{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 5: Synaptic Plasticity Modeling - Foundation of Learning and Memory\n",
    "\n",
    "Synaptic plasticity is the biological foundation of learning and memory in neural systems. The strength (weight) of synapses dynamically changes according to neural activity patterns, following specific learning rules.\n",
    "\n",
    "BrainEvent provides efficient implementations of synaptic plasticity, supporting various learning rules and data structures.\n",
    "\n",
    "## Contents\n",
    "1. Biological Background of Synaptic Plasticity\n",
    "2. STDP - Spike-Timing-Dependent Plasticity\n",
    "3. Implementing STDP on CSR Format\n",
    "4. Implementing STDP on COO Format\n",
    "5. Implementing STDP on Dense Matrices\n",
    "6. Practice: Building an Adaptive Network\n",
    "7. Advanced: Multiple Synaptic Plasticity Rules"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Biological Background of Synaptic Plasticity\n",
    "\n",
    "### Hebb's Rule\n",
    "> \"Neurons that fire together, wire together\" (Neurons that fire together, wire together)\n",
    "\n",
    "This is a learning principle proposed by Donald Hebb in 1949, and modern neuroscience has confirmed that synaptic plasticity indeed exists.\n",
    "\n",
    "### STDP (Spike-Timing-Dependent Plasticity)\n",
    "\n",
    "**Spike-Timing-Dependent Plasticity**is the most important synaptic plasticity mechanism:\n",
    "\n",
    "- 🧠 **LTP (Long-Term Potentiation)**: If the presynaptic neuron spike occurs **before** the postsynaptic neuron, the synapse is strengthened\n",
    "- 🧠 **LTD (Long-Term Depression)**: If the presynaptic neuron spike occurs **after** the postsynaptic neuron, the synapse is weakened\n",
    "\n",
    "### Mathematical Expression\n",
    "\n",
    "STDP weight update rule:\n",
    "\n",
    "$$\\Delta w = \\begin{cases}\n",
    "A_{+} \\cdot \\text{trace}_{post} & \\text{if pre-spike} \\\\\n",
    "A_{-} \\cdot \\text{trace}_{pre} & \\text{if post-spike}\n",
    "\\end{cases}$$\n",
    "\n",
    "where trace is the exponentially decaying spike trace:\n",
    "$$\\text{trace}(t) = \\text{trace}(t-dt) \\cdot e^{-dt/\\tau} + \\text{spike}(t)$$\n",
    "\n",
    "### Implementation in BrainEvent\n",
    "\n",
    "BrainEvent provides several core functions:\n",
    "- **`csr_on_pre`**: Presynaptic spike-triggered weight update (CSR format)\n",
    "- **`coo_on_pre`**: Presynaptic spike-triggered weight update (COO format)\n",
    "- **`dense_on_pre`**: Presynaptic spike-triggered weight update (dense format)\n",
    "- as well as corresponding `_on_post` versions"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-21T07:43:20.870616Z",
     "start_time": "2025-10-21T07:43:19.911740Z"
    }
   },
   "source": [
    "import brainevent\n",
    "import brainstate\n",
    "import braintools\n",
    "import jax.numpy as jnp\n",
    "import jax\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(f\"BrainEvent version: {brainevent.__version__}\")\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BrainEvent version: 0.0.4\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Basic STDP Implementation\n",
    "\n",
    "Let's first implement a simple STDP learning system."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-21T07:43:21.871537Z",
     "start_time": "2025-10-21T07:43:21.865920Z"
    }
   },
   "source": [
    "class STDPSystem:\n",
    "    \"\"\"System implementing STDP learning rules\"\"\"\n",
    "    \n",
    "    def __init__(self, \n",
    "                 tau_pre=20.0,    # Presynaptic trace time constant (ms)\n",
    "                 tau_post=20.0,   # Postsynaptic trace time constant (ms)\n",
    "                 A_plus=0.01,     # LTP learning rate\n",
    "                 A_minus=0.01,    # LTD learning rate\n",
    "                 dt=1.0):         # Time step (ms)\n",
    "        self.tau_pre = tau_pre\n",
    "        self.tau_post = tau_post\n",
    "        self.A_plus = A_plus\n",
    "        self.A_minus = A_minus\n",
    "        self.dt = dt\n",
    "        \n",
    "        # trace decay factor\n",
    "        self.decay_pre = np.exp(-dt / tau_pre)\n",
    "        self.decay_post = np.exp(-dt / tau_post)\n",
    "        \n",
    "        print(f\"STDP system configuration:\")\n",
