merge conflicts

Author: chaoming0625

.github/workflows/Publish.yml

@@ -0,0 +1,17 @@
+name: Publish to PyPI.org
+on:
+  release:
+    types: [published]
+jobs:
+  pypi:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+      - run: python setup.py bdist_wheel
+      - name: Publish package
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          password: ${{ secrets.PYPI_API_TOKEN }}

docs/_static/NG-RC-vs-Traditional-RC.png

@@ -225,7 +225,7 @@
     }
    },
    "source": [
-    "In the ``Bifurcation1D`` analyzer, ``model`` refers to the modelto be analyzed (essentially the analyzer will access the derivative function in the model), ``target_vars`` denotes the target variables, ``target_pars`` denotes the changing parameters, and ``resolution`` determines  the resolutioin of the analysis."
+    "In the ``Bifurcation1D`` analyzer, ``model`` refers to the model to be analyzed (essentially the analyzer will access the derivative function in the model), ``target_vars`` denotes the target variables, ``target_pars`` denotes the changing parameters, and ``resolution`` determines  the resolutioin of the analysis."
    ]
   },
   {

docs/_static/conductance_model_diagram.png

@@ -118,17 +118,12 @@ Alternatively, you can download the preferred release ".whl" file for jaxlib, an
     pip install jax
 
 
-.. warning::
-    For m1 macOS users, you should run your python environment on ``Apple`` silicon instead of ``intel``
-    silicon since ``rosetta2`` cannot translate ``jaxlib``. One suggestion is uninstall miniconda3 and install
-    miniforge3 for managing your python environment.
-
 Windows
 ^^^^^^^
 
 For **Windows** users, JAX can be installed by the following methods:
 
-- **Method 1**: There are several community supported Windows build for jax, please refer
+- **Method 1**: There are several communities support JAX for Windows, please refer
   to the github link for more details: https://github.com/cloudhan/jax-windows-builder .
   Simply speaking, the provided binary releases of JAX for Windows
   are available at https://whls.blob.core.windows.net/unstable/index.html .

docs/_static/potassium_channel_equivalent_circuit.png

@@ -33,7 +33,7 @@
     }
    },
    "source": [
-    "In recent years, we saw the revolution that training a dynamical system from data or tasks has provided important insights to understand brain functions. To support this, BrainPy porvides various interfaces to help users train dynamical systems. "
+    "In recent years, we saw the revolution that training a dynamical system from data or tasks has provided important insights to understand brain functions. To support this, BrainPy provides various interfaces to help users train dynamical systems."
    ]
   },
   {
@@ -743,7 +743,7 @@
     }
    },
    "source": [
-    "Here, we demonstrate how to train an artificial recurrent neural network by using a white noise integration task. In this task, we want our trained RNN model has the ability to integrate white noise. For example, if we has a time series of noise data, "
+    "Here, we demonstrate how to train an artificial recurrent neural network by using a white noise integration task. In this task, we want our trained RNN model has the ability to integrate white noise. For example, if we have a time series of noise data,"
    ]
   },
   {

docs/quickstart/analysis.ipynb

@@ -33,7 +33,7 @@
     }
    },
    "source": [
-    "In this section, we are going to use the [low-dimensional analyzers](./lowdim_analysis.ipynb) to make phase plane and bifurcation analysis for the decision making model proposed by (Wong & Wang) [1]. "
+    "In this section, we are going to use the [low-dimensional analyzers](./lowdim_analysis.ipynb) to make phase plane and bifurcation analysis for the decision-making model proposed by (Wong & Wang) [1]."
    ]
   },
   {
@@ -203,7 +203,7 @@
     }
    },
    "source": [
-    "The advantage of the reduced model is that we can understand what dynamical behaviors the model generate for a particular parmeter set using phase-plane analysis and the explore how this behavior changed when the model parameters are variaed (bifurcation analysis). \n",
+    "The advantage of the reduced model is that we can understand what dynamical behaviors the model generate for a particular parmeter set using phase-plane analysis and the explore how this behavior changed when the model parameters are varied (bifurcation analysis).\n",
     "\n",
     "To this end, we will use ``brainpy.analysis`` module. "
    ]
@@ -517,7 +517,7 @@
     }
    },
    "source": [
-    "To see how the ohase portrait of the system changed when we chang the stimulus current, we will generate a bifucation diagram for the reduced model. On the bifurcation diagram the fixed points of the model are shown as a function of a changing parameter. \n",
+    "To see how the phase portrait of the system changed when we chang the stimulus current, we will generate a bifurcation diagram for the reduced model. On the bifurcation diagram the fixed points of the model are shown as a function of a changing parameter.\n",
     "\n",
     "In the next, we generate bifurcation diagrams with the different parameters. "
    ]

docs/quickstart/installation.rst

@@ -206,7 +206,7 @@
     }
    },
    "source": [
-    "``brainpy.analysis.SlowPointFinder`` is aimed to find slow/fixed points of high-dimensional systems. Of course, it can optimize to find fixed points of low-dimensional systems. We take the 2D decision making system as an example. "
+    "``brainpy.analysis.SlowPointFinder`` is aimed to find slow/fixed points of high-dimensional systems. Of course, it can optimize to find fixed points of low-dimensional systems. We take the 2D decision-making system as an example."
    ]
   },
   {
@@ -515,7 +515,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "The following code demonstrates how to use ``SlowPointFinder`` to find fixed points of a continous-attractor neural network."
+    "The following code demonstrates how to use ``SlowPointFinder`` to find fixed points of a continuous attractor neural network."
    ],
    "metadata": {
     "collapsed": false,
@@ -569,7 +569,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "The found fixed points are a series of attractor. We can visualized this line of attractors on a 2D space."
+    "The found fixed points are a series of attractor. We can visualize this line of attractors on a 2D space."
    ],
    "metadata": {
     "collapsed": false,