{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **KNOWN ISSUE (2026-05).** This notebook does not currently execute end-to-end.\n",
    "> Cells that build `AlignPostProj` with a `COBA` output fail inside `saiunit.exprel`,\n",
    "> which now requires a `unit_to_scale=` argument. The breakage is in the in-flight\n",
    "> `brainpy_state`/`brainstate`/`saiunit` API refactor, not in the notebook content.\n",
    "> Tracking this as a follow-up task; the notebook will be refreshed once the upstream\n",
    "> API stabilises. Use the [BrainPy-style modeling guide](index.rst) entry points\n",
    "> and the `examples/` directory in the meantime.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Synapses\n",
    "\n",
    "Synapses model the temporal dynamics of neural connections in `brainpy.state`. This document explains\n",
    "how synapses work, what models are available, and how to use them effectively.\n",
    "\n",
    "## Overview\n",
    "\n",
    "Synapses provide temporal filtering of spike trains, transforming discrete spikes into continuous currents or conductances. They model:\n",
    "\n",
    "- **Postsynaptic potentials** (PSPs)\n",
    "- **Temporal integration** of spike trains\n",
    "- **Synaptic dynamics** (rise and decay)\n",
    "\n",
    "In BrainPy's architecture, synapses are part of the projection system:"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "```text\n",
    "Spikes → [Connectivity] → [Synapse] → [Output] → Neurons\n",
    "                              ↑\n",
    "                      Temporal filtering\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Usage\n",
    "\n",
    "### Creating Synapses\n",
    "\n",
    "Synapses are typically created as part of projections:"
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-13T09:44:12.956284Z",
     "start_time": "2025-11-13T09:44:07.906351Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import brainstate\n",
    "import braintools\n",
    "import saiunit as u\n",
    "import jax.numpy as jnp\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import brainpy.state\n",
    "\n",
    "# Set simulation timestep (required by brainstate.environ)\n",
    "brainstate.environ.set(dt=0.1 * u.ms)\n"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-13T09:44:12.985015Z",
     "start_time": "2025-11-13T09:44:12.956284Z"
    }
   },
   "source": [
    "# Create neurons for demonstration\n",
    "neurons = brainpy.state.LIF(50, V_rest=-65 * u.mV, V_th=-50 * u.mV, tau=10 * u.ms)\n",
    "\n",
    "# Create synapse descriptor\n",
    "syn = brainpy.state.Expon(\n",
    "    in_size=100,  # Number of synapses\n",
    "    tau=5. * u.ms  # Time constant\n",
    ")\n",
    "\n",
    "# Use in projection\n",
    "projection = brainpy.state.AlignPostProj(\n",
    "    comm=brainstate.nn.EventFixedProb(100, 50, 0.1, 0.5),\n",
    "    syn=syn,  # Synapse here\n",
    "    out=brainpy.state.CUBA(),\n",
    "    post=neurons\n",
    ")"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Synapse Lifecycle\n",
    "\n",
    "1. **Creation**: Define synapse with `()` method\n",
    "2. **Integration**: Include in projection\n",
    "3. **Update**: Called automatically by projection\n",
    "4. **Access**: Read synaptic variables as needed"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-13T09:56:55.823289Z",
     "start_time": "2025-11-13T09:56:55.338315Z"
    }
   },
   "source": [
    "# Example presynaptic spikes\n",
    "presynaptic_spikes = jnp.zeros(100)  # 100 presynaptic neurons\n",
    "\n",
    "projection = brainpy.state.AlignPostProj(\n",
    "    comm=brainstate.nn.AllToAll(100, 100, braintools.init.KaimingNormal(unit=u.mS)),\n",
    "    syn=brainpy.state.Expon(100, tau=5.0),\n",
    "    out=brainpy.state.COBA(E=0),\n",
    "    post=neurons,\n",
    ")\n",
    "brainstate.nn.init_all_states(projection)\n",
    "\n",
    "# During simulation\n",
    "projection(presynaptic_spikes)  # Updates synapse internally\n",
    "\n",
    "# Access synaptic variable\n",
    "synaptic_current = projection.syn"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Available Synapse Models\n",
    "\n",
