{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "# Tutorial 4: Fixed Connection Count Structures - Biologically Realistic Network Topology\n\nIn biological neural networks, each neuron typically has a **fixed number of synaptic connections**. For example:\n- Cortical pyramidal neurons: approximately 10,000 dendritic spines (receiving connections)\n- Cerebellar Purkinje cells: approximately 200,000 dendritic spines\n- The number of output connections per neuron is also relatively fixed\n\nBrainEvent provides two fixed connection count structures to simulate these biological characteristics:\n- **FixedPostNumConn**: Each **presynaptic** neuron has a fixed number of output connections\n- **FixedPreNumConn**: Each **postsynaptic** neuron has a fixed number of input connections\n\n## Contents\n1. Biological significance of fixed connection counts\n2. FixedPostNumConn - Fixed output connections\n3. FixedPreNumConn - Fixed input connections\n4. Creation and usage\n5. Using with BinaryArray\n6. Practice: Biologically realistic cortical network\n7. Performance and memory analysis"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 1. Biological significance of fixed connection counts\n\n### Why do we need fixed connection counts?\n\nIn biological neural networks:\n- 🧠 **Energy constraints**: The number of synapses per neuron is limited by metabolic costs\n- 🧠 **Space constraints**: Dendritic physical space is limited\n- 🧠 **Functional requirements**: Different neuron types have specific connection patterns\n\n### Comparison with other structures\n\n| Data Structure | Connection Method | Use Cases |\n|---------|---------|----------|\n| **CSR/COO** | Arbitrary sparse connections | General sparse matrices |\n| **JIT connections** | Random probability connections | Ultra-large-scale networks |\n| **FixedPostNumConn** | Fixed number of outputs per presynaptic neuron | Biologically realistic models |\n| **FixedPreNumConn** | Fixed number of inputs per postsynaptic neuron | Specific topologies |\n\n### Core advantages\n- ✅ **Biologically realistic**: Matches real neural network characteristics\n- ✅ **Regular memory**: Regular data structures, easy to optimize\n- ✅ **Efficient computation**: Fixed-size operations, easy to parallelize\n- ✅ **Strong controllability**: Precisely control connection count per neuron"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-21T09:06:52.757282Z",
     "start_time": "2025-10-21T09:06:52.739340Z"
    }
   },
   "source": [
    "import brainevent\n",
    "import brainstate\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": 10
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 2. FixedPostNumConn - Fixed output connection count\n\n**FixedPostNumConn** represents that each **presynaptic neuron** sends out a fixed number of output connections.\n\n### Data structure\n```python\ndata:    [num_pre, num_conn]  # weight matrix\nindices: [num_pre, num_conn]  # which postsynaptic neurons each presynaptic neuron connects to\n```\n\n### Creation method"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-21T09:06:55.784857Z",
     "start_time": "2025-10-21T09:06:55.763631Z"
    }
   },
   "source": "# Example: 100 presynaptic neurons -> 50 postsynaptic neurons\n# Each presynaptic neuron connects to 10 postsynaptic neurons\n\nn_pre = 100\nn_post = 50\nnum_conn_per_pre = 10  # Number of output connections per presynaptic neuron\n\n# Randomly generate connection indices and weights\nbrainstate.random.seed(42)\n\n# Randomly select 10 postsynaptic neurons for each presynaptic neuron\nindices = brainstate.random.randint(0, n_post, size=(n_pre, num_conn_per_pre))\n\n# Generate random weights\nweights = brainstate.random.normal(size=(n_pre, num_conn_per_pre)) * 0.1\n\n# Create FixedPostNumConn structure\nfixed_post = brainevent.FixedPostNumConn(\n    (weights, indices),\n    shape=(n_pre, n_post)\n)\n\nprint(\"FixedPostNumConn information:\")\nprint(f\"  shape: {fixed_post.shape}\")\nprint(f\"  weight array shape: {fixed_post.data.shape}\")\nprint(f\"  indices array shape: {fixed_post.indices.shape}\")\nprint(f\"  total connections: {n_pre * num_conn_per_pre}\")\nprint(f\"\\nExample connections:\")\nprint(f\"  neuron 0 connects to: {indices[0]}\")\nprint(f\"  corresponding weights: {weights[0]}\")",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FixedPostNumConn information:\n",
      "  shape: (100, 50)\n",
      "  weight array shape: (100, 10)\n",
      "  indices array shape: (100, 10)\n",
      "  total connections: 1000\n",
      "\n",
      "Example connections:\n",