    "        print(f\"  τ_pre:  {tau_pre} ms\")\n",
    "        print(f\"  τ_post: {tau_post} ms\")\n",
    "        print(f\"  A+:     {A_plus}\")\n",
    "        print(f\"  A-:     {A_minus}\")\n",
    "        print(f\"  dt:     {dt} ms\")\n",
    "    \n",
    "    def update_traces(self, pre_trace, post_trace, pre_spike, post_spike):\n",
    "        \"\"\"Update presynaptic and postsynaptic traces\"\"\"\n",
    "        # Exponential decay + spike increment\n",
    "        pre_trace = pre_trace * self.decay_pre + pre_spike.astype(jnp.float32)\n",
    "        post_trace = post_trace * self.decay_post + post_spike.astype(jnp.float32)\n",
    "        return pre_trace, post_trace\n",
    "    \n",
    "    def stdp_window(self, delta_t_range=(-50, 50)):\n",
    "        \"\"\"Calculate STDP window function\"\"\"\n",
    "        delta_t = np.linspace(delta_t_range[0], delta_t_range[1], 1000)\n",
    "        dw = np.where(\n",
    "            delta_t > 0,\n",
    "            self.A_plus * np.exp(-delta_t / self.tau_post),\n",
    "            -self.A_minus * np.exp(delta_t / self.tau_pre)\n",
    "        )\n",
    "        return delta_t, dw\n",
    "\n",
    "# Create STDP system\n",
    "stdp = STDPSystem(tau_pre=20.0, tau_post=20.0, A_plus=0.01, A_minus=0.01, dt=1.0)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STDP system configuration:\n",
      "  τ_pre:  20.0 ms\n",
      "  τ_post: 20.0 ms\n",
      "  A+:     0.01\n",
      "  A-:     0.01\n",
      "  dt:     1.0 ms\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize STDP Window"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-21T07:43:25.981723Z",
     "start_time": "2025-10-21T07:43:25.882275Z"
    }
   },
   "source": [
    "# Plot STDP learning window\n",
    "delta_t, dw = stdp.stdp_window()\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(delta_t, dw, linewidth=2, color='darkblue')\n",
    "plt.axhline(0, color='black', linestyle='--', alpha=0.3)\n",
    "plt.axvline(0, color='red', linestyle='--', alpha=0.3, label='Post spike time')\n",
    "plt.fill_between(delta_t[delta_t > 0], 0, dw[delta_t > 0], alpha=0.3, color='green', label='LTP (potentiation)')\n",
    "plt.fill_between(delta_t[delta_t < 0], 0, dw[delta_t < 0], alpha=0.3, color='red', label='LTD (depression)')\n",
    "plt.xlabel('Δt = t_pre - t_post (ms)', fontsize=12)\n",
    "plt.ylabel('Weight Change (Δw)', fontsize=12)\n",
    "plt.title('STDP Learning Window', fontsize=14)\n",
    "plt.legend(fontsize=11)\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"Explanation:\")\n",
    "print(\"  Δt > 0: Pre before post → strengthen synapse (LTP)\")\n",
    "print(\"  Δt < 0: Pre after post → weaken synapse (LTD)\")"
   ],
   "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
     }
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explanation:\n",
      "  Δt > 0: Pre before post → strengthen synapse (LTP)\n",
      "  Δt < 0: Pre after post → weaken synapse (LTD)\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Implementing STDP on CSR Format\n",
    "\n",
    "The `csr_on_pre` function of BrainEvent can efficiently implement STDP on CSR sparse matrices."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-21T07:43:32.385852Z",
     "start_time": "2025-10-21T07:43:29.458361Z"
    }
   },
   "source": [
    "# Create a simple neural network\n",
    "n_pre = 100\n",
    "n_post = 50\n",
    "conn_prob = 0.1\n",
    "\n",
    "# Generate sparse connections\n",
    "brainstate.random.seed(42)\n",
    "\n",
    "mask = brainstate.random.bernoulli(conn_prob, size=(n_pre, n_post))\n",
    "weights_dense = brainstate.random.uniform(0.0, 0.5, size=(n_pre, n_post)) * mask\n",
    "\n",
    "# Convert to CSR format (manual construction, since COO.to_csr() is not available)\n",
    "row_idx, col_idx = jnp.where(weights_dense != 0)\n",
    "data = weights_dense[row_idx, col_idx]\n",
    "\n",
    "# Manually build CSR format\n",
    "csr_data = []\n",
    "csr_indices = []\n",
    "csr_indptr = [0]\n",
    "\n",
    "for i in range(n_pre):\n",
    "    row = weights_dense[i]\n",
    "    nz_indices = jnp.where(row != 0)[0]\n",
    "    csr_indices.extend(nz_indices.tolist())\n",
    "    csr_data.extend(row[nz_indices].tolist())\n",
    "    csr_indptr.append(len(csr_indices))\n",
