{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9c4ceb9a",
   "metadata": {},
   "source": [
    "# Building a Spiking Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18529448",
   "metadata": {},
   "source": [
    "BrainState provides the *substrate* for brain modeling — `State`, `Module`, integrators, delays,\n",
    "and event-driven operators — but it deliberately ships few concrete neuron or synapse models.\n",
    "Those live in the companion package **`brainpy`**, whose `brainpy.state` API is built directly on\n",
    "BrainState. This is the intended cross-package workflow: import ready-made `LIF`, synapse, and\n",
    "projection models from `brainpy.state`, and wire them together as ordinary BrainState modules.\n",
    "\n",
    "We build a classic **excitatory–inhibitory (E/I) balanced network** one piece at a time, then run\n",
    "it and plot the resulting spike raster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bf56914b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-30T16:49:31.246009Z",
     "iopub.status.busy": "2026-05-30T16:49:31.245820Z",
     "iopub.status.idle": "2026-05-30T16:49:39.350714Z",
     "shell.execute_reply": "2026-05-30T16:49:39.349899Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'0.4.0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import brainunit as u\n",
    "import jax.numpy as jnp\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import brainstate\n",
    "import brainpy\n",
    "import braintools\n",
    "\n",
    "brainstate.random.seed(0)\n",
    "brainstate.environ.set(dt=0.1 * u.ms)\n",
    "brainstate.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8c9e711",
   "metadata": {},
   "source": [
    "## Step 1: a population of spiking neurons"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a50053c2",
   "metadata": {},
   "source": [
    "`brainpy.state.LIF` is a leaky integrate-and-fire population — a BrainState `Dynamics` module.\n",
    "All physical quantities carry units (via `brainunit`): potentials in millivolts, time constants\n",
    "in milliseconds. After `init_all_states`, the population is ready to be driven by an input\n",
    "current."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ce274125",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-30T16:49:39.353227Z",
     "iopub.status.busy": "2026-05-30T16:49:39.352793Z",
     "iopub.status.idle": "2026-05-30T16:49:40.294644Z",
     "shell.execute_reply": "2026-05-30T16:49:40.293965Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "membrane potential: [-59.84079742 -59.84079742 -59.84079742 -59.84079742] mV\n",
      "spikes this step  : [0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "neurons = brainpy.state.LIF(\n",
    "    4,\n",
    "    V_rest=-52. * u.mV,\n",
    "    V_th=-50. * u.mV,\n",
    "    V_reset=-60. * u.mV,\n",
    "    tau=10. * u.ms,\n",
    "    V_initializer=braintools.init.Constant(-60. * u.mV),\n",
    "    spk_reset='soft',\n",
    ")\n",
    "brainstate.nn.init_all_states(neurons)\n",
    "\n",
    "# Drive the neurons with a constant supra-threshold current for one step.\n",
    "with brainstate.environ.context(t=0. * u.ms):\n",
    "    spikes = neurons(8. * u.mA)\n",
    "print('membrane potential:', neurons.V.value)\n",
    "print('spikes this step  :', spikes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67112227",
   "metadata": {},
   "source": [
    "## Step 2: connecting populations with a projection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72f7a6c7",
   "metadata": {},
   "source": [
    "A **projection** carries spikes from a source population to a target. We assemble one from four\n",
    "composable parts:\n",
    "\n",
    "- a **communication** operator — `EventFixedProb`, the event-driven sparse connectivity from the\n",
    "  [previous tutorial](03_event_driven_operators.ipynb);\n",
    "- a **synapse** model — `Expon`, exponential conductance decay;\n",
    "- an **output** model — `CUBA`, current-based synaptic input;\n",
    "- the **postsynaptic** population.\n",
    "\n",
    "`brainpy.state.AlignPostProj` binds them together. The `.desc(...)` calls are deferred\n",
    "constructors that the projection instantiates against the target population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7550f603",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-30T16:49:40.297012Z",
     "iopub.status.busy": "2026-05-30T16:49:40.296741Z",
     "iopub.status.idle": "2026-05-30T16:49:40.315126Z",
     "shell.execute_reply": "2026-05-30T16:49:40.314054Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AlignPostProj connects a source onto (4,) neurons\n"
     ]
    }
   ],
   "source": [
    "projection = brainpy.state.AlignPostProj(\n",
    "    comm=brainstate.nn.EventFixedProb(4, 4, conn_num=0.5, conn_weight=1.0 * u.mS),\n",
    "    syn=brainpy.state.Expon.desc(4, tau=2. * u.ms),\n",
    "    out=brainpy.state.CUBA.desc(),\n",
    "    post=neurons,\n",
    ")\n",
    "print(type(projection).__name__, 'connects a source onto', neurons.varshape, 'neurons')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a5b047e",
   "metadata": {},
   "source": [
    "## Step 3: assembling the E/I network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eb67066",
   "metadata": {},
   "source": [
    "A balanced network has an excitatory and an inhibitory sub-population, each projecting onto the\n",
    "whole network. We wrap a single `LIF` population (the first `n_exc` units are excitatory, the rest\n",
    "inhibitory) and two projections in one `Module`. Its `update` reads the current spikes, routes\n",