      "  neuron 0 connects to: [35 22  4 22 35  4 41 38  6 25]\n",
      "  corresponding weights: [-0.02108904 -0.13627948 -0.00450038 -0.11536395  0.19141139 -0.04770131\n",
      "  0.06478766  0.06747401  0.29508728 -0.08744793]\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Visualizing connection patterns"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-21T09:06:58.326189Z",
     "start_time": "2025-10-21T09:06:58.139945Z"
    }
   },
   "source": "# Convert to dense matrix for visualization\ndense_matrix = fixed_post.todense()\n\nfig, axes = plt.subplots(1, 2, figsize=(14, 5))\n\n# Connection pattern\nim1 = axes[0].imshow(np.array(dense_matrix != 0), aspect='auto', cmap='binary', interpolation='nearest')\naxes[0].set_title(f'Connection Pattern\\n({n_pre * num_conn_per_pre} connections)', fontsize=12)\naxes[0].set_xlabel('Post-synaptic Neurons (50)', fontsize=10)\naxes[0].set_ylabel('Pre-synaptic Neurons (100)', fontsize=10)\nplt.colorbar(im1, ax=axes[0], label='Connection exists')\n\n# Weight distribution\nim2 = axes[1].imshow(np.array(dense_matrix), aspect='auto', cmap='RdBu_r', interpolation='nearest')\naxes[1].set_title('Weight Values', fontsize=12)\naxes[1].set_xlabel('Post-synaptic Neurons (50)', fontsize=10)\naxes[1].set_ylabel('Pre-synaptic Neurons (100)', fontsize=10)\nplt.colorbar(im2, ax=axes[1], label='Weight')\n\nplt.tight_layout()\nplt.show()\n\n# Statistics of connections received by each postsynaptic neuron\npost_conn_counts = (dense_matrix != 0).sum(axis=0)\nprint(f\"\\nInput connections per postsynaptic neuron:\")\nprint(f\"  Minimum: {post_conn_counts.min()}\")\nprint(f\"  Maximum: {post_conn_counts.max()}\")\nprint(f\"  Average: {post_conn_counts.mean():.1f}\")\nprint(f\"  Standard deviation: {post_conn_counts.std():.1f}\")",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x500 with 4 Axes>"
      ],
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     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Input connections per postsynaptic neuron:\n",
      "  Minimum: 12\n",
      "  Maximum: 29\n",
      "  Average: 18.4\n",
      "  Standard deviation: 3.7\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 3. FixedPreNumConn - Fixed input connection count\n\n**FixedPreNumConn** represents that each **postsynaptic neuron** receives a fixed number of input connections.\n\n### Data structure\n```python\ndata:    [num_post, num_conn]  # weight matrix\nindices: [num_post, num_conn]  # which presynaptic neurons each postsynaptic neuron receives input from\n```"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-21T09:07:01.822500Z",
     "start_time": "2025-10-21T09:07:01.812155Z"
    }
   },
   "source": "# Example: 200 presynaptic neurons -> 100 postsynaptic neurons\n# Each postsynaptic neuron receives 15 input connections\n\nn_pre = 200\nn_post = 100\nnum_conn_per_post = 15  # Number of input connections per postsynaptic neuron\n\nbrainstate.random.seed(123)\n\n# Randomly select 15 presynaptic neurons for each postsynaptic neuron\nindices = brainstate.random.randint(0, n_pre, size=(n_post, num_conn_per_post))\n\n# Generate random weights\nweights = brainstate.random.normal(size=(n_post, num_conn_per_post)) * 0.05\n\n# Create FixedPreNumConn structure\nfixed_pre = brainevent.FixedPreNumConn(\n    (weights, indices),\n    shape=(n_pre, n_post)\n)\n\nprint(\"FixedPreNumConn information:\")\nprint(f\"  shape: {fixed_pre.shape}\")\nprint(f\"  weight array shape: {fixed_pre.data.shape}\")\nprint(f\"  indices array shape: {fixed_pre.indices.shape}\")\nprint(f\"  total connections: {n_post * num_conn_per_post}\")\nprint(f\"\\nExample connections:\")\nprint(f\"  neuron 0 receives from: {indices[0]}\")\nprint(f\"  corresponding weights: {weights[0]}\")",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FixedPreNumConn information:\n",
      "  shape: (200, 100)\n",
      "  weight array shape: (100, 15)\n",
      "  indices array shape: (100, 15)\n",
      "  total connections: 1500\n",
      "\n",
      "Example connections:\n",
      "  neuron 0 receives from: [ 16  45  37  61  50  72  47 197 140 186 182 105   7 100  30]\n",
      "  corresponding weights: [-0.09292667 -0.02407313 -0.00171479 -0.17258634  0.01208776 -0.02844428\n",
      " -0.0558086   0.01732394 -0.05132271 -0.00761002  0.03108218  0.05758841\n",
      "  0.10048639  0.0313901  -0.02753625]\n"
     ]
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 4. Using with BinaryArray\n\nWhen combined with BinaryArray, fixed connection count structures can efficiently implement event-driven synaptic transmission."
