Major release update merge to main (in prep for 2.0.0 release on release branch/pip) (#99)

* add initial patch mask features

* minor edit to bern-cell

* fixed bernoulli error cell

* example rate cell test

* made some corrections to bern err-cell and heb syn

* made some corrections to bern err-cell and heb syn

* cleaned up bern-cell, hebb-syn

* minor mod to model-utils

* attempted rewrite of bernoulli-cell

* got bernoulli-cell rewritten and unit-tested

* edit to bern-cell

* bernoulli and poisson cells revised, unit-tested

* latency-cell refactored and unit-tested

* refactored Rate Cell

* minor revisions to input-encoders, revised phasor-cell w/ unit-test

* revised and add unit-test for varTrace

* revised and added unit-test for exp-kernel

* revised and added unit-test for exp-kernel

* revised slif cell w/ unit-test; needed mod to diffeq

* revised slif-cell w/ unit-test; cleaned up ode_utils to play nicer w/ new sim-lib

* revised lif-cell w/ unit-test

* revised unit-tests to pass globally; some minor patches to phasor-cell and lif

* minor cleanup of unit-test for phasor

* revised if-cell w/ unit-test

* revised if-cell w/ unit-test

* revised quad-lif w/ unit-test

* revised adex-cell w/ unit test, minor cleanup of quad-lif

* minor edit to adex unit-test

* refactor bernoulli, laplacian, and rewarderror cells

* revised raf-cell w/ unit test; fixed typos/mistakes in all spiking cells

* revised wtas-cell w/ unit test

* revised fh-cell w/ unit test

* revised izh-cell w/ unit test

* patched ode_utils backend wrt jax, cleaned up unit-tests, added disable flag for phasor-cell

* update rate cell

* fix test rate cell

* update test for bernoulli cell

* update refactoring for gaussian error cell

* update unit testing for all graded neurons

* wrote+unit-test of hodgkin-huxley spike cell, minor tweaks/clean-up elsewhere

* added rk2 support for H-H cell

* update rate cell and fix bug of passing a tuple of (jax Array -- not hashable)  to jax jit functions. Basically, simplify the codebase by using a hashmap of functions

* update test rate cell

* refactored dense and trace-stdp syn w/ unit-test

* refactored exp-stdp syn w/ unit-test

* refactored event-stdp w/ unit-test

* cleanup of stdp-syn

* refactored bcm syn w/ unit-test

* refactored stp-syn with unit-test

* cleaned up modulated

* refactored mstdp-et syn w/ unit-test

* refactored lava components to new sim-lib

* refactored conv/hebb-conv syn w/ unit-test

* refactored/revised hebb-deconv syn w/ unit-test

* revised/refactored hebb/stdp conv/deconv syn w/ unit-tests

* updated modeling doc to point to hodgkin-huxley cell

* updated modeling docs

* fixed typo in adex-cell tutorial doc

* revised tutorials to reflect new sim-lib config/syntax

* revised tutorials to reflect new sim-lib config/syntax

* patched docs to reflect revisions/refactor

* tweaked requirements in prep for major release

* cleaned up a few unit tests to use deterministic syn init vals

* mod to requirements

* nudge toml to upcoming 2.0.0

* update to support docs in prep for 2.0.0

* update patched synapses and their test cases

* cleaned up syn modeling doc

* push hebbian synapse

* push reinforce synapse

* push np seed

* patched minor prior None arg issue in hebb-syn

* moved reinforce-syn to right spot

* update reinforce synapse and testing

* tweaked trace-stdp and mstdpet

* patched mstdpet unit-test

* update reinforce synapse and test cases

* add reinforce synapse fix

* minor mod to mstdpet

* update test code for more than 1 steps

* Updated monitors

* patched tests to use process naming

* Added wrapper for reset and advance_state

* Added a JaxProcess

Added Jax Process to allow for scanning over the process.

* update the old rate cell

* update old hebbian synapse

* minor edit to if-cell

* ported over adex tutorial to new ngclearn format

* hh-cell supports rk4 integration

* clean up and integrated hodgkin-huxley mini lesson in neurocog tutorials

* Update jaxProcess.py

Updated the jax process to allow for more configurations of inputs.

* update working reinforce synapse

* update correct reinforce and testing

* update documentation

* update features, documentation, and testing

* update testing for REINFORCE cell

* update code and test

* update code

* add clipping gradient to model utils

* update reinforce cell to the new model utils clip

* update test cases

---------

Co-authored-by: Viet Dung Nguyen <[email protected]>
Co-authored-by: Alexander Ororbia <[email protected]>
Co-authored-by: Will Gebhardt <[email protected]>

Author: 4 people

AUTHORS

@@ -5,10 +5,11 @@
 # The email address is not required for organizations.
 
