{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dc802e0d",
   "metadata": {},
   "source": [
    "# BrainPy-style point-neuron modeling with ``brainpy.state``\n",
    "\n",
    "[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/chaobrain/brainx/blob/main/docs/tutorials/brainpy_point_neuron_networks.ipynb)\n",
    "[![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/chaobrain/brainx/blob/main/docs/tutorials/brainpy_point_neuron_networks.ipynb)\n",
    "\n",
    "## What is `brainpy.state`?\n",
    "``brainpy.state`` is the point-neuron modeling package in the BrainX ecosystem. It is used to build spiking neural systems from stateful neurons, synapses, projections, inputs, and readouts. The package keeps neural dynamics explicit, works with physical units through ``brainunit``, and runs on top of JAX-compatible state transformations from ``brainstate``. \n",
    "\n",
    "For point-neuron networks, ``brainpy.state`` provides two complementary modeling routes:\n",
    "\n",
    "- [**BrainPy-style models**](https://brainx.chaobrain.com/brainpy-state/brainpy-style/index.html) use the native BrainX style. You compose neurons, synapses, projections, and output models directly, which makes the code close to the model anatomy and convenient for scalable, differentiable workflows.\n",
    "- [**NEST-compatible models**](https://brainx.chaobrain.com/brainpy-state/nest-style/index.html) keep a familiar NEST-style workflow for users who want to create nodes, connect populations and devices, simulate a time window, and record traces or spikes. \n",
    "\n",
    "For the full API reference, model catalogs, and additional examples across both routes, read the complete [brainpy.state documentation](https://brainx.chaobrain.com/brainpy-state/).\n",
    "\n",
    "This tutorial focuses on the **BrainPy-style modeling** : assembling networks from explicit, stateful parts and running them with unit-aware dynamics. The examples progress from a single ``LIFRef`` population to connected recurrent circuits, showing how neuron state, synaptic dynamics, connectivity, and output models work together. \n",
    "\n",
    "You will learn how to:\n",
    "\n",
    "- create and inspect a refractory leaky integrate-and-fire population with ``brainpy.state.LIFRef``;\n",
    "- drive stateful neurons through time with ``brainstate.transform.for_loop``;\n",
    "- read the four parts of a projection: ``comm``, ``syn``, ``out``, and ``post``;\n",
    "- understand why ``AlignPostProj`` is useful for convergent recurrent networks;\n",
    "- build and visualize a compact conductance-based excitatory/inhibitory network.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b758b5f",
   "metadata": {},
   "source": [
    "## 1. Setup and mental model\n",
    "\n",
    "Most BrainPy-style models are unit-aware. Set the global simulation time step before creating and running the model. The examples below use small populations so the states, shapes, and plots are easy to inspect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7ce5438e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-03T06:27:40.263963Z",
     "iopub.status.busy": "2026-07-03T06:27:40.263963Z",
     "iopub.status.idle": "2026-07-03T06:27:43.095986Z",
     "shell.execute_reply": "2026-07-03T06:27:43.095986Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dt = 0.1 ms\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import brainunit as u\n",
    "import brainstate\n",
    "import braintools\n",
    "import brainpy.state\n",
    "\n",
    "brainstate.environ.set(dt=0.1 * u.ms)\n",
    "brainstate.random.seed(0)\n",
    "\n",
    "dt = brainstate.environ.get_dt()\n",
    "print(\"dt =\", dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1ddd1f5",
   "metadata": {},
   "source": [
    "## 2. A first ``LIFRef`` population\n",
    "\n",