    "For more synapse models, see the [API reference](../api/index).\n",
    "\n",
    "### Expon (Single Exponential)\n",
    "\n",
    "The simplest and most commonly used synapse model.\n",
    "\n",
    "**Mathematical Model:**\n",
    "\n",
    "\n",
    "$$\n",
    "\\tau \\frac{dg}{dt} = -g\n",
    "$$\n",
    "When spike arrives: \n",
    "$g \\leftarrow g + 1$\n",
    "\n",
    "**Impulse Response:**\n",
    "\n",
    "\n",
    "$$\n",
    "g(t) = \\exp(-t/\\tau)\n",
    "$$\n",
    "**Example:**"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-13T09:57:06.489948Z",
     "start_time": "2025-11-13T09:57:06.486122Z"
    }
   },
   "source": [
    "syn = brainpy.state.Expon(\n",
    "    in_size=100,\n",
    "    tau=5. * u.ms,\n",
    "    g_initializer=braintools.init.Constant(0. * u.mS)\n",
    ")"
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Parameters:**\n",
    "\n",
    "- `size`: Number of synapses\n",
    "- `tau`: Decay time constant\n",
    "- `g_initializer`: Initial synaptic variable (optional)\n",
    "\n",
    "**Key Features:**\n",
    "\n",
    "- Single time constant\n",
    "- Fast computation\n",
    "- Instantaneous rise\n",
    "\n",
    "**Use cases:**\n",
    "\n",
    "- General-purpose modeling\n",
    "- Fast simulations\n",
    "- When precise kinetics are not critical\n",
    "\n",
    "**Behavior:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Response to single spike at t=0\n",
    "# g(t) = exp(-t/τ)\n",
    "# Fast rise, exponential decay"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alpha Synapse\n",
    "\n",
    "A more realistic model with non-instantaneous rise time.\n",
    "\n",
    "**Mathematical Model:**\n",
    "\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\tau \\frac{dh}{dt} &= -h \\\\\n",
    "\\tau \\frac{dg}{dt} &= -g + h\n",
    "\\end{aligned}\n",
    "$$\n",
    "When spike arrives: $ h\\leftarrow h + 1 $\n",
    "\n",
    "**Impulse Response:**\n",
    "\n",
    "$$\n",
    "g(t) = \\frac{t}{\\tau}\\exp(-t/\\tau)\n",
    "$$\n",
    "**Example:**"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-13T09:57:12.935057Z",
     "start_time": "2025-11-13T09:57:12.929863Z"
    }
   },
   "source": [
    "syn = brainpy.state.Alpha(\n",
    "    in_size=100,\n",
    "    tau=5. * u.ms,\n",
    "    g_initializer=braintools.init.Constant(0. * u.mS)\n",
    ")"
   ],
   "outputs": [],
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Parameters:**\n",
    "\n",
    "Same as ``Expon``, but produces alpha-shaped response.\n",
    "\n",
    "**Key Features:**\n",
    "\n",
    "- Smooth rise and fall\n",
    "- Biologically realistic\n",
    "- Peak at t = τ\n",
    "\n",
    "**Use cases:**\n",
    "\n",
    "- Biological realism\n",
    "- Detailed cortical modeling\n",
    "- When kinetics matter\n",
    "\n",
    "**Behavior:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Response to single spike at t=0\n",
    "# g(t) = (t/τ) * exp(-t/τ)\n",
    "# Gradual rise to peak at τ, then decay"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Synaptic Variables\n",
    "\n",
    "### The Descriptor Pattern\n",
    "\n",
    "BrainPy synapses use a descriptor pattern:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-13T09:58:19.442492Z",
     "start_time": "2025-11-13T09:58:19.435903Z"
    }
   },
   "source": [
    "# Define neurons for example\n",
    "example_neurons = brainpy.state.LIF(100, V_rest=-65 * u.mV, V_th=-50 * u.mV, tau=10 * u.ms)\n",
    "\n",
    "# Instantiated within projection\n",
    "example_projection = brainpy.state.AlignPostProj(\n",
    "    comm=brainstate.nn.EventFixedProb(100, 100, 0.1, 0.5 * u.mS),\n",
    "    syn=brainpy.state.Expon(in_size=100, tau=5 * u.ms),\n",
    "    out=brainpy.state.CUBA(),\n",
    "    post=example_neurons\n",
    ")\n",
    "brainstate.nn.init_all_states(example_projection)\n",
    "\n",
    "# Access instantiated synapse\n",
    "actual_synapse = example_projection.syn\n",
    "g_value = actual_synapse"
   ],
   "outputs": [],
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#",
    "#",
    "#",
    " ",
    "A",
    "c",
    "c",
    "e",
    "s",
    "s",
    "i",
    "n",
    "g",