    "them through the two projections, then advances the neurons by one step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2f0a57bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-30T16:49:40.317450Z",
     "iopub.status.busy": "2026-05-30T16:49:40.317201Z",
     "iopub.status.idle": "2026-05-30T16:49:40.322121Z",
     "shell.execute_reply": "2026-05-30T16:49:40.321608Z"
    }
   },
   "outputs": [],
   "source": [
    "class EINet(brainstate.nn.Module):\n",
    "    def __init__(self, n_exc, n_inh, prob, JE, JI):\n",
    "        super().__init__()\n",
    "        self.n_exc = n_exc\n",
    "        self.num = n_exc + n_inh\n",
    "        self.N = brainpy.state.LIF(\n",
    "            self.num,\n",
    "            V_rest=-52. * u.mV, V_th=-50. * u.mV, V_reset=-60. * u.mV,\n",
    "            tau=10. * u.ms,\n",
    "            V_initializer=braintools.init.Normal(-60., 10., unit=u.mV),\n",
    "            spk_reset='soft',\n",
    "        )\n",
    "        self.E = brainpy.state.AlignPostProj(\n",
    "            comm=brainstate.nn.EventFixedProb(n_exc, self.num, prob, JE),\n",
    "            syn=brainpy.state.Expon.desc(self.num, tau=2. * u.ms),\n",
    "            out=brainpy.state.CUBA.desc(), post=self.N,\n",
    "        )\n",
    "        self.I = brainpy.state.AlignPostProj(\n",
    "            comm=brainstate.nn.EventFixedProb(n_inh, self.num, prob, JI),\n",
    "            syn=brainpy.state.Expon.desc(self.num, tau=2. * u.ms),\n",
    "            out=brainpy.state.CUBA.desc(), post=self.N,\n",
    "        )\n",
    "\n",
    "    def update(self, inp):\n",
    "        spikes = self.N.get_spike() != 0.\n",
    "        self.E(spikes[:self.n_exc])     # excitatory spikes\n",
    "        self.I(spikes[self.n_exc:])     # inhibitory spikes\n",
    "        self.N(inp)                     # advance the neurons\n",
    "        return self.N.get_spike()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce305774",
   "metadata": {},
   "source": [
    "## Step 4: running the simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85829e85",
   "metadata": {},
   "source": [
    "We instantiate a 500-neuron network (400 excitatory, 100 inhibitory), initialise its state, and\n",
    "step it for 200 ms under a constant background current. `brainstate.transform.for_loop` runs the\n",
    "whole trajectory as a single compiled scan, collecting the spikes at every step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e3994793",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-30T16:49:40.324474Z",
     "iopub.status.busy": "2026-05-30T16:49:40.324212Z",
     "iopub.status.idle": "2026-05-30T16:49:41.677233Z",
     "shell.execute_reply": "2026-05-30T16:49:41.676435Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spike array shape (time, neurons): (2000, 500)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total spikes: 4356\n"
     ]
    }
   ],
   "source": [
    "n_exc, n_inh, prob = 400, 100, 0.1\n",
    "# Balanced weights scale as 1/sqrt(expected number of inputs).\n",
    "JE = 1. / u.math.sqrt(prob * n_exc) * u.mS\n",
    "JI = -1. / u.math.sqrt(prob * n_inh) * u.mS\n",
    "\n",
    "net = EINet(n_exc, n_inh, prob, JE, JI)\n",
    "brainstate.nn.init_all_states(net)\n",
    "\n",
    "times = u.math.arange(0. * u.ms, 200. * u.ms, brainstate.environ.get_dt())\n",
    "spikes = brainstate.transform.for_loop(lambda t: net.update(3. * u.mA), times)\n",
    "print('spike array shape (time, neurons):', spikes.shape)\n",
    "print('total spikes:', int(jnp.sum(spikes != 0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c07adc1",
   "metadata": {},
   "source": [
    "## Step 5: the spike raster"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "217e6a3f",
   "metadata": {},
   "source": [
    "A raster plot — neuron index versus spike time — reveals the asynchronous irregular firing\n",
    "characteristic of a balanced network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bc4f8b9e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-30T16:49:41.679205Z",
     "iopub.status.busy": "2026-05-30T16:49:41.678965Z",
     "iopub.status.idle": "2026-05-30T16:49:42.417689Z",
     "shell.execute_reply": "2026-05-30T16:49:42.417003Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_ms = times.to_decimal(u.ms)\n",
    "t_idx, n_idx = u.math.where(spikes != 0)\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.plot(t_ms[t_idx], n_idx, 'k.', markersize=1)\n",
    "plt.xlabel('time (ms)')\n",
    "plt.ylabel('neuron index')\n",
    "plt.title('E/I balanced network raster')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8984a5c7",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "- BrainState is the substrate; **`brainpy.state`** supplies concrete `LIF` neurons, `Expon`\n",
    "  synapses, `CUBA` outputs, and `AlignPostProj` projections, all as BrainState modules.\n",
    "- A projection composes a **communication** operator, a **synapse**, an **output**, and a\n",
    "  **postsynaptic** population — here using the event-driven `EventFixedProb`.\n",
    "- `init_all_states` prepares a network and `brainstate.transform.for_loop` runs the trajectory as\n",
    "  one compiled scan.\n",
    "\n",
    "### See also\n",
    "\n",
    "- [Training a spiking neural network](05_training_an_snn.ipynb) — making such a network learn with surrogate gradients.\n",
    "- [Event-driven operators](03_event_driven_operators.ipynb) — the connectivity used in the projections.\n",
    "- The [brain-dynamics gallery](../../examples/brain_dynamics/index.rst) — complete simulation and Hodgkin–Huxley examples."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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