    "``brainpy.state.LIFRef`` is a refractory leaky integrate-and-fire neuron population. The population owns its dynamical variables as state: membrane potential ``V`` and refractory timing are stored inside the module, while calling the module once advances the dynamics by one time step.\n",
    "\n",
    "The constructor uses physical units from ``brainunit``. A threshold is in millivolts, a time constant is in milliseconds, and injected current is in milliamps. This makes the equations readable and catches many unit mistakes early."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef6297de",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-03T06:27:43.097501Z",
     "iopub.status.busy": "2026-07-03T06:27:43.097501Z",
     "iopub.status.idle": "2026-07-03T06:27:43.292039Z",
     "shell.execute_reply": "2026-07-03T06:27:43.292039Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LIFRef(\n",
       "  in_size=(16,),\n",
       "  out_size=(16,),\n",
       "  spk_reset=soft,\n",
       "  spk_fun=ReluGrad(alpha=0.3, width=1.0),\n",
       "  R=Quantity(1., \"ohm\"),\n",
       "  tau=Quantity(20., \"ms\"),\n",
       "  tau_ref=Quantity(5., \"ms\"),\n",
       "  V_th=Quantity(-50., \"mV\"),\n",
       "  V_rest=Quantity(-60., \"mV\"),\n",
       "  V_reset=Quantity(-60., \"mV\"),\n",
       "  V_initializer=Normal(mean=-60. mV, std=3. mV),\n",
       "  V=HiddenState(\n",
       "    value=Quantity(float32[16], \"mV\")\n",
       "  ),\n",
       "  last_spike_time=ShortTermState(\n",
       "    value=Quantity(~float32[16], \"ms\")\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_neurons = 16\n",
    "\n",
    "lif = brainpy.state.LIFRef(\n",
    "    num_neurons,\n",
    "    V_rest=-60.0 * u.mV,\n",
    "    V_th=-50.0 * u.mV,\n",
    "    V_reset=-60.0 * u.mV,\n",
    "    tau=20.0 * u.ms,\n",
    "    tau_ref=5.0 * u.ms,\n",
    "    V_initializer=braintools.init.Normal(-60.0 * u.mV, 3.0 * u.mV),\n",
    ")\n",
    "\n",
    "brainstate.nn.init_all_states(lif)\n",
    "lif"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fb2a5c5",
   "metadata": {},
   "source": [
    "## 3. Drive the population with ``for_loop``\n",
    "\n",
    "A model update is written as a normal Python function for one time step. ``brainstate.transform.for_loop`` lowers that step function over a time axis, carries the model state from one step to the next, and stacks the returned values.\n",
    "\n",
    "The per-step function sets the current simulation time with ``brainstate.environ.context(t=t)``. This is the standard pattern for time-dependent neural dynamics in ``brainpy.state``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a0f9eec4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-03T06:27:43.293042Z",
     "iopub.status.busy": "2026-07-03T06:27:43.293042Z",
     "iopub.status.idle": "2026-07-03T06:27:43.378118Z",
     "shell.execute_reply": "2026-07-03T06:27:43.378118Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "voltages: (600, 16)\n",
      "spikes: (600, 16)\n",
      "total spikes: 64.0\n"
     ]
    }
   ],
   "source": [
    "def run_lif_population(currents):\n",
    "    lif.reset_state()\n",
    "    times = u.math.arange(0.0 * u.ms, len(currents) * dt, dt)\n",
    "\n",
    "    def step(t, current):\n",
    "        with brainstate.environ.context(t=t):\n",
    "            spike = lif(current)\n",
    "            return lif.V.value, spike\n",
    "\n",
    "    voltages, spikes = brainstate.transform.for_loop(step, times, currents)\n",
    "    return times, voltages, spikes\n",
    "\n",
    "n_steps = 600\n",
    "constant_current = 25.0 * u.mA\n",
    "currents = np.ones(n_steps) * constant_current\n",
    "\n",
    "times, voltages, spikes = run_lif_population(currents)\n",
    "\n",
    "print(\"voltages:\", voltages.shape)\n",
    "print(\"spikes:\", spikes.shape)\n",