    "\n",
    "csr_weights = brainevent.CSR(\n",
    "    (jnp.array(csr_data), jnp.array(csr_indices), jnp.array(csr_indptr)),\n",
    "    shape=(n_pre, n_post)\n",
    ")\n",
    "\n",
    "print(f\"Network configuration:\")\n",
    "print(f\"  Presynaptic neurons: {n_pre}\")\n",
    "print(f\"  Postsynaptic neurons: {n_post}\")\n",
    "print(f\"  Number of connections: {csr_weights.nse}\")\n",
    "print(f\"  Initial weight range: [{csr_weights.data.min():.3f}, {csr_weights.data.max():.3f}]\")\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Network configuration:\n",
      "  Presynaptic neurons: 100\n",
      "  Postsynaptic neurons: 50\n",
      "  Number of connections: 542\n",
      "  Initial weight range: [0.002, 0.500]\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-21T07:43:47.256127Z",
     "start_time": "2025-10-21T07:43:34.227729Z"
    }
   },
   "source": [
    "# Simulate network running and apply STDP\n",
    "n_steps = 500\n",
    "dt = 1.0  # ms\n",
    "\n",
    "# Initialize trace\n",
    "pre_trace = jnp.zeros(n_pre)\n",
    "post_trace = jnp.zeros(n_post)\n",
    "\n",
    "# Record weight changes\n",
    "weight_history = [csr_weights.data.mean()]\n",
    "weight_std_history = [csr_weights.data.std()]\n",
    "\n",
    "# STDP parameters\n",
    "tau_pre = 20.0\n",
    "tau_post = 20.0\n",
    "A_plus = 0.005\n",
    "A_minus = 0.005\n",
    "\n",
    "decay_pre = np.exp(-dt / tau_pre)\n",
    "decay_post = np.exp(-dt / tau_post)\n",
    "\n",
    "print(\"Starting STDP learning...\\n\")\n",
    "\n",
    "brainstate.random.seed(100)\n",
    "\n",
    "for step in range(n_steps):\n",
    "    # Generate random spikes\n",
    "    pre_spike = brainstate.random.bernoulli(0.05, size=(n_pre,))\n",
    "    post_spike = brainstate.random.bernoulli(0.05, size=(n_post,))\n",
    "\n",
    "    # Update trace (decay + spike)\n",
    "    pre_trace = pre_trace * decay_pre + pre_spike.astype(jnp.float32)\n",
    "    post_trace = post_trace * decay_post + post_spike.astype(jnp.float32)\n",
    "\n",
    "    # STDP rule 1: Presynaptic spike trigger (LTP)\n",
    "    # If presynaptic fires, strengthen weights based on postsynaptic trace\n",
    "    new_weights = brainevent.update_csr_on_binary_pre(\n",
    "        weight=csr_weights.data,\n",
    "        indices=csr_weights.indices,\n",
    "        indptr=csr_weights.indptr,\n",
    "        pre_spike=pre_spike,\n",
    "        post_trace=post_trace * A_plus,\n",
    "        w_min=0.0,\n",
    "        w_max=1.0,\n",
    "        shape=csr_weights.shape\n",
    "    )\n",
    "\n",
    "    # Update weights\n",
    "    csr_weights = brainevent.CSR((new_weights, csr_weights.indices, csr_weights.indptr), shape=csr_weights.shape)\n",
    "\n",
    "    # Record statistics\n",
    "    if step % 10 == 0:\n",
    "        weight_history.append(csr_weights.data.mean())\n",
    "        weight_std_history.append(csr_weights.data.std())\n",
    "\n",
    "print(f\"STDP learning completed!\")\n",
    "print(f\"  Final weight range: [{csr_weights.data.min():.3f}, {csr_weights.data.max():.3f}]\")\n",
    "print(f\"  Mean Weight: {csr_weights.data.mean():.3f}\")\n",
    "print(f\"  Weight change: {(csr_weights.data.mean() - weight_history[0]):.3f}\")\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting STDP learning...\n",
      "\n",
      "STDP learning completed!\n",
      "  Final weight range: [0.040, 0.697]\n",
      "  Mean Weight: 0.382\n",
      "  Weight change: 0.124\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-21T07:43:54.832209Z",
     "start_time": "2025-10-21T07:43:54.709763Z"
    }
   },
   "source": [
    "# Visualize Weight Evolution\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "# Mean Weight\n",
    "axes[0].plot(weight_history, linewidth=2, color='darkblue')\n",
    "axes[0].set_xlabel('Steps (×10)', fontsize=12)\n",
    "axes[0].set_ylabel('Mean Weight', fontsize=12)\n",
    "axes[0].set_title('Weight Evolution with STDP', fontsize=14)\n",
    "axes[0].grid(True, alpha=0.3)\n",
    "\n",
    "# Weight distribution\n",
    "axes[1].hist(csr_weights.data, bins=30, alpha=0.7, color='darkblue', edgecolor='black')\n",
    "axes[1].set_xlabel('Weight Value', fontsize=12)\n",
    "axes[1].set_ylabel('Count', fontsize=12)\n",
