Merge pull request #107 from PKU-NIP-Lab/V2.1.1

fix #98: add the inputs tutorial to toolbox

Author: chaoming0625

brainpy/dyn/neurons/reduced_models.py

@@ -714,7 +714,7 @@ def __init__(self, size, V_rest=-70., V_reset=-70., V_th_inf=-50., V_th_reset=-6
     self.I1 = bm.Variable(bm.zeros(self.num))
     self.I2 = bm.Variable(bm.zeros(self.num))
     self.V_th = bm.Variable(bm.ones(self.num) * -50.)
-    self.V = bm.Variable(bm.zeros(self.num))
+    self.V = bm.Variable(bm.zeros(self.num) - 70.)
     self.input = bm.Variable(bm.zeros(self.num))
     self.spike = bm.Variable(bm.zeros(self.num, dtype=bool))
     self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7)

docs/index.rst

@@ -64,6 +64,7 @@ The code of BrainPy is open-sourced at GitHub:
    tutorial_toolbox/synaptic_weights
    tutorial_toolbox/optimizers
    tutorial_toolbox/runners
+   tutorial_toolbox/inputs
    tutorial_toolbox/monitors
    tutorial_toolbox/saving_and_loading
 

docs/quickstart/simulation.ipynb

@@ -170,7 +170,7 @@
    "id": "43ec39f4",
    "metadata": {},
    "source": [
-    "After build a SNN, we can use it for dynamic simulation. To run a simulation, we need first wrap the network model into a runner. Currently BrainPy provides ``DSRunner``, ``StructRunner``, ``ReportRunner`` in ``brainpy.dyn``. They receive inputs with the same structure, and will be expanded in [Running a Simulation](../tutorial_simulation/runner.ipynb). Here we use ``DSRunner`` as an example:"
+    "After build a SNN, we can use it for dynamic simulation. To run a simulation, we need first wrap the network model into a **runner**. Currently BrainPy provides ``DSRunner`` and ``ReportRunner`` in ``brainpy.dyn``, which will be expanded in the [Runners](../tutorial_simulation/runner.ipynb) tutorial. Here we use ``DSRunner`` as an example:"
    ]
   },
   {
@@ -191,7 +191,7 @@
    "id": "11473917",
    "metadata": {},
    "source": [
-    "To make dynamic simulation more applicable and powerful, users can [**monitor**](../tutorial_simulation/monitors_and_inputs.ipynb) variable trajectories and give [**inputs**](../tutorial_simulation/monitors_and_inputs.ipynb) to target neuron groups. Here we monitor the ``spike`` variable in the ``E`` and ``I`` LIF model, which refers to the spking status of the neuron group, and give a constant input to both neuron groups. The time interval of numerical integration ``dt`` (with the default value of 0.1) can also be specified.\n",
+    "To make dynamic simulation more applicable and powerful, users can [**monitor**](../tutorial_toolbox/monitors.ipynb) variable trajectories and give [**inputs**](../tutorial_toolbox/inputs.ipynb) to target neuron groups. Here we monitor the ``spike`` variable in the ``E`` and ``I`` LIF model, which refers to the spking status of the neuron group, and give a constant input to both neuron groups. The time interval of numerical integration ``dt`` (with the default value of 0.1) can also be specified.\n",
     "\n",
     "After creating the runner, we can run a simulation by calling the runner:"
    ]
@@ -290,9 +290,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "brainpy",
+   "display_name": "Python [conda env:root] *",
    "language": "python",
-   "name": "brainpy"
+   "name": "conda-root-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -304,7 +304,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.11"
+   "version": "3.8.8"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,

docs/tutorial_simulation/network_models.ipynb

@@ -342,7 +342,7 @@
    "id": "84449872",
    "metadata": {},
    "source": [
-    " All elements are passed as ``**kwargs`` argument can be accessed by the provided keys. This will affect the following dynamics simualtion and will be discussed in greater detail in [tutorial of Monitors and Inputs](../tutorial_simulation/monitors_and_inputs.ipynb)."
+    "All elements are passed as ``**kwargs`` argument can be accessed by the provided keys. This will affect the following dynamics simualtion and will be discussed in greater detail in tutorial of [Runners](../tutorial_toolbox/runners.ipynb)."
    ]
   },
   {
@@ -466,13 +466,21 @@
    "id": "ee0ef0f9",
    "metadata": {},
    "source": [
-    "Above are some simulation examples showing the possible application of network models. The detailed description of dynamics simulation is covered in [Dynamics Simulation](../tutorial_simulation/index.rst), where the use of monitors and inputs will be expatiated."
+    "Above are some simulation examples showing the possible application of network models. The detailed description of dynamics simulation is covered in the toolboxes, where the use of [runners](../tutorial_toolbox/runners.ipynb), [monitors](../tutorial_toolbox/monitors.ipynb), and [inputs](../tutorial_toolbox/inputs.ipynb) will be expatiated."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d31c4afc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -486,7 +494,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.8.8"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,