    "print(\"total spikes:\", float(u.math.sum(spikes)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4106f59e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-03T06:27:43.379121Z",
     "iopub.status.busy": "2026-07-03T06:27:43.379121Z",
     "iopub.status.idle": "2026-07-03T06:27:43.481868Z",
     "shell.execute_reply": "2026-07-03T06:27:43.481868Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_ms = times.to_decimal(u.ms)\n",
    "voltage_mV = voltages.to_decimal(u.mV)\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.plot(time_ms, voltage_mV[:, 0], label=\"neuron 0\")\n",
    "plt.plot(time_ms, voltage_mV[:, 1], label=\"neuron 1\", alpha=0.8)\n",
    "plt.axhline(-50.0, color=\"tab:red\", ls=\"--\", lw=0.8, label=\"threshold\")\n",
    "plt.xlabel(\"time (ms)\")\n",
    "plt.ylabel(\"membrane potential (mV)\")\n",
    "plt.title(\"LIFRef membrane potentials under constant current\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ba7a99e",
   "metadata": {},
   "source": [
    "## 4. Projection anatomy: ``comm``, ``syn``, ``out``, ``post``\n",
    "\n",
    "A projection connects a presynaptic event stream to a postsynaptic target. In BrainPy-style modeling, a projection is built from four explicit roles:\n",
    "\n",
    "- ``comm``: communication or connectivity, such as ``brainstate.nn.EventFixedProb``;\n",
    "- ``syn``: synaptic dynamics, such as ``brainpy.state.Expon``;\n",
    "- ``out``: output conversion, such as ``brainpy.state.COBA`` for conductance-based output or ``brainpy.state.CUBA`` for current-based output;\n",
    "- ``post``: the postsynaptic population that receives the current.\n",
    "\n",
    "This decomposition lets you swap connectivity, synapse kinetics, and output biophysics independently. It also makes the central memory question explicit: where should synaptic state live?\n",
    "\n",
    "A naive simulator can store one dynamical variable per realized synapse. For sparse recurrent networks, that means memory grows with the number of connected pairs. ``brainpy.state`` avoids this by aligning synaptic state to a neuron dimension instead of a connection dimension. The temporal state is stored either on the presynaptic side or on the postsynaptic side, while the projection still computes the same drive.\n",
    "\n",
    "![AlignPre and AlignPost projection design](https://brainx.chaobrain.com/brainpy-state/_images/alignpre-alignpost.png)\n",
    "\n",
    "Figure: **AlignPre vs AlignPost.** The synapse-dynamics block sits before the connection matrix on the left (state aligned to the presynaptic population, memory ``O(N_pre)``, natural for one-to-many communication) and after the connection matrix on the right (state aligned to the postsynaptic population, memory ``O(N_post)``, natural for many-to-one fan-in with exponential-family synapses). Orange indicates presynaptic activity, blue indicates postsynaptic activity, and green indicates communication through the connectivity matrix.\n",
    "\n",
    "**``AlignPre`` aligns synaptic state with the presynaptic population.** Each presynaptic neuron maintains its own trace before communication distributes it to targets. This is useful when one source is reused across many targets, or when the synapse model is naturally updated before communication.\n",
    "\n",
    "**``AlignPost`` aligns synaptic state with the postsynaptic population.** Presynaptic events are first communicated and accumulated for each target. The synapse dynamics then evolve on the postsynaptic side. For exponential-family synapses, incoming events can be merged before the state update, so the result is exact while memory scales with the target population size.\n",
    "\n",
    "The E/I network below uses ``AlignPostProj`` because excitatory and inhibitory presynaptic slices both converge back onto the same LIFRef population."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a38cb88e",
