{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fbd54f89",
   "metadata": {},
   "source": [
    "# How to add short-term plasticity\n",
    "\n",
    "**Task.** Model use-dependent synapses whose efficacy changes on a fast\n",
    "timescale — short-term facilitation (`STP`) and short-term depression (`STD`).\n",
    "\n",
    "**Audience.** Simulation. Assumes {doc}`../tutorials/02-synapse-and-projection`.\n",
    "\n",
    "Short-term plasticity makes a synapse's strength depend on its recent activity.\n",
    "`brainpy.state` provides two models you can drive directly:\n",
    "\n",
    "- **`STP`** — the Tsodyks–Markram model with both facilitation and depression,\n",
    "  tracking a utilization variable `u` and a resources variable `x`. The\n",
    "  effective release is proportional to `u · x`.\n",
    "- **`STD`** — pure depression, tracking the resources `x`.\n",
    "\n",
    "Here we examine their dynamics in isolation: drive each with a regular spike\n",
    "train and watch the internal variables and the resulting efficacy evolve. This\n",
    "is the clearest way to understand what each model does before wiring it into a\n",
    "projection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c729a239",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-17T09:08:26.551741Z",
     "iopub.status.busy": "2026-06-17T09:08:26.551442Z",
     "iopub.status.idle": "2026-06-17T09:08:30.640014Z",
     "shell.execute_reply": "2026-06-17T09:08:30.638545Z"
    }
   },
   "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"
     ]
    }
   ],
   "source": [
    "import brainpy\n",
    "import brainstate\n",
    "import braintools\n",
    "import brainunit as u\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2f61f92",
   "metadata": {},
   "source": [
    "## A regular presynaptic spike train\n",
    "\n",
    "We build a 20 Hz spike train over 400 ms (one spike every 50 ms)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dfcd70d6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-17T09:08:30.642144Z",
     "iopub.status.busy": "2026-06-17T09:08:30.641715Z",
     "iopub.status.idle": "2026-06-17T09:08:33.409751Z",
     "shell.execute_reply": "2026-06-17T09:08:33.408766Z"
    }
   },
   "outputs": [],
   "source": [
    "with brainstate.environ.context(dt=0.1 * u.ms):\n",
    "    times = u.math.arange(0. * u.ms, 400. * u.ms, brainstate.environ.get_dt())\n",
    "    t_ms = times.to_decimal(u.ms)\n",
    "    # a spike whenever the time is (almost) a multiple of 50 ms\n",
    "    spike_train = (u.math.asarray((t_ms % 50.0) < 0.05)).astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11dd5a0e",
   "metadata": {},
   "source": [
    "## Short-term facilitation + depression (`STP`)\n",
    "\n",
    "`STP.update(pre_spike)` advances `u` and `x` and returns the modulated efficacy.\n",
    "With a long facilitation time constant, `u` ramps up across the train while `x`\n",
    "is drawn down — the product `u·x` shows the net synaptic efficacy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "13dc3130",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-17T09:08:33.412387Z",
     "iopub.status.busy": "2026-06-17T09:08:33.411915Z",
     "iopub.status.idle": "2026-06-17T09:08:33.669897Z",
     "shell.execute_reply": "2026-06-17T09:08:33.669014Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 700x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with brainstate.environ.context(dt=0.1 * u.ms):\n",
    "    stp = brainpy.state.STP(1, U=0.2, tau_f=1500. * u.ms, tau_d=200. * u.ms)\n",
    "    brainstate.nn.init_all_states(stp)\n",
    "\n",
    "    def step_stp(sp):\n",
    "        eff = stp(sp)\n",
    "        return eff, stp.u.value, stp.x.value\n",
    "\n",
    "    eff_stp, u_var, x_var = brainstate.transform.for_loop(step_stp, spike_train)\n",
    "\n",
    "fig, gs = braintools.visualize.get_figure(2, 1, 1.8, 7.0)\n",
    "ax = fig.add_subplot(gs[0, 0])\n",
    "ax.plot(t_ms, u_var[:, 0], label='u (facilitation)')\n",
    "ax.plot(t_ms, x_var[:, 0], label='x (resources)')\n",
    "ax.legend(loc='center right'); ax.set_ylabel('STP variables')\n",
    "ax = fig.add_subplot(gs[1, 0])\n",
    "ax.plot(t_ms, (u_var * x_var)[:, 0], color='k')\n",
    "ax.set_ylabel('efficacy u*x'); ax.set_xlabel('Time (ms)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "257ec378",
   "metadata": {},
   "source": [
    "## Short-term depression (`STD`)\n",
    "\n",
    "`STD` tracks only the resources `x`, which deplete with each spike and recover\n",
    "with time constant `tau`. Successive spikes in the train produce progressively\n",
    "weaker responses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cb970e8f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-17T09:08:33.671997Z",
     "iopub.status.busy": "2026-06-17T09:08:33.671763Z",
     "iopub.status.idle": "2026-06-17T09:08:33.786386Z",
     "shell.execute_reply": "2026-06-17T09:08:33.785526Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x220 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with brainstate.environ.context(dt=0.1 * u.ms):\n",
    "    std = brainpy.state.STD(1, tau=200. * u.ms, U=0.2)\n",
    "    brainstate.nn.init_all_states(std)\n",
    "\n",
    "    def step_std(sp):\n",
    "        eff = std(sp)\n",
    "        return eff, std.x.value\n",
    "\n",
    "    eff_std, x_std = brainstate.transform.for_loop(step_std, spike_train)\n",
    "\n",
    "plt.figure(figsize=(7, 2.2))\n",
    "plt.plot(t_ms, x_std[:, 0], color='k')\n",
    "plt.ylabel('x (resources)'); plt.xlabel('Time (ms)')\n",
    "plt.title('Short-term depression')\n",
    "plt.show()"
   ]
  },
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   "cell_type": "markdown",
   "id": "4053dcb5",
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   "source": [
    "## Using STP/STD in a projection\n",
    "\n",
    "To make a *projection* dynamic, the functional projection builders accept an\n",
    "`stp=` describer, e.g.::\n",
    "\n",
    "    brainpy.state.align_post_projection(\n",
    "        pre.prefetch('V'),\n",
    "        lambda v: pre.get_spike(v) != 0.,\n",
    "        comm=brainstate.nn.EventFixedProb(n_pre, n_post, 0.1, w),\n",
    "        syn=brainpy.state.Expon.desc(n_post, tau=5. * u.ms),\n",
    "        out=brainpy.state.COBA.desc(E=0. * u.mV),\n",
    "        post=post,\n",
    "        stp=brainpy.state.STP.desc(n_pre, U=0.2, tau_f=1500. * u.ms, tau_d=200. * u.ms),\n",
    "    )\n",
    "\n",
    "## See also\n",
    "\n",
    "- {doc}`/apis/brainpy-plasticity` — `STP` and `STD` reference.\n",
    "- {doc}`sim-delays` — add transmission delays to a projection.\n",
    "- {doc}`/concepts/alignpre-alignpost` — how projections route presynaptic state."
   ]
  }
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