    " ",
    "S",
    "y",
    "n",
    "a",
    "p",
    "t",
    "i",
    "c",
    " ",
    "S",
    "t",
    "a",
    "t",
    "e"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-13T09:58:32.435703Z",
     "start_time": "2025-11-13T09:58:32.429353Z"
    }
   },
   "source": [
    "# Define neurons for this example\n",
    "demo_neurons = brainpy.state.LIF(100, V_rest=-65 * u.mV, V_th=-50 * u.mV, tau=10 * u.ms)\n",
    "\n",
    "# Within projection\n",
    "demo_projection = brainpy.state.AlignPostProj(\n",
    "    comm=brainstate.nn.EventFixedProb(100, 100, conn_num=10, conn_weight=0.5 * u.mS),\n",
    "    syn=brainpy.state.Expon(in_size=100, tau=5 * u.ms),\n",
    "    out=brainpy.state.CUBA(),\n",
    "    post=demo_neurons\n",
    ")\n",
    "\n",
    "# Initialize states\n",
    "brainstate.nn.init_all_states(demo_projection)\n",
    "\n",
    "# Access the synaptic conductance state\n",
    "synaptic_var = demo_projection.syn.g.value  # Current value with units\n",
    "\n",
    "# Convert to array for plotting\n",
    "g_array = u.get_magnitude(synaptic_var)"
   ],
   "outputs": [],
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#",
    "#",
    " ",
    "S",
    "y",
    "n",
    "a",
    "p",
    "t",
    "i",
    "c",
    " ",
    "D",
    "y",
    "n",
    "a",
    "m",
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    "s",
    " ",
    "V",
    "i",
    "s",
    "u",
    "a",
    "l",
    "i",
    "z",
    "a",
    "t",
    "i",
    "o",
    "n",
    "\n"
   ]
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  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-13T10:06:26.322483Z",
     "start_time": "2025-11-13T10:06:25.930516Z"
    }
   },
   "source": [
    "# Set simulation timestep\n",
    "brainstate.environ.set(dt=0.1 * u.ms)\n",
    "\n",
    "# Create different synapses (without unit initializers to avoid mismatch)\n",
    "expon = brainpy.state.Expon(1, tau=5 * u.ms)\n",
    "alpha = brainpy.state.Alpha(1, tau=5 * u.ms)\n",
    "ampa = brainpy.state.AMPA(1, T=2 * u.mM)\n",
    "gaba = brainpy.state.GABAa(1, T=1. * u.mM)\n",
    "\n",
    "# Initialize\n",
    "for syn in [expon, alpha, ampa, gaba]:\n",
    "    brainstate.nn.init_all_states(syn)\n",
    "\n",
    "# Single spike at t=0 (dimensionless spike count)\n",
    "spike_input = jnp.zeros(100) * u.mS\n",
    "spike_input = spike_input.at[0].set(1.0 * u.mS)\n",
    "\n",
    "# Simulate\n",
    "times = u.math.arange(0 * u.ms, 50 * u.ms, 0.1 * u.ms)\n",
    "responses = {\n",
    "    'Expon': [],\n",
    "    'Alpha': [],\n",
    "    'AMPA': [],\n",
    "    'GABAa': []\n",
    "}\n",
    "\n",
    "for syn, name in zip([expon, alpha], ['Expon', 'Alpha']):\n",
    "    brainstate.nn.init_all_states(syn)\n",
    "\n",
    "\n",
    "    def step_run(i, t):\n",
    "        with brainstate.environ.context(t=t, i=i):\n",
    "            inp = u.math.where(i == 0, 1.0 * u.mS, 0.0 * u.mS)\n",
    "            g_val = syn(inp)\n",
    "        return g_val\n",
    "\n",
    "\n",
    "    responses[name] = brainstate.transform.for_loop(\n",
    "        step_run, u.math.arange(times.size), times,\n",
    "    )\n",
    "\n",
    "for syn, name in zip([ampa, gaba], ['AMPA', 'GABAa']):\n",
    "    brainstate.nn.init_all_states(syn)\n",
    "\n",
    "\n",
    "    def step_run(i, t):\n",
    "        with brainstate.environ.context(t=t, i=i):\n",
    "            inp = u.math.where(i == 0, 1.0, 0.0)\n",
    "            g_val = syn(inp)\n",
    "        return g_val\n",
    "\n",
    "\n",
    "    responses[name] = brainstate.transform.for_loop(\n",
    "        step_run, u.math.arange(times.size), times,\n",
    "    )\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "for name, response in responses.items():\n",
    "    plt.plot(times, response, label=name, linewidth=2)\n",
    "\n",
    "plt.xlabel('Time (ms)')\n",
    "plt.ylabel('Synaptic Variable (normalized)')\n",
    "plt.title('Comparison of Synapse Models (Single Spike)')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 18
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Ecosystem-py",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}