   "metadata": {},
   "source": [
    "## 5. Build a compact conductance-based E/I network\n",
    "\n",
    "The network below extends the same BrainPy-style pattern to a compact recurrent circuit: one mixed ``LIFRef`` population, one excitatory recurrent projection, and one inhibitory recurrent projection.\n",
    "\n",
    "The projections are conductance-based. Excitatory input uses ``COBA(E=0 mV)`` and inhibitory input uses ``COBA(E=-80 mV)``. Each projection has its own exponential synapse time constant and connectivity pattern."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "24acb564",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-03T06:27:43.482878Z",
     "iopub.status.busy": "2026-07-03T06:27:43.482878Z",
     "iopub.status.idle": "2026-07-03T06:27:43.487319Z",
     "shell.execute_reply": "2026-07-03T06:27:43.487319Z"
    }
   },
   "outputs": [],
   "source": [
    "class EINet(brainstate.nn.Module):\n",
    "    \"\"\"A compact conductance-based recurrent E/I network.\"\"\"\n",
    "\n",
    "    def __init__(self, n_exc, n_inh, prob, exc_weight, inh_weight):\n",
    "        super().__init__()\n",
    "        self.n_exc = n_exc\n",
    "        self.n_inh = n_inh\n",
    "        self.num = n_exc + n_inh\n",
    "\n",
    "        self.neurons = brainpy.state.LIFRef(\n",
    "            self.num,\n",
    "            V_rest=-60.0 * u.mV,\n",
    "            V_th=-50.0 * u.mV,\n",
    "            V_reset=-60.0 * u.mV,\n",
    "            tau=20.0 * u.ms,\n",
    "            tau_ref=5.0 * u.ms,\n",
    "            V_initializer=braintools.init.Normal(-55.0 * u.mV, 2.0 * u.mV),\n",
    "        )\n",
    "\n",
    "        self.exc = brainpy.state.AlignPostProj(\n",
    "            comm=brainstate.nn.EventFixedProb(n_exc, self.num, prob, exc_weight),\n",
    "            syn=brainpy.state.Expon.desc(self.num, tau=5.0 * u.ms),\n",
    "            out=brainpy.state.COBA.desc(E=0.0 * u.mV),\n",
    "            post=self.neurons,\n",
    "        )\n",
    "        self.inh = brainpy.state.AlignPostProj(\n",
    "            comm=brainstate.nn.EventFixedProb(n_inh, self.num, prob, inh_weight),\n",
    "            syn=brainpy.state.Expon.desc(self.num, tau=10.0 * u.ms),\n",
    "            out=brainpy.state.COBA.desc(E=-80.0 * u.mV),\n",
    "            post=self.neurons,\n",
    "        )\n",
    "\n",
    "    def update(self, t, background_current):\n",
    "        with brainstate.environ.context(t=t):\n",
    "            previous_spikes = self.neurons.get_spike() != 0.0\n",
    "            self.exc(previous_spikes[:self.n_exc])\n",
    "            self.inh(previous_spikes[self.n_exc:])\n",
    "            self.neurons(background_current)\n",
    "            return self.neurons.get_spike()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1be5e84d",
   "metadata": {},
   "source": [
    "## 6. Initialize and inspect the network\n",
    "\n",
    "State allocation is explicit. ``brainstate.nn.init_all_states`` walks the module tree and creates the dynamical state variables for the neuron population and synapses. The connection weights are conductances, so both excitatory and inhibitory projection weights are positive; the sign of their effect comes from the reversal potential in ``COBA``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "efd7035d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-03T06:27:43.488322Z",
     "iopub.status.busy": "2026-07-03T06:27:43.488322Z",
     "iopub.status.idle": "2026-07-03T06:27:43.618149Z",
     "shell.execute_reply": "2026-07-03T06:27:43.618149Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EINet(\n",
       "  n_exc=320,\n",
       "  n_inh=80,\n",
       "  num=400,\n",
       "  neurons=LIFRef(\n",