    "axes[1].set_title('Final Weight Distribution', fontsize=14)\n",
    "axes[1].grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Implementing STDP on COO Format\n",
    "\n",
    "For scenarios requiring frequent modification of connection structure, COO format is more flexible."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-21T07:44:00.066921Z",
     "start_time": "2025-10-21T07:43:59.834005Z"
    }
   },
   "source": [
    "# Using COO format\n",
    "n_pre = 80\n",
    "n_post = 40\n",
    "\n",
    "# Create COO matrix\n",
    "brainstate.random.seed(123)\n",
    "\n",
    "n_synapses = 400\n",
    "pre_ids = brainstate.random.randint(0, n_pre, size=(n_synapses,))\n",
    "post_ids = brainstate.random.randint(0, n_post, size=(n_synapses,))\n",
    "weights = brainstate.random.uniform(0.1, 0.5, size=(n_synapses,))\n",
    "\n",
    "coo_weights = brainevent.COO((weights, pre_ids, post_ids), shape=(n_pre, n_post))\n",
    "\n",
    "print(f\"COO network:\")\n",
    "print(f\"  Shape: {coo_weights.shape}\")\n",
    "print(f\"  Number of synapses: {n_synapses}\")\n",
    "print(f\"  Initial weights: {coo_weights.data.mean():.3f} ± {coo_weights.data.std():.3f}\")\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COO network:\n",
      "  Shape: (80, 40)\n",
      "  Number of synapses: 400\n",
      "  Initial weights: 0.307 ± 0.118\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-21T07:44:12.182075Z",
     "start_time": "2025-10-21T07:44:02.516025Z"
    }
   },
   "source": [
    "# Implementing STDP using coo_on_pre\n",
    "n_steps = 300\n",
    "pre_trace = jnp.zeros(n_pre)\n",
    "post_trace = jnp.zeros(n_post)\n",
    "\n",
    "weight_mean_history = [coo_weights.data.mean()]\n",
    "\n",
    "brainstate.random.seed(999)\n",
    "\n",
    "for step in range(n_steps):\n",
    "    # Generate spikes\n",
    "    pre_spike = brainstate.random.bernoulli(0.08, size=(n_pre,))\n",
    "    post_spike = brainstate.random.bernoulli(0.08, size=(n_post,))\n",
    "\n",
    "    # Update trace\n",
    "    pre_trace = pre_trace * decay_pre + pre_spike.astype(jnp.float32)\n",
    "    post_trace = post_trace * decay_post + post_spike.astype(jnp.float32)\n",
    "\n",
    "    # 使用coo_on_preUpdate weights\n",
    "    new_weights = brainevent.update_coo_on_binary_pre(\n",
    "        weight=coo_weights.data,\n",
    "        pre_ids=coo_weights.row,\n",
    "        post_ids=coo_weights.col,\n",
    "        pre_spike=pre_spike,\n",
    "        post_trace=post_trace * A_plus,\n",
    "        w_min=0.0,\n",
    "        w_max=1.0\n",
    "    )\n",
    "\n",
    "    # Update COO matrix\n",
    "    coo_weights = brainevent.COO((new_weights, coo_weights.row, coo_weights.col), shape=coo_weights.shape)\n",
    "\n",
    "    if step % 10 == 0:\n",
    "        weight_mean_history.append(coo_weights.data.mean())\n",
    "\n",
    "print(f\"\\nCOO-STDP learning completed:\")\n",
    "print(f\"  Final weights: {coo_weights.data.mean():.3f} ± {coo_weights.data.std():.3f}\")\n",
    "print(f\"  Weight change: {(coo_weights.data.mean() - weight_mean_history[0]):.3f}\")\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "COO-STDP learning completed:\n",
      "  Final weights: 0.493 ± 0.127\n",
      "  Weight change: 0.186\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Practice: Building a Self-Learning Neural Network\n",
    "\n",
    "Let's build a complete spiking neural network with STDP learning capability."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-21T07:46:46.131874Z",
     "start_time": "2025-10-21T07:46:45.864667Z"
    }
   },
   "source": [
    "class AdaptiveSpikingNetwork:\n",
    "    \"\"\"Spiking neural network with STDP learning capability\"\"\"\n",
    "\n",
    "    def __init__(self, n_input, n_hidden, n_output, conn_prob=0.15, seed=0):\n",
    "        self.n_input = n_input\n",
    "        self.n_hidden = n_hidden\n",
    "        self.n_output = n_output\n",
    "\n",
    "        brainstate.random.seed(seed)\n",
    "\n",
    "        # Create sparse connections (CSR format)\n",
    "        # First layer\n",
    "        mask1 = brainstate.random.bernoulli(conn_prob, size=(n_input, n_hidden))\n",
    "        w1 = brainstate.random.uniform(0.0, 0.3, size=(n_input, n_hidden)) * mask1\n",
    "\n",
    "        # Manually build CSR format\n",
