dev
   * includes support for Intel's lava-nc emulator (several spiking/stdp components that play with ngc-lava)
+
+2.0.3
+— — — — — — — — -
+  * Minor patch to point / depend on minor-patched ngcsimlib 1.0.1 (nudge to minor patched release)
+  * Added wrapper `inverse_sigmoid` for original `inverse_logistic` routine in model_utils (for convenience)

ngclearn/utils/model_utils.py

@@ -508,10 +508,13 @@ def d_sigmoid(x):
     sigm_x = nn.sigmoid(x) ## pre-compute once
     return sigm_x * (1. - sigm_x)
 
+def inverse_sigmoid(x, clip_bound=0.03): ## wrapper call for naming convention ease
+    return inverse_logistic(x, clip_bound=clip_bound)
+
 @jit
-def inverse_logistic(x, clip_bound=0.03): # 0.03
+def inverse_logistic(x, clip_bound=0.03):
     """
-    The inverse logistic link - logit function.
+    The inverse logistic link - the logit function.
 
     Args:
         x: data to transform via inverse logistic function

pyproject.toml

@@ -1,10 +1,14 @@
 [build-system]
-requires = ["setuptools>=61.0"]
-build-backend = "setuptools.build_meta"
+requires = [
+  "setuptools>=61.0",    # default
+  "wheel",               # also often needed
+  "numpy>=1.19.5"        # add numpy here for build-time use
+]
+build-backend = "setuptools.build_meta" # using setuptool building engine
 
 [project]
 name = "ngclearn"
-version = "2.0.2"
+version = "2.0.3"
 description = "Simulation software for building and analyzing arbitrary predictive coding, spiking network, and biomimetic neural systems."
 authors = [
   {name = "Alexander Ororbia", email = "ago@cs.rit.edu"},

requirements.txt

@@ -5,6 +5,6 @@ matplotlib>=3.8.0
 patchify
 jax>=0.4.28
 jaxlib>=0.4.28
-ngcsimlib>=1.0.0
+ngcsimlib>=1.0.1
 imageio>=2.31.5
 pandas>=2.2.3