       "    in_size=(400,),\n",
       "    out_size=(400,),\n",
       "    before_updates={\n",
       "      \"(<class 'brainpy.state.Expon'>, (400,), {'tau': '5. ms'}) // (<class 'brainpy.state.COBA'>, (), {'E': '0. mV'})\": _AlignPost(\n",
       "        syn=Expon(\n",
       "          in_size=(400,),\n",
       "          out_size=(400,),\n",
       "          tau=Quantity(5., \"ms\"),\n",
       "          g_initializer=Constant(value=0. mS),\n",
       "          g=HiddenState(\n",
       "            value=Quantity(~float32[400], \"mS\")\n",
       "          )\n",
       "        ),\n",
       "        out=COBA(\n",
       "          E=Quantity(0., \"mV\")\n",
       "        )\n",
       "      ),\n",
       "      \"(<class 'brainpy.state.Expon'>, (400,), {'tau': '10. ms'}) // (<class 'brainpy.state.COBA'>, (), {'E': '-80. mV'})\": _AlignPost(\n",
       "        syn=Expon(\n",
       "          in_size=(400,),\n",
       "          out_size=(400,),\n",
       "          tau=Quantity(10., \"ms\"),\n",
       "          g_initializer=Constant(value=0. mS),\n",
       "          g=HiddenState(\n",
       "            value=Quantity(~float32[400], \"mS\")\n",
       "          )\n",
       "        ),\n",
       "        out=COBA(\n",
       "          E=Quantity(-80., \"mV\")\n",
       "        )\n",
       "      )\n",
       "    },\n",
       "    current_inputs={\n",
       "      'AlignPostProj2': COBA(...),\n",
       "      'AlignPostProj3': COBA(...)\n",
       "    },\n",
       "    spk_reset=soft,\n",
       "    spk_fun=ReluGrad(alpha=0.3, width=1.0),\n",
       "    R=Quantity(1., \"ohm\"),\n",
       "    tau=Quantity(20., \"ms\"),\n",
       "    tau_ref=Quantity(5., \"ms\"),\n",
       "    V_th=Quantity(-50., \"mV\"),\n",
       "    V_rest=Quantity(-60., \"mV\"),\n",
       "    V_reset=Quantity(-60., \"mV\"),\n",
       "    V_initializer=Normal(mean=-55. mV, std=2. mV),\n",
       "    V=HiddenState(\n",
       "      value=Quantity(float32[400], \"mV\")\n",
       "    ),\n",
       "    last_spike_time=ShortTermState(\n",
       "      value=Quantity(~float32[400], \"ms\")\n",
       "    )\n",
       "  ),\n",
       "  exc=AlignPostProj(\n",
       "    name=AlignPostProj2,\n",
       "    modules=(),\n",
       "    merging=True,\n",
       "    comm=EventFixedNumConn(\n",
       "      in_size=(320,),\n",
       "      out_size=(400,),\n",
       "      efferent_target=post,\n",
       "      conn_num=8,\n",
       "      allow_multi_conn=True,\n",
       "      weight=ParamState(\n",
       "        value=Quantity(~float32[], \"mS\")\n",
       "      ),\n",
       "      conn=FixedNumPerPre(float32[320, 400], nse=2560)\n",
       "    ),\n",
       "    syn=Expon(...),\n",
       "    out=COBA(...),\n",
       "    post=LIFRef(...)\n",
       "  ),\n",
       "  inh=AlignPostProj(\n",
       "    name=AlignPostProj3,\n",
       "    modules=(),\n",
       "    merging=True,\n",
       "    comm=EventFixedNumConn(\n",
       "      in_size=(80,),\n",
       "      out_size=(400,),\n",
       "      efferent_target=post,\n",
       "      conn_num=8,\n",
       "      allow_multi_conn=True,\n",
       "      weight=ParamState(\n",
       "        value=Quantity(~float32[], \"mS\")\n",
       "      ),\n",
       "      conn=FixedNumPerPre(float32[80, 400], nse=640)\n",
       "    ),\n",
       "    syn=Expon(...),\n",
       "    out=COBA(...),\n",
       "    post=LIFRef(...)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_exc = 320\n",
    "n_inh = 80\n",
    "prob = 0.02\n",
    "\n",
    "exc_weight = 0.6 * u.mS\n",
    "inh_weight = 6.7 * u.mS\n",
    "background_current = 20.0 * u.mA\n",
    "\n",
    "net = EINet(n_exc, n_inh, prob, exc_weight, inh_weight)\n",
    "brainstate.nn.init_all_states(net)\n",
    "net"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcbb828b",