    "        csr_data1 = []\n",
    "        csr_indices1 = []\n",
    "        csr_indptr1 = [0]\n",
    "        for i in range(n_input):\n",
    "            row = w1[i]\n",
    "            nz = jnp.where(row != 0)[0]\n",
    "            csr_indices1.extend(nz.tolist())\n",
    "            csr_data1.extend(row[nz].tolist())\n",
    "            csr_indptr1.append(len(csr_indices1))\n",
    "        self.w1 = brainevent.CSR(\n",
    "            (jnp.array(csr_data1), jnp.array(csr_indices1), jnp.array(csr_indptr1)),\n",
    "            shape=(n_input, n_hidden)\n",
    "        )\n",
    "\n",
    "        # Second layer\n",
    "        mask2 = brainstate.random.bernoulli(conn_prob, size=(n_hidden, n_output))\n",
    "        w2 = brainstate.random.uniform(0.0, 0.3, size=(n_hidden, n_output)) * mask2\n",
    "\n",
    "        csr_data2 = []\n",
    "        csr_indices2 = []\n",
    "        csr_indptr2 = [0]\n",
    "        for i in range(n_hidden):\n",
    "            row = w2[i]\n",
    "            nz = jnp.where(row != 0)[0]\n",
    "            csr_indices2.extend(nz.tolist())\n",
    "            csr_data2.extend(row[nz].tolist())\n",
    "            csr_indptr2.append(len(csr_indices2))\n",
    "        self.w2 = brainevent.CSR(\n",
    "            (jnp.array(csr_data2), jnp.array(csr_indices2), jnp.array(csr_indptr2)),\n",
    "            shape=(n_hidden, n_output)\n",
    "        )\n",
    "\n",
    "        # Initialize trace\n",
    "        self.input_trace = jnp.zeros(n_input)\n",
    "        self.hidden_trace = jnp.zeros(n_hidden)\n",
    "        self.output_trace = jnp.zeros(n_output)\n",
    "\n",
    "        # STDP parameters\n",
    "        self.tau = 20.0\n",
    "        self.A_plus = 0.008\n",
    "        self.decay = np.exp(-1.0 / self.tau)\n",
    "\n",
    "        print(f\"Adaptive network: {n_input} -> {n_hidden} -> {n_output}\")\n",
    "        print(f\"  First layer connection: {self.w1.nse}\")\n",
    "        print(f\"  Second layer connection: {self.w2.nse}\")\n",
    "\n",
    "    def forward(self, input_spikes, learning=True):\n",
    "        \"\"\"Forward propagation, optional learning\"\"\"\n",
    "        # First layer\n",
    "        hidden_input = input_spikes @ self.w1\n",
    "        hidden_spikes = brainevent.BinaryArray(hidden_input > 0.5)\n",
    "\n",
    "        # Second layer\n",
    "        output_input = hidden_spikes @ self.w2\n",
    "        output_spikes = brainevent.BinaryArray(output_input > 0.8)\n",
    "\n",
    "        # Update trace\n",
    "        input_arr = input_spikes.value if isinstance(input_spikes, brainevent.BinaryArray) else input_spikes\n",
    "        self.input_trace = self.input_trace * self.decay + input_arr.astype(jnp.float32)\n",
    "        self.hidden_trace = self.hidden_trace * self.decay + hidden_spikes.astype(jnp.float32)\n",
    "        self.output_trace = self.output_trace * self.decay + output_spikes.astype(jnp.float32)\n",
    "\n",
    "        # STDP learning\n",
    "        if learning:\n",
    "            # update First layer weight\n",
    "            new_w1 = brainevent.update_csr_on_binary_pre(\n",
    "                weight=self.w1.data,\n",
    "                indices=self.w1.indices,\n",
    "                indptr=self.w1.indptr,\n",
    "                pre_spike=input_spikes.value if isinstance(input_spikes, brainevent.BinaryArray) else input_spikes,\n",
    "                post_trace=self.hidden_trace * self.A_plus,\n",
    "                w_min=0.0,\n",
    "                w_max=1.0,\n",
    "                shape=self.w1.shape\n",
    "            )\n",
    "            self.w1 = brainevent.CSR((new_w1, self.w1.indices, self.w1.indptr), shape=self.w1.shape)\n",
    "\n",
    "            # update Second layer weight\n",
    "            new_w2 = brainevent.update_csr_on_binary_pre(\n",
    "                weight=self.w2.data,\n",
    "                indices=self.w2.indices,\n",
    "                indptr=self.w2.indptr,\n",
    "                pre_spike=hidden_spikes.value if isinstance(hidden_spikes, brainevent.BinaryArray) else hidden_spikes,\n",
    "                post_trace=self.output_trace * self.A_plus,\n",
    "                w_min=0.0,\n",
    "                w_max=1.0,\n",