 Core Team
-Alexander Ororbia <[email protected]>
-William Gebhardt <[email protected]>
-Viet Dung Nguyen <[email protected]>
-Ankur Mali <[email protected]>
+Alexander Ororbia <[email protected]>
+William Gebhardt <[email protected]>
+Viet Dung Nguyen <[email protected]>
+Ankur Mali <[email protected]>
+Faeze Habibi <[email protected]>
 
 Contributors
 Maxbeth2 (Ohas)

README.md

@@ -37,7 +37,7 @@ ngc-learn requires:
 2) NumPy (>=1.26.0)
 3) SciPy (>=1.7.0)
 4) ngcsimlib (>=0.3.b4), (visit official page <a href="https://github.com/NACLab/ngc-sim-lib">here</a>)
-5) JAX (>= 0.4.18) (to enable GPU use, make sure to install one of the CUDA variants)
+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`
 6) matplotlib (>=3.4.3) if using `ngclearn.utils.viz`
@@ -79,7 +79,7 @@ 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__
-'1.2b3'
+'2.0.0'
 ```
 
 <i>Note:</i> For access to the previous Tensorflow-2 version of ngc-learn (of
@@ -126,7 +126,7 @@ $ python install -e .
 </pre>
 
 **Version:**<br>
-1.2.3-Beta <!-- -Alpha -->
+2.0.0 <!--1.2.3-Beta--> <!-- -Alpha -->
 
 Author:
 Alexander G. Ororbia II<br>

docs/conf.py

@@ -117,7 +117,7 @@
 html_logo = "images/ngc-learn-logo.png"
 html_theme_options = {
     'logo_only': True,
-    'display_version': True,
+    #'display_version': True,
 }
 
 # def setup(app):

docs/modeling/input_encoders.md

@@ -71,3 +71,18 @@ whereas lower intensity values yield later firing times.
   .. automethod:: reset
     :noindex:
 ```
+
+### Phasor Cell
+
+This cell takes a real-valued pattern(s) and transforms it on-the-fly to
+a spike train composed of pulses emitted a regular interval. 
+
+```{eval-rst}
+.. autoclass:: ngclearn.components.PhasorCell
+  :noindex:
+
+  .. automethod:: advance_state
+    :noindex:
+  .. automethod:: reset
+    :noindex:
+```

docs/modeling/neurons.md

@@ -228,3 +228,20 @@ fast spiking (FS), low-threshold spiking (LTS), and resonator (RZ) neurons.
   .. automethod:: reset
     :noindex:
 ```
+
+### The Hodgkin-Huxley Cell
+
+This cell models dynamics over voltage `v` and three channels/gates (related to 
+potassium and sodium activation/inactivation). This sophisticated cell system is, 
+as a result,  a set of four coupled differential equations and is driven by an appropriately  configured set of biophysical constants/coefficients (default values of which have  been set according to relevant source work). 
+(Note that this cell supports either Euler or midpoint method / RK-2 integration.)
+
+```{eval-rst}
+.. autoclass:: ngclearn.components.HodgkinHuxleyCell
+  :noindex:
+
+  .. automethod:: advance_state
+    :noindex:
+  .. automethod:: reset
+    :noindex:
+```

docs/modeling/synapses.md

@@ -17,3 +17,4 @@ relevant, referenced publicly available ngc-learn simulation code.
   sparse_coding
   snn_dc
   snn_bfa
+  sindy

docs/museum/index.rst

@@ -436,7 +436,7 @@ $\mathbf{ẏ} = -2.0\mathbf{x} - 0.1\mathbf{y}$
 ẋ =  2.000  𝑦 -0.100  𝑥 
 ẏ = -0.100  𝑦 -2.000  𝑥
 
-$[𝑦, 𝑦^2, 𝑥, 𝑥𝑦, 𝑥^2]$
+[𝑦, 𝑦^2, 𝑥, 𝑥𝑦, 𝑥^2]
 [[ 1.999  0. -0.100  0.  0.]
  [-0.099  0. -1.999  0.  0.]]
 ```

docs/museum/sindy.md

@@ -209,7 +209,7 @@ To use your saved model and examine its performance on the MNIST test-set, you
 can execute the evaluation script like so:
 
 ```console
-$ python analyze_dcsnn.py --dataX=../data/mnist/testX.npy --sample_idx=0
+$ python analyze_dcsnn.py --dataX=../../data/mnist/testX.npy --sample_idx=0
 ```
 
 while will produce a visualization of your DC-SNN's receptive fields (the

docs/museum/snn_dc.md

@@ -20,6 +20,14 @@ ngclearn.components.input\_encoders.latencyCell module
    :undoc-members:
    :show-inheritance:
 
+ngclearn.components.input\_encoders.phasorCell module
+-----------------------------------------------------
+
+.. automodule:: ngclearn.components.input_encoders.phasorCell
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 ngclearn.components.input\_encoders.poissonCell module
 ------------------------------------------------------