   "metadata": {},
   "source": [
    "## 7. Run and visualize the recurrent simulation\n",
    "\n",
    "Each time step uses spikes from the previous step to update recurrent synaptic input, then advances the neuron population. ``for_loop`` returns a time-by-neuron spike matrix that can be plotted as a raster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "80aee783",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-03T06:27:43.619152Z",
     "iopub.status.busy": "2026-07-03T06:27:43.619152Z",
     "iopub.status.idle": "2026-07-03T06:27:44.202632Z",
     "shell.execute_reply": "2026-07-03T06:27:44.202632Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b70adaec92304ff5b72828fafcea3e65",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spike_history: (3000, 400)\n",
      "total spikes: 6400.0\n"
     ]
    }
   ],
   "source": [
    "duration = 300.0 * u.ms\n",
    "times = u.math.arange(0.0 * u.ms, duration, brainstate.environ.get_dt())\n",
    "\n",
    "spike_history = brainstate.transform.for_loop(\n",
    "    lambda t: net.update(t, background_current),\n",
    "    times,\n",
    "    pbar=brainstate.transform.ProgressBar(20),\n",
    ")\n",
    "\n",
    "print(\"spike_history:\", spike_history.shape)\n",
    "print(\"total spikes:\", float(u.math.sum(spike_history)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "15f848ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-03T06:27:44.203635Z",
     "iopub.status.busy": "2026-07-03T06:27:44.203635Z",
     "iopub.status.idle": "2026-07-03T06:27:44.907756Z",
     "shell.execute_reply": "2026-07-03T06:27:44.907756Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_indices, neuron_indices = u.math.where(spike_history)\n",
    "time_ms = times.to_decimal(u.ms)\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.scatter(time_ms[t_indices], neuron_indices, s=1, color=\"black\")\n",
    "plt.axhline(n_exc - 0.5, color=\"tab:red\", lw=0.8, label=\"E/I boundary\")\n",
    "plt.xlabel(\"time (ms)\")\n",
    "plt.ylabel(\"neuron index\")\n",
    "plt.title(\"Spike raster from a BrainPy-style E/I network\")\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f940b45d",
   "metadata": {},
   "source": [
    "## Troubleshooting\n",
    "\n",
    "- **No spikes:** increase ``background_current`` or simulate for a longer duration.\n",
    "- **Runaway activity:** reduce ``exc_weight``, strengthen inhibition, or shorten the excitatory synaptic time constant.\n",
    "- **Shape errors:** make sure ``EventFixedProb(n_pre, n_post, ...)`` matches the presynaptic slice and postsynaptic population. In this tutorial, the synaptic state uses ``self.num`` because ``AlignPostProj`` aligns it to the target population.\n",
    "- **Unit errors:** keep time constants in ``u.ms``, voltages in ``u.mV``, currents in ``u.mA``, and conductance weights in ``u.mS``.\n",
    "- **Repeated independent runs:** call ``brainstate.nn.init_all_states(model)`` before a new run, or call the relevant ``reset_state`` method when you want to reuse the same module."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88e74bb2",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "This tutorial introduced the essential BrainPy-style workflow for point-neuron modeling with ``brainpy.state``. You created a refractory LIF population, advanced stateful neural dynamics with ``brainstate.transform.for_loop``, examined how projections are organized into ``comm``, ``syn``, ``out``, and ``post`` components, and built a compact conductance-based excitatory/inhibitory recurrent network.\n",
    "\n",
    "These examples are meant to provide a concise starting point. For more detailed content, see the complete [``brainpy.state`` documentation](https://brainx.chaobrain.com/brainpy-state/).\n"
   ]
  }
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