    "                shape=self.w2.shape\n",
    "            )\n",
    "            self.w2 = brainevent.CSR((new_w2, self.w2.indices, self.w2.indptr), shape=self.w2.shape)\n",
    "\n",
    "        return output_input, hidden_spikes, output_spikes\n",
    "\n",
    "# Create adaptive network\n",
    "adaptive_net = AdaptiveSpikingNetwork(\n",
    "    n_input=200,\n",
    "    n_hidden=100,\n",
    "    n_output=10,\n",
    "    conn_prob=0.12,\n",
    "    seed=2024\n",
    ")\n",
    "\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adaptive network: 200 -> 100 -> 10\n",
      "  First layer connection: 2433\n",
      "  Second layer connection: 123\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-21T07:47:12.663093Z",
     "start_time": "2025-10-21T07:46:52.718977Z"
    }
   },
   "source": [
    "# Train Network\n",
    "n_epochs = 50\n",
    "n_samples_per_epoch = 20\n",
    "\n",
    "w1_history = []\n",
    "w2_history = []\n",
    "output_activity_history = []\n",
    "\n",
    "# Record spike activity for visualization\n",
    "hidden_spike_matrix = []\n",
    "output_spike_matrix = []\n",
    "\n",
    "brainstate.random.seed(0)\n",
    "\n",
    "print(\"Starting training...\\n\")\n",
    "\n",
    "for epoch in range(n_epochs):\n",
    "    epoch_output_count = 0\n",
    "\n",
    "    for sample in range(n_samples_per_epoch):\n",
    "        # Generate random input pattern\n",
    "        input_pattern = brainstate.random.bernoulli(0.1, size=(200,))\n",
    "        input_spikes = brainevent.BinaryArray(input_pattern)\n",
    "\n",
    "        # Forward propagation + learning\n",
    "        output, hidden_spk, output_spk = adaptive_net.forward(input_spikes, learning=True)\n",
    "        epoch_output_count += int(output_spk.sum())\n",
    "\n",
    "        # Record spike activity in last epoch (first 50 neurons)\n",
    "        if epoch == n_epochs - 1:\n",
    "            hidden_spike_matrix.append(np.array(hidden_spk.value[:50], dtype=int))\n",
    "            output_spike_matrix.append(np.array(output_spk.value, dtype=int))\n",
    "\n",
    "    # Record statistics\n",
    "    w1_history.append(adaptive_net.w1.data.mean())\n",
    "    w2_history.append(adaptive_net.w2.data.mean())\n",
    "    output_activity_history.append(epoch_output_count / n_samples_per_epoch)\n",
    "\n",
    "    if epoch % 10 == 0:\n",
    "        print(f\"Epoch {epoch}: W1={w1_history[-1]:.4f}, W2={w2_history[-1]:.4f}, Output={output_activity_history[-1]:.2f}\")\n",
    "\n",
    "# Convert to arrays for visualization\n",
    "hidden_spike_matrix = np.array(hidden_spike_matrix)  # (20, 50)\n",
    "output_spike_matrix = np.array(output_spike_matrix)  # (20, 10)\n",
    "\n",
    "print(f\"\\nTraining completed!\")\n",
    "print(f\"  First layerWeight change: {w1_history[0]:.4f} -> {w1_history[-1]:.4f}\")\n",
    "print(f\"  Second layerWeight change: {w2_history[0]:.4f} -> {w2_history[-1]:.4f}\")\n",
    "print(f\"  Total hidden layer spikes: {hidden_spike_matrix.sum()}\")\n",
    "print(f\"  Total output layer spikes: {output_spike_matrix.sum()}\")\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting training...\n",
      "\n",
      "Epoch 0: W1=0.1965, W2=0.3578, Output=4.35\n",
      "Epoch 10: W1=0.9997, W2=1.0000, Output=10.00\n",
      "Epoch 20: W1=1.0000, W2=1.0000, Output=10.00\n",
      "Epoch 30: W1=1.0000, W2=1.0000, Output=10.00\n",
      "Epoch 40: W1=1.0000, W2=1.0000, Output=10.00\n",
      "\n",
      "Training completed!\n",
      "  First layerWeight change: 0.1965 -> 1.0000\n",
      "  Second layerWeight change: 0.3578 -> 1.0000\n",
      "  Total hidden layer spikes: 921\n",
      "  Total output layer spikes: 200\n"
     ]
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-21T07:47:26.842201Z",
     "start_time": "2025-10-21T07:47:26.496930Z"
    }
   },
   "source": [
    "# Visualize Learning Process\n",
    "fig = plt.figure(figsize=(14, 12))\n",
    "\n",
    "# Subplot 1: Weight Evolution\n",
    "ax1 = plt.subplot(3, 2, 1)\n",
    "ax1.plot(w1_history, label='Layer 1', linewidth=2, color='blue')\n",
    "ax1.plot(w2_history, label='Layer 2', linewidth=2, color='orange')\n",
    "ax1.set_xlabel('Epoch', fontsize=12)\n",
    "ax1.set_ylabel('Mean Weight', fontsize=12)\n",