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-21T09:07:04.965721Z",
     "start_time": "2025-10-21T09:07:04.934442Z"
    }
   },
   "source": "# Using FixedPostNumConn for forward propagation\nprint(\"=\" * 60)\nprint(\"Testing FixedPostNumConn\")\nprint(\"=\" * 60)\n\n# Generate input spikes (must match fixed_post's input dimension: 100)\nbrainstate.random.seed(999)\nspikes_bool = brainstate.random.bernoulli(0.1, size=(100,))  # Using n_pre=100\nspikes = brainevent.BinaryArray(spikes_bool)\n\nprint(f\"\\nInput spikes: {spikes.sum()} / 100 fired\")\n\n# Event-driven matrix multiplication\noutput = spikes @ fixed_post\n\nprint(f\"\\nOutput statistics:\")\nprint(f\"  shape: {output.shape}\")\nprint(f\"  maximum value: {output.max():.4f}\")\nprint(f\"  minimum value: {output.min():.4f}\")\nprint(f\"  average value: {output.mean():.4f}\")\nprint(f\"  non-zero outputs: {jnp.sum(output != 0)}\")",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "Testing FixedPostNumConn\n",
      "============================================================\n",
      "\n",
      "Input spikes: 8 / 100 fired\n",
      "\n",
      "Output statistics:\n",
      "  shape: (50,)\n",
      "  maximum value: 0.4428\n",
      "  minimum value: -0.2197\n",
      "  average value: 0.0296\n",
      "  non-zero outputs: 39\n"
     ]
    }
   ],
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-21T09:07:07.160406Z",
     "start_time": "2025-10-21T09:07:07.141799Z"
    }
   },
   "source": "# Using FixedPreNumConn for forward propagation\nprint(\"=\" * 60)\nprint(\"Testing FixedPreNumConn\")\nprint(\"=\" * 60)\n\n# Generate input spikes (matching fixed_pre's size)\nbrainstate.random.seed(888)\nspikes_bool = brainstate.random.bernoulli(0.05, size=(200,))\nspikes = brainevent.BinaryArray(spikes_bool)\n\nprint(f\"\\nInput spikes: {spikes.sum()} / 200 fired\")\n\n# Event-driven matrix multiplication\noutput = spikes @ fixed_pre\n\nprint(f\"\\nOutput statistics:\")\nprint(f\"  shape: {output.shape}\")\nprint(f\"  maximum value: {output.max():.4f}\")\nprint(f\"  minimum value: {output.min():.4f}\")\nprint(f\"  average value: {output.mean():.4f}\")\nprint(f\"  non-zero outputs: {jnp.sum(output != 0)}\")",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "Testing FixedPreNumConn\n",
      "============================================================\n",
      "\n",
      "Input spikes: 7 / 200 fired\n",
      "\n",
      "Output statistics:\n",
      "  shape: (100,)\n",
      "  maximum value: 0.0901\n",
      "  minimum value: -0.1044\n",
      "  average value: 0.0005\n",
      "  non-zero outputs: 41\n"
     ]
    }
   ],
   "execution_count": 15
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 5. Practice: Biologically realistic cortical network\n\nLet's build a network simulating the cerebral cortex, using fixed connection counts to reflect real neuron characteristics."