docs/tutorial_simulation/neuron_models.ipynb

@@ -395,7 +395,7 @@
    "id": "f9d2604b",
    "metadata": {},
    "source": [
-    "The details of the model simulation will be expanded in the [Dynamics Simulation](../tutorial_simulation/index.rst) section. In brief, running any dynamical system instance should be accomplished with a runner, such like `brianpy.StructRunner` and `brainpy.ReportRunner`. In the runner, the variables want to monitor and the input crrents try to specify can be provided when initializing the runner. The details please see the tutorial of [Monitors and Inputs](../tutorial_simulation/monitors_and_inputs.ipynb). "
+    "The details of the model simulation will be expanded in the [Runners](../tutorial_toolbox/runners.ipynb) section. In brief, running any dynamical system instance should be accomplished with a runner, such like `brianpy.DSRunner` and `brainpy.ReportRunner`. The variables to be monitored and the input crrents to be applied in the simulation can be provided when initializing the runner. The details are accessible in [Monitors](../tutorial_toolbox/monitors.ipynb) and [Inputs](../tutorial_toolbox/inputs.ipynb). "
    ]
   },
   {
@@ -512,7 +512,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -526,7 +526,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.8.8"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
@@ -562,4 +562,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

docs/tutorial_simulation/synapse_models.ipynb

@@ -169,7 +169,7 @@
     "1. Constructor function ``__init__()``, in which three key arguments are needed.  \n",
     "  - `pre`: the pre-synaptic neural group. It should be an instance of `brainpy.dyn.NeuGroup`.\n",
     "  - `post`: the post-synaptic neural group. It should be an instance of `brainpy.dyn.NeuGroup`.\n",
-    "  - `conn` (optional): the connection type between these two groups. BrainPy has provided abundant connection types that are described in details in the [Synaptic Connections](./synaptic_connections.ipynb).\n",
+    "  - `conn` (optional): the connection type between these two groups. BrainPy has provided abundant connection types that are described in details in the [Synaptic Connections](../tutorial_toolbox/synaptic_connections.ipynb).\n",
     "2. Update function ``update(_t, _dt)`` describes the updating rule from the current time $\\mathrm{\\_t}$ to the next time $\\mathrm{\\_t + \\_dt}$. "
    ]
   },
@@ -385,7 +385,7 @@
    "id": "44fa4941",
    "metadata": {},
    "source": [
-    "More details of the connection structures please see the tutorial of [Synaptic Connections](./synaptic_connections.ipynb)."
+    "More details of the connection structures please see the tutorial of [Synaptic Connections](../tutorial_toolbox/synaptic_connections.ipynb)."
    ]
   },
   {
@@ -953,15 +953,15 @@
     "\n",
     "Imaging you want to connect 10,000 pre-synaptic neurons to 10,000 post-synaptic neurons with a 10% random connection probability. Using matrix, you need $10^8$ floats to save the synaptic state, and at each update step, you need do computation on $10^8$ floats. Actually, the number of synapses you really connect is only $10^7$. See, there is a huge memory waste and computing resource inefficiency. Moreover, at the given time $\\mathrm{\\_t}$, the number of pre-synaptic neurons in the spiking state is small on average. This means we have made many useless computations when defining synaptic computations with matrix-based connections (zeros dot connection matrix results in zeros).\n",
     "\n",
-    "Therefore, we need new ways to define synapse models. Specifically, we use vectors to store the connected neuron indices, like the ``pre_ids`` and ``post_ids`` (see [Synaptic Connections](./synaptic_connections.ipynb)). "
+    "Therefore, we need new ways to define synapse models. Specifically, we use vectors to store the connected neuron indices, like the ``pre_ids`` and ``post_ids`` (see [Synaptic Connections](../tutorial_toolbox/synaptic_connections.ipynb)). "
    ]
   },
   {
    "cell_type": "markdown",
    "id": "b67256b8",
    "metadata": {},
    "source": [
-    "In the below, we assume you have learned the synaptic connection types detailed in the tutorial of [Synaptic Connections](./synaptic_connections.ipynb)."
+    "In the below, we assume you have learned the synaptic connection types detailed in the tutorial of [Synaptic Connections](../tutorial_toolbox/synaptic_connections.ipynb)."
    ]
   },
   {
@@ -1233,7 +1233,7 @@
    "encoding": "# -*- coding: utf-8 -*-"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1247,7 +1247,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.8.8"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,