    "ax1.set_title('Weight Evolution', fontsize=13, fontweight='bold')\n",
    "ax1.legend()\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# Subplot 2: Output Activity\n",
    "ax2 = plt.subplot(3, 2, 2)\n",
    "ax2.plot(output_activity_history, linewidth=2, color='green')\n",
    "ax2.set_xlabel('Epoch', fontsize=12)\n",
    "ax2.set_ylabel('Avg Output Spikes', fontsize=12)\n",
    "ax2.set_title('Output Layer Activity', fontsize=13, fontweight='bold')\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "# 子图3: First layerWeight distribution\n",
    "ax3 = plt.subplot(3, 2, 3)\n",
    "ax3.hist(adaptive_net.w1.data, bins=30, alpha=0.7, color='blue', edgecolor='black')\n",
    "ax3.set_xlabel('Weight Value', fontsize=12)\n",
    "ax3.set_ylabel('Count', fontsize=12)\n",
    "ax3.set_title('Layer 1 Weight Distribution', fontsize=13, fontweight='bold')\n",
    "ax3.grid(True, alpha=0.3)\n",
    "\n",
    "# 子图4: Second layerWeight distribution\n",
    "ax4 = plt.subplot(3, 2, 4)\n",
    "ax4.hist(adaptive_net.w2.data, bins=30, alpha=0.7, color='orange', edgecolor='black')\n",
    "ax4.set_xlabel('Weight Value', fontsize=12)\n",
    "ax4.set_ylabel('Count', fontsize=12)\n",
    "ax4.set_title('Layer 2 Weight Distribution', fontsize=13, fontweight='bold')\n",
    "ax4.grid(True, alpha=0.3)\n",
    "\n",
    "# Subplot 5: Hidden Layer Spike Raster (using braintools)\n",
    "ax5 = plt.subplot(3, 2, 5)\n",
    "ts = np.arange(len(hidden_spike_matrix))\n",
    "braintools.visualize.raster_plot(\n",
    "    ts,\n",
    "    hidden_spike_matrix,\n",
    "    markersize=4,\n",
    "    ax=ax5,\n",
    "    xlim=(0, len(hidden_spike_matrix)),\n",
    "    ylim=(-1, 50),\n",
    "    xlabel='Sample ID (Last Epoch)',\n",
    "    ylabel='Hidden Neuron ID (first 50)',\n",
    "    title=f'Hidden Layer Spike Raster (Rate: {hidden_spike_matrix.mean():.2%})',\n",
    "    show=False\n",
    ")\n",
    "\n",
    "# Subplot 6: Output Layer Spike Raster (using braintools)\n",
    "ax6 = plt.subplot(3, 2, 6)\n",
    "braintools.visualize.raster_plot(\n",
    "    ts,\n",
    "    output_spike_matrix,\n",
    "    markersize=5,\n",
    "    ax=ax6,\n",
    "    xlim=(0, len(output_spike_matrix)),\n",
    "    ylim=(-1, 10),\n",
    "    xlabel='Sample ID (Last Epoch)',\n",
    "    ylabel='Output Neuron ID',\n",
    "    title=f'Output Layer Spike Raster (Rate: {output_spike_matrix.mean():.2%})',\n",
    "    show=False\n",
    ")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1200 with 6 Axes>"
      ],
      "image/png": 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LW9wtisScRV2N9VlbkkWh4lBxRrmXPodUuIod8zX2c15FQeV8yv00pIGogKvcJ5rHaMgqfRmt7vu6lXQ+qs9vfdbrpvxfXzhruCgNr6RlyqmjdIw1rEMs5X5SkGOvfdWYvCpMJ+YVhb2eyIuKwZMnT3bFLw3BoPxFxy82n44WHNV4QkOoJT5v7PnRMVEBXMVPNazQF9y6fkg8BiWRqxX2GiJxHOHSysnzs3DhQrcNGl5Nt/z2Ua/l0rCnHDF2Hd0nDu8Xu+6evuDQ8oLG2d02kbciVVC0BQJGiZFaoqpFYV6iyVxUfkWd3SUfBaUCrL5RVxH2oYceci0FVHTUh6AS4lj6FliJoz4Mtb7u1bo0dpIp7ZsKtrEtIGIlJmIFHVs2r0KijqFu2l4lUSri6lv3kqB91bhjSj5VtI1NXrWP+rBXQTevc5NfsW53XyaotbLGiFWCoXF+o8+hY6yJH7RciYiOrWLqwiZ2cgI9rqK7knptl246h0qUdWx2JzpZhcbE0/lMpCJtLCWxuxvkP5YmElErhOgYWYlU/I2+fpTYa0wyFXGVYEXHAdOFl86rzrnGf9OFps6HWjVovOKCTLahdTQx29ixY/NcrouLotB4tPqyQOPt6UsUtfjQa1pj7eX1WtTFgArr2ie99zQhSUnS+1c0ll0iPba7eCqGq5WwLjzVwkSt6tXaRK890f/1vk5MfrUv0TENAQC7UiFPf59VYNsdLVfBJvplaX6FhdhJOIsq8blLM1ZBqJWq8hb1UFMvFB0zbZPGuE38nFduo1aZGtdSn+1qEakvr6O5SXR9tbzNrzdLYm+7ouajat2nMUI1FqkKk/qcjC3aFpe+FFYep+fVuLXFuZ7Iiz7vY/N4jbeqHmya/0DjHkfpC2odb+WiGmtUeariq3FA7PnRemr9q3kgNEaqWu5qrgT1SFL+U5K5WmHOmfLR3TV2KcmcfHf7J+qhlV8vQzUuKS0aD1k5fH45okTzRP290ntfReTYhjgq5Kpl8J7yV/1+bKvh3cWJfTxxXfJWpAqKtoCHlIxFWyLG0od44nqidWO/Tda31YlJgopOasUZmyR5Rd3HVABTsS/2G2UlzomUmCiBUetWJWCaEEAD8ccWLrVv+qZTSV9RE+DCUpKlhDbxAz/xvKnIrW+5CzIshrp8aTKFM8880w2ToOQzWrzUPuq59K16QRLigogmfUos1FJD3xiLWmLq9aRkN/aCSpNYJFI3JrXI0E2JoS5gNBu1vs1XEpjfBVl0shIlZSX9mlQXJdFkdAU57rogUXcntarVBZC2WTM7K0HUMYidYEDPmWh3+/i///3PTepQ0t+u6/liJxNTV1Md/8RjqRYDOjeaTEPvkYJ26SsMTbKh1+nnn3/uLpxik2wV9WMfS6SCuF7T0aFf1LUxdpIGJc6Js+0qIddzRye0AwDkTfmEWjRqkiUVhBLpizy1vIwtzKlnlQoMiaK9u2Lt6bNNOVFsa0DlQ/qsik7eFO3FlRivKLGKQvmOCqxqxRj7uZnX/qtQqEYAKsio1Z166MT2+tIyfVGsolOycu3oREn6IjeWjrGGM4vNF5UHyO4mzopS0VOf68rptE/Kw0vzekK5mordyh9VXFRvP13HqDWxWtpqEtiovK6PooU4ba9uKvqpCKwWvNGirRe5mvLRvPLG0sjJ89v26PWhGpt4cQ2oLzVUFFeOmEgNX7R90QYW0YmOta4aVUTpZ72mo8vzo+UazkKF1dgee4oT+/zkrQgKxrQFPKQPKo2do1lBYwuxiS1J9eGrD2HNYhnbAjavmVv1ATRnzhxXKE2k5FQtE5NFiYmSi9iWFLpoSJyhNUpJsZI3XVQoUUycOVT7pueKdlmLpf3KK/kuCCXkOmZ5UavSvIas0My+sV0RlWypsLunb/pjz6m6ganFrfY7+g25WoDquCl5TWztrJ9VYCwKtejVN/16zUS7D0UL4rFxlPAkHovEmErMokXSaJej6LjDiedA3/YrodLQBLFdvWJf70Wl1jKSV4KYFyVuGpdK3eNeeeWVfI+BEi61DE+kfcxruAS9LpWo6YI5ry6ZGg84SkOmqNubXnOFpedSkVwXLBrGI0rvCY1NrfOm1kHR45IXvUYTu90VlFom6XWr1iCxr321otb7VV9A5EUXkLowuf/++3MvNtSqPna8YJ2TxJbY8+bNc/dHHXVUobcVAMJELfP0Zbbyp8TPbHU91xfFVapUyR0rNVrE0mdabAtdfUboi+S8Pv92l2Op+3es6Kzr0ZxIeYBanyWOg5rfZ60UNafLiz7rE3MqbWNeLX2VK+gzVsNCqSGBClGxXwzrudRCVQ0TEouoxc1rVFTMa2g1FbfV6yYxFxV9vkZpH/WzrhlUnNwTfSZriAe1GlRRW62KS/t6Qi13lYNEW7zmlYfldY2jc5WYg6lBgIpnsd3fSyJXKyzlXXot5NUNv6Rzcr2P83p/6FjoeVUQz6tlaeLrUq13C9KCtzD0OtJwZLF5uRoiafix2BxRQ42oZa7GiI2ln7V/GuZrd3mz4uj1EDs8iY69GgXpi5Zoq2nyVgQFLSD0I58AAG2pSURBVG0BDylx0QeHvtW//vrrXaKhDyC1rI1NovWtvr6R1jezak2hYu8XX3zhCoqJXTCUkCvp0nrqZqMxn5SIfP31166wqKJpSXbbUNKa12RNSv70oaukTPunb+/1jbgSe7XMzKsbn77B1Lei0ckC9O15LHUJ1wWJjoO+3dTYqkpM9W28fkcfrvogLywlAvqAVQtYbas+7JUMqbis1inqChP77aoo2VBrlh49erixd5V4ab/UOrig9LzRYQZ0QaNESxdR6vo2ZMgQd660jr6Z1jf4upDq1auXey0UhV4bSlB0ERJt6atv9NXqVOdKMTSwvlppKpmJ+uc//+ku+pRkacgBXTzoYkffZEe/Tdb/lXCqlbSSZbWs1vpKIpWEqTCt86muiHo9a5B/jdmqVtOxFxyFoW/t9XpRy1J1HywIvSfUkkPbqWOr864WLHq9arIBJWd6T+Y1PIjeSxp3WROZtW/f3nVpU+tW7Zsu7nRM9c2/9knJpN4XelwXPNFucdpXFeS13p4mI9MFhi5GdD7UmkDjoKlFjY5b7HAQKkTrPa9t0XlSchor9ssPva7UclznOrYVTrS7pcb3FR0DtdgStUyOUmsWHTO9F/Va/PXXX13LJb0X8xvjWMM5qKgcO+Gi3qcac1hdJOW1115z418nTmKnVjiJ7z0AwK5jyupvu1qFqcioyavUSkx5hCblVOFD3XujvV9En8caBkg5gD7/lAvp81qtNhMnPtLnnz5rldPpc0nPHTv2oz5TNByWPgdUZNLnkPK+Nm3axOUSd955p7vXZ6IKuNFWoYmxRF2TtY3K8/T5Fi3m5kVFwLyGDVCuphaZynf0uaYijj5TtY3aH3Vrz4vyMnXf12e18oVE2g8t0zFQ3qfn1OevjpueV/8vCn3uqWWljqVyUuUZ+tzX57+KUprMNJZ6s+nLf+Uw2hZdFyhH0GdrfmO3JtIX8TpfyomUd6hHj/K30rqe0LHSdcyjjz7qvojWOVBPJw0ZpvOoITw09EFiy1UV3ZSDKn/Q60rHRsdaRcLYFtQlkasVlvIZNSiZNWtW3OTCpZGT68sZPaZ91HtVr3HlwrrpGkvXJvoboNel8mRdo+j1rnxNXwpERYv6BRn7WO8d5f7Rwqneu9H3m45rtEeo3msqimv7da2i967+ZqjgqVw1dh90vDSBoY6BGnjoekuvQ+WZ2qeovPJmvdb1e8ppdW2p6y/9/Yv+vYtF3opAiAAokilTpqiqE/nss8/yXN6pU6fIwQcfHPdYkyZNIt27d4977KuvvnLrVqpUKbL33ntHRo4cGcnMzHTPvWjRotz1duzYERkxYkSkYcOGkcqVK0eOP/74yDfffJPnc27cuDEyZMiQyP777x+pUKFCZK+99oocddRRkXvuuSeydetWt46eWzHuvvvuXbZdjw8bNmy3+//++++79fK7ffjhh2497csBBxwQqVixYqRFixbuuOm58/vzc9ddd7lld9xxR76xH3nkkUi7du3ccahWrVrkkEMOiQwaNCjy+++/xx3rM844I1IQ27Zti0yePDnStWtX93va1ipVqkQOPfRQd3y2bNmyy34//fTT7hjXq1fPbYdiLVmyJO55dV70fFH5HfOHHnrIPX7jjTfmPvbCCy9EjjnmmEh6erq76dhde+21kQULFhT5danX0H777edu27dvj+zcudMd5+g+a39ff/31Xbb7v//9b+SUU05x+6rXU+PGjSNXXXVVZNmyZXHPr2O47777RsqWLeu2Qccq9rh16dIlUqNGDfda1zZcfvnlkc8//zzueGlfC2Ps2LGRqlWrRv7888+4xxVfxysvw4cPj9u+2bNnR4488kh3Hhs1auReS2+//fYu+5CdnR25+OKLIzVr1nTLYo+R3ldjxoxx73kdy1q1arnXqN6z69evz10v+tqPfd786Pl03nW89Hxnn3125IsvvthlPf392N17MZaOceLflujxKsjvi97b+nui7apbt647zhs2bMhzH9544w13fmLfm1GjR492x1t/07Svia9VPX7LLbfs8TgBAP4/p7zooovc38/y5ctHGjRo4H7++uuv81z/nXfeibRq1cp9tjdv3jzy5JNP5pmj/fDDD5HjjjvOfU5qWTTvjK773XffRf72t7+5nEyfV3369Ils3rw57jn0Od2zZ0+XB2i9888/P7Jy5co8c07lwsqJy5Qpk+dnVl6fa3ndlGvI2rVrIz169HD5sD6TlI9on/LKoaP0ea74v/76a57LV6xY4T7/MjIyco/1iSee6HLUxJzx+eefjxTEL7/8Ern11ltdTqKcq1y5cu5zVjnme++9t8t+K2f6+eefXY6mvLV+/fruWOozNFbiMY6et1WrVsWdH+UTOj5z584t8PVEfvK6DoqaOXNm3DbpGJ977rkuv9Lr4+9//7vLG2LXUS4+cODASJs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     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 14
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Summary\n",
    "\n",
    "In this tutorial, we learned:\n",
    "\n",
    "1. ✅ **Synaptic plasticity principles**: Hebb's Rule和STDP机制\n",
    "2. ✅ **STDP window**: Time dependency of LTP and LTD\n",
    "3. ✅ **CSR format STDP**: Using `csr_on_pre` for efficient learning\n",
    "4. ✅ **COO format STDP**: Using `coo_on_pre` for flexible learning\n",
    "5. ✅ **Trace mechanism**: Exponentially decaying spike trace\n",
    "6. ✅ **Adaptive network**: Build complete network with learning capability\n",
    "7. ✅ **Learning process visualization**: Observe weight evolution and network adaptation\n",
    "\n",
    "### Key Points\n",
    "- 🔑 **STDP = Biological learning rule**\n",
    "- 🔑 **Trace = Record historical spikes**\n",
    "- 🔑 **BrainEvent provides efficient implementation**\n",
    "- 🔑 **Supports multiple data structures**"
   ]
  }
 ],
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