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-21T09:07:11.662385Z",
     "start_time": "2025-10-21T09:07:11.652920Z"
    }
   },
   "source": "class CorticalNetwork:\n    \"\"\"Cortical network simulation using fixed connection count structures\"\"\"\n\n    def __init__(self,\n                 n_exc=800,      # Number of excitatory neurons (80%)\n                 n_inh=200,      # Number of inhibitory neurons (20%)\n                 exc_fanout=50,  # Output connections per excitatory neuron\n                 inh_fanout=30,  # Output connections per inhibitory neuron\n                 seed=0):\n        \"\"\"\n        Create a biologically realistic cortical network\n\n        References:\n        - Excitatory neurons account for approximately 80% in cortex, inhibitory ~20%\n        - Each pyramidal neuron has approximately thousands of synaptic connections\n        \"\"\"\n        self.n_exc = n_exc\n        self.n_inh = n_inh\n        self.n_total = n_exc + n_inh\n\n        brainstate.random.seed(seed)\n\n        # Excitatory -> Excitatory connections\n        exc_to_exc_idx = brainstate.random.randint(0, n_exc, size=(n_exc, exc_fanout))\n        exc_to_exc_w = brainstate.random.normal(size=(n_exc, exc_fanout)) * 0.05\n        self.w_ee = brainevent.FixedPostNumConn((exc_to_exc_w, exc_to_exc_idx), shape=(n_exc, n_exc))\n\n        # Excitatory -> Inhibitory connections\n        exc_to_inh_idx = brainstate.random.randint(0, n_inh, size=(n_exc, exc_fanout // 2))\n        exc_to_inh_w = brainstate.random.normal(size=(n_exc, exc_fanout // 2)) * 0.08\n        self.w_ei = brainevent.FixedPostNumConn((exc_to_inh_w, exc_to_inh_idx), shape=(n_exc, n_inh))\n\n        # Inhibitory -> Excitatory connections (negative weights)\n        inh_to_exc_idx = brainstate.random.randint(0, n_exc, size=(n_inh, inh_fanout))\n        inh_to_exc_w = -brainstate.random.normal(size=(n_inh, inh_fanout)) * 0.15  # Negative weights\n        self.w_ie = brainevent.FixedPostNumConn((inh_to_exc_w, inh_to_exc_idx), shape=(n_inh, n_exc))\n\n        print(f\"Cortical network structure:\")\n        print(f\"  Total neurons: {self.n_total}\")\n        print(f\"  Excitatory: {n_exc} ({n_exc/self.n_total*100:.0f}%)\")\n        print(f\"  Inhibitory: {n_inh} ({n_inh/self.n_total*100:.0f}%)\")\n        print(f\"\\nConnection statistics:\")\n        print(f\"  E->E: {n_exc * exc_fanout:,} connections\")\n        print(f\"  E->I: {n_exc * exc_fanout // 2:,} connections\")\n        print(f\"  I->E: {n_inh * inh_fanout:,} connections\")\n        print(f\"  Total connections: {n_exc * exc_fanout + n_exc * exc_fanout // 2 + n_inh * inh_fanout:,}\")\n\n    def forward(self, exc_spikes, inh_spikes):\n        \"\"\"Network forward propagation\"\"\"\n        # Input received by excitatory neurons\n        exc_input_from_exc = exc_spikes @ self.w_ee\n        exc_input_from_inh = inh_spikes @ self.w_ie\n        exc_total_input = exc_input_from_exc + exc_input_from_inh\n\n        # Input received by inhibitory neurons\n        inh_input = exc_spikes @ self.w_ei\n\n        return exc_total_input, inh_input\n\n# Create cortical network\ncortical_net = CorticalNetwork(\n    n_exc=800,\n    n_inh=200,\n    exc_fanout=50,\n    inh_fanout=30,\n    seed=2024\n)",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cortical network structure:\n",
      "  Total neurons: 1000\n",
      "  Excitatory: 800 (80%)\n",
      "  Inhibitory: 200 (20%)\n",
      "\n",
      "Connection statistics:\n",
      "  E->E: 40,000 connections\n",
      "  E->I: 20,000 connections\n",
      "  I->E: 6,000 connections\n",
      "  Total connections: 66,000\n"
     ]
    }
   ],
   "execution_count": 17
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-21T09:07:14.788971Z",
     "start_time": "2025-10-21T09:07:14.546774Z"
    }
   },
   "source": "# Simulate network dynamics\nimport braintools\n\nn_steps = 100\nspike_threshold_exc = 0.3  # Lower threshold for excitatory neurons\nspike_threshold_inh = 0.25  # Lower threshold for inhibitory neurons\n\n# Initialize\nbrainstate.random.seed(0)\nexc_spikes = brainevent.BinaryArray(brainstate.random.bernoulli(0.1, size=(800,)))\ninh_spikes = brainevent.BinaryArray(jnp.zeros(200, dtype=bool))\n\n# Record history (complete spike matrix for raster plots)\nexc_spike_matrix = []  # Record first 100 excitatory neurons\ninh_spike_matrix = []  # Record first 100 inhibitory neurons\nexc_spike_counts = []  # Record total spike counts\ninh_spike_counts = []  # Record total spike counts\n\nprint(\"Simulating network dynamics...\\n\")\n\nfor step in range(n_steps):\n    # Forward propagation\n    exc_input, inh_input = cortical_net.forward(exc_spikes, inh_spikes)\n\n    # Add external input (simulating background activity) - increased strength\n    external_input = brainstate.random.normal(size=(800,)) * 0.3\n    exc_input = exc_input + external_input\n\n    # Generate spikes (simple threshold model)\n    exc_spikes = brainevent.BinaryArray(exc_input > spike_threshold_exc)\n    inh_spikes = brainevent.BinaryArray(inh_input > spike_threshold_inh)\n\n    # Record spike counts\n    exc_spike_counts.append(int(exc_spikes.sum()))\n    inh_spike_counts.append(int(inh_spikes.sum()))\n\n    # Record spike matrix (first 100 neurons)\n    exc_spike_matrix.append(np.array(exc_spikes.value[:100], dtype=int))\n    inh_spike_matrix.append(np.array(inh_spikes.value[:100], dtype=int))\n\n# Convert to arrays\nexc_spike_matrix = np.array(exc_spike_matrix)  # (100, 100)\ninh_spike_matrix = np.array(inh_spike_matrix)  # (100, 100)\nts = np.arange(n_steps)\n\n# Visualize network activity using braintools\nfig = plt.figure(figsize=(14, 10))\n\n# Subplot 1: Excitatory neuron raster plot\nax1 = plt.subplot(3, 1, 1)\nbraintools.visualize.raster_plot(\n    ts,\n    exc_spike_matrix,\n    markersize=2,\n    ax=ax1,\n    xlim=(0, n_steps),\n    ylim=(-1, 100),\n    xlabel='Time Step',\n    ylabel='Excitatory Neuron ID (first 100)',\n    title=f'Excitatory Population Spike Raster (Avg rate: {exc_spike_matrix.mean():.2%})',\n    show=False\n)\n\n# Subplot 2: Inhibitory neuron raster plot\nax2 = plt.subplot(3, 1, 2)\nbraintools.visualize.raster_plot(\n    ts,\n    inh_spike_matrix,\n    markersize=2,\n    ax=ax2,\n    xlim=(0, n_steps),\n    ylim=(-1, 100),\n    xlabel='Time Step',\n    ylabel='Inhibitory Neuron ID (first 100)',\n    title=f'Inhibitory Population Spike Raster (Avg rate: {inh_spike_matrix.mean():.2%})',\n    show=False\n)\n\n# Subplot 3: Population firing rates\nax3 = plt.subplot(3, 1, 3)\nexc_rates = np.array(exc_spike_counts) / 800 * 100\ninh_rates = np.array(inh_spike_counts) / 200 * 100\nax3.plot(exc_rates, label='Excitatory (%)', color='red', linewidth=2, alpha=0.8)\nax3.plot(inh_rates, label='Inhibitory (%)', color='blue', linewidth=2, alpha=0.8)\nax3.set_xlabel('Time Step', fontsize=12)\nax3.set_ylabel('Population Firing Rate (%)', fontsize=12)\nax3.set_title('Network Population Activity', fontsize=13, fontweight='bold')\nax3.legend(fontsize=11)\nax3.grid(True, alpha=0.3)\n\nplt.tight_layout()\nplt.show()\n\nprint(f\"\\nSimulation statistics:\")\nprint(f\"  Average excitatory firing rate: {np.mean(exc_rates):.2f}%\")\nprint(f\"  Average inhibitory firing rate: {np.mean(inh_rates):.2f}%\")\nprint(f\"  Excitatory firing range: [{min(exc_spike_counts)}, {max(exc_spike_counts)}]\")\nprint(f\"  Inhibitory firing range: [{min(inh_spike_counts)}, {max(inh_spike_counts)}]\")\nprint(f\"  Excitatory first 100 neurons total spikes: {exc_spike_matrix.sum()}\")\nprint(f\"  Inhibitory first 100 neurons total spikes: {inh_spike_matrix.sum()}\")\n\nprint(f\"\\nNote: Thresholds adjusted to generate realistic activity levels:\")\nprint(f\"  - Excitatory threshold: {spike_threshold_exc} (was 1.0)\")\nprint(f\"  - Inhibitory threshold: {spike_threshold_inh} (was 0.8)\")\nprint(f\"  - External input strength increased to 0.3 (was 0.2)\")",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulating network dynamics...\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1000 with 3 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Simulation statistics:\n",
      "  Average excitatory firing rate: 21.90%\n",
      "  Average inhibitory firing rate: 21.80%\n",
      "  Excitatory firing range: [141, 199]\n",
      "  Inhibitory firing range: [24, 56]\n",
      "  Excitatory first 100 neurons total spikes: 2095\n",
      "  Inhibitory first 100 neurons total spikes: 2118\n",
      "\n",
      "Note: Thresholds adjusted to generate realistic activity levels:\n",
      "  - Excitatory threshold: 0.3 (was 1.0)\n",
      "  - Inhibitory threshold: 0.25 (was 0.8)\n",
      "  - External input strength increased to 0.3 (was 0.2)\n"
     ]
    }
   ],
   "execution_count": 18
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 6. Performance and memory analysis"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-21T09:11:12.164969Z",
     "start_time": "2025-10-21T09:11:12.134340Z"
    }
   },
   "source": "import time\n\n# Compare performance of different data structures\nn_pre = 5000\nn_post = 5000\nnum_conn = 50  # Each presynaptic neuron connects to 50 postsynaptic neurons\n\nprint(f\"Performance test configuration: {n_pre} -> {n_post}, {num_conn} connections per neuron\\n\")\n\n# 1. FixedPostNumConn\nbrainstate.random.seed(0)\nindices_fixed = brainstate.random.randint(0, n_post, size=(n_pre, num_conn))\nweights_fixed = brainstate.random.normal(size=(n_pre, num_conn)) * 0.1\nfixed_conn = brainevent.FixedPostNumConn((weights_fixed, indices_fixed), shape=(n_pre, n_post))\n\n# 2. COO (for comparison - not using CSR because to_csr() is not available)\nrow_indices = jnp.repeat(jnp.arange(n_pre), num_conn)\ncol_indices = indices_fixed.flatten()\ndata = weights_fixed.flatten()\ncoo_conn = brainevent.COO((data, row_indices, col_indices), shape=(n_pre, n_post))\n\n# Generate input\nspikes = brainevent.BinaryArray(brainstate.random.bernoulli(0.02, size=(n_pre,)))\n\nprint(f\"Input spikes: {spikes.sum()} / {n_pre}\\n\")\n\n# Memory comparison\nfixed_memory = fixed_conn.data.nbytes + fixed_conn.indices.nbytes\ncoo_memory = coo_conn.data.nbytes + coo_conn.row.nbytes + coo_conn.col.nbytes\n\nprint(\"Memory usage:\")\nprint(f\"  FixedPostNumConn: {fixed_memory / 1024 / 1024:.2f} MB\")\nprint(f\"  COO:              {coo_memory / 1024 / 1024:.2f} MB\")\nprint(f\"  Ratio:            {coo_memory / fixed_memory:.2f}x\\n\")\n\n# Warm-up\n_ = jax.block_until_ready(spikes @ fixed_conn)\n_ = jax.block_until_ready(spikes @ coo_conn)\n\nn_trials = 100\n\n# FixedPostNumConn performance\nstart = time.time()\nfor _ in range(n_trials):\n    result_fixed = jax.block_until_ready(spikes @ fixed_conn)\nfixed_time = (time.time() - start) / n_trials\n\n# COO performance\nstart = time.time()\nfor _ in range(n_trials):\n    result_coo = jax.block_until_ready(spikes @ coo_conn)\ncoo_time = (time.time() - start) / n_trials\n\nprint(\"Computation performance (average time):\")\nprint(f\"  FixedPostNumConn: {fixed_time*1000:.3f} ms\")\nprint(f\"  COO:              {coo_time*1000:.3f} ms\")\n\n# Avoid division by zero error\nif fixed_time > 0:\n    print(f\"  Speed ratio:      {coo_time/fixed_time:.2f}x\")\nelse:\n    print(f\"  Speed ratio:      FixedPostNumConn is too fast to measure accurately (< {1/n_trials*1000:.3f} ms)\")\n\nprint(f\"\\nResult consistency: {jnp.allclose(result_fixed, result_coo, atol=1e-5)}\")",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance test configuration: 5000 -> 5000, 50 connections per neuron\n",
      "\n",
      "Input spikes: 88 / 5000\n",
      "\n",
      "Memory usage:\n",
      "  FixedPostNumConn: 1.91 MB\n",
      "  COO:              2.86 MB\n",
      "  Ratio:            1.50x\n",
      "\n",
      "Computation performance (average time):\n",
      "  FixedPostNumConn: 0.000 ms\n",
      "  COO:              0.000 ms\n",
      "  Speed ratio:      FixedPostNumConn is too fast to measure accurately (< 10.000 ms)\n",
      "\n",
      "Result consistency: True\n"
     ]
    }
   ],
   "execution_count": 20
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 7. Usage recommendations\n\n### FixedPostNumConn vs FixedPreNumConn?\n\n**Use FixedPostNumConn when:**\n- ✅ Each presynaptic neuron sends out a fixed number of connections\n- ✅ Row-wise access is needed (standard forward propagation)\n- ✅ Most spiking neural network applications\n\n**Use FixedPreNumConn when:**\n- ✅ Each postsynaptic neuron receives a fixed number of connections\n- ✅ Column-wise access is needed\n- ✅ Specific biological constraints (e.g., fixed dendritic spine count)\n\n### Selection among other structures\n\n- **Small-scale, arbitrary connections**: Use **CSR/COO**\n- **Ultra-large-scale, random connections**: Use **JIT connections**\n- **Biologically realistic, fixed connection counts**: Use **FixedPostNumConn/FixedPreNumConn**\n- **Dense connections**: Use **regular arrays**"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Summary\n",
    "\n",
    "In this tutorial, we learned:\n",
    "\n",
    "1. ✅ **Biological significance**: Fixed connection counts match real neural network characteristics\n",
    "2. ✅ **FixedPostNumConn**: Fixed output connections per presynaptic neuron\n",
    "3. ✅ **FixedPreNumConn**: Fixed input connections per postsynaptic neuron\n",
    "4. ✅ **Creation and usage**: How to build fixed connection count structures\n",
    "5. ✅ **Using with BinaryArray**: Efficient event-driven computation\n",
    "6. ✅ **Practical application**: Building biologically realistic cortical networks\n",
    "7. ✅ **Performance analysis**: Comparison with other data structures\n",
    "\n",
    "### Key takeaways\n",
    "- 🔑 **Fixed connection counts = Biological realism**\n",
    "- 🔑 **Regular data structures = Efficient computation**\n",
    "- 🔑 **Suitable for medium-scale biological modeling**\n",
    "\n",
    "## Next Steps\n",
    "\n",
    "In the next tutorial, we will learn:\n",
    "- 📚 **Tutorial 5**: Synaptic plasticity modeling - STDP, short-term plasticity\n",
    "- Learn how to implement learning and memory in BrainEvent\n",
    "- Build neural networks with adaptive capabilities"
   ]
  }
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