{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 2: Spatial Connectivity\n",
    "\n",
    "This tutorial explores spatial connectivity patterns in `braintools.conn`. You'll learn how to create distance-dependent connections, work with neuron positions, and model spatially-organized neural networks.\n",
    "\n",
    "## 1. Introduction to Spatial Connectivity <a name=\"introduction\"></a>\n",
    "\n",
    "Real neural circuits are embedded in physical space, and connectivity often depends on spatial relationships between neurons.\n",
    "\n",
    "### Why Spatial Connectivity Matters\n",
    "\n",
    "- **Biological Realism**: Real neurons connect based on proximity and spatial organization\n",
    "- **Local vs. Global Processing**: Nearby neurons process local features; distant neurons integrate information\n",
    "- **Wiring Cost**: Brains minimize connection lengths to reduce metabolic cost\n",
    "- **Topographic Maps**: Sensory and motor systems maintain spatial relationships (e.g., retinotopy, somatotopy)\n",
    "\n",
    "Let's start by importing the necessary libraries:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-01T16:09:57.835924Z",
     "start_time": "2025-10-01T16:09:54.175823Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import brainunit as u\n",
    "from braintools import conn, visualize as vis\n",
    "from braintools.init import Constant, Normal, Uniform, GaussianProfile, ExponentialProfile\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "np.random.seed(42)\n",
    "\n",
    "# Configure matplotlib\n",
    "plt.rcParams['figure.figsize'] = (12, 4)\n",
    "plt.rcParams['font.size'] = 10\n",
    "\n",
    "print(\"✓ Imports successful\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ Imports successful\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 2. Working with Neuron Positions <a name=\"positions\"></a>\n",
    "\n",
    "Spatial connectivity requires neuron positions. `braintools.conn` uses position arrays with physical units.\n",
    "\n",
    "### 2.1 Creating Position Arrays\n",
    "\n",
    "Positions are typically 2D or 3D arrays with shape `(n_neurons, n_dimensions)`:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-01T16:09:58.110944Z",
     "start_time": "2025-10-01T16:09:57.886061Z"
    }
   },
   "source": [
    "# Random 2D positions\n",
    "n_neurons = 100\n",
    "positions_2d = np.random.uniform(0, 1000, (n_neurons, 2)) * u.um\n",
    "\n",
    "print(\"2D Positions:\")\n",
    "print(f\"  Shape: {positions_2d.shape}\")\n",
    "print(f\"  Units: {u.get_unit(positions_2d)}\")\n",
    "print(f\"  First 5 neurons:\\n{positions_2d[:5]}\")\n",
    "\n",
    "# 1D positions (e.g., for linear arrays)\n",
    "positions_1d = np.linspace(0, 1000, n_neurons).reshape(-1, 1) * u.um\n",
    "\n",
    "print(f\"\\n1D Positions:\")\n",
    "print(f\"  Shape: {positions_1d.shape}\")\n",
    "\n",
    "# 3D positions (e.g., for cortical volumes)\n",
    "positions_3d = np.random.randn(n_neurons, 3) * np.array([100, 100, 200]) * u.um\n",
    "\n",
    "print(f\"\\n3D Positions:\")\n",
    "print(f\"  Shape: {positions_3d.shape}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2D Positions:\n",
      "  Shape: (100, 2)\n",
      "  Units: um\n",
      "  First 5 neurons:\n",
      "ArrayImpl([[374.54013062, 950.71429443],\n",
      "           [731.99395752, 598.6585083],\n",
      "           [156.01864624, 155.99452209],\n",
      "           [ 58.08361053, 866.17614746],\n",
      "           [601.11499023, 708.0725708]], dtype=float32) * umetre\n",
      "\n",
      "1D Positions:\n",
      "  Shape: (100, 1)\n",
      "\n",
      "3D Positions:\n",
      "  Shape: (100, 3)\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Visualizing Neuron Positions"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-01T16:10:02.260603Z",
     "start_time": "2025-10-01T16:10:02.053380Z"
    }
   },
   "source": [
    "# Visualize 2D positions\n",
    "pos_2d_vals = u.get_mantissa(positions_2d)\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n",
    "\n",
    "# Random distribution\n",
    "axes[0].scatter(pos_2d_vals[:, 0], pos_2d_vals[:, 1], alpha=0.6, s=50)\n",
    "axes[0].set_xlabel('X position (μm)')\n",
    "axes[0].set_ylabel('Y position (μm)')\n",
    "axes[0].set_title('Random 2D Neuron Positions')\n",
    "axes[0].grid(True, alpha=0.3)\n",
    "axes[0].set_aspect('equal')\n",
    "\n",
    "# Grid distribution (we'll create this)\n",
    "grid_size = 10\n",
    "x = np.linspace(0, 900, grid_size)\n",
    "y = np.linspace(0, 900, grid_size)\n",
    "xx, yy = np.meshgrid(x, y)\n",
    "positions_grid = np.stack([xx.flatten(), yy.flatten()], axis=1) * u.um\n",
    "pos_grid_vals = u.get_mantissa(positions_grid)\n",
    "\n",
    "axes[1].scatter(pos_grid_vals[:, 0], pos_grid_vals[:, 1], alpha=0.6, s=50, color='orange')\n",
    "axes[1].set_xlabel('X position (μm)')\n",
    "axes[1].set_ylabel('Y position (μm)')\n",
    "axes[1].set_title('Grid 2D Neuron Positions')\n",
    "axes[1].grid(True, alpha=0.3)\n",
    "axes[1].set_aspect('equal')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 3. Distance-Dependent Connectivity <a name=\"distance-dependent\"></a>\n",
    "\n",
    "Distance-dependent connectivity creates connections with probability that depends on spatial distance.\n",
    "\n",
    "### 3.1 Gaussian Distance Profile\n",
    "\n",
    "Gaussian profiles create smooth, locally-biased connectivity:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-01T16:10:06.518088Z",
     "start_time": "2025-10-01T16:10:06.477217Z"
    }
   },
   "source": [
    "# Create positions\n",
    "n_neurons = 200\n",
    "positions = np.random.uniform(0, 1000, (n_neurons, 2)) * u.um\n",
    "\n",
    "# Gaussian distance-dependent connectivity\n",
    "gaussian_conn = conn.Gaussian(\n",
    "    distance_profile=GaussianProfile(\n",
    "        sigma=100 * u.um,          # Connection probability peaks at zero distance\n",
    "        max_distance=300 * u.um    # No connections beyond this distance\n",
    "    ),\n",
    "    weight=Constant(1.0 * u.nS),\n",
    "    seed=42\n",
    ")\n",
    "\n",
    "result_gaussian = gaussian_conn(\n",
    "    pre_size=n_neurons,\n",
    "    post_size=n_neurons,\n",
    "    pre_positions=positions,\n",
    "    post_positions=positions\n",
    ")\n",
    "\n",
    "print(\"Gaussian Distance-Dependent Connectivity:\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"Neurons: {n_neurons}\")\n",
    "print(f\"Connections: {len(result_gaussian.pre_indices)}\")\n",
    "print(f\"Sigma: 100 μm\")\n",
    "print(f\"Max distance: 300 μm\")\n",
    "print(f\"Average connections per neuron: {len(result_gaussian.pre_indices) / n_neurons:.1f}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Distance-Dependent Connectivity:\n",
      "==================================================\n",
      "Neurons: 200\n",
      "Connections: 2520\n",
      "Sigma: 100 μm\n",
      "Max distance: 300 μm\n",
      "Average connections per neuron: 12.6\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Exponential Distance Profile\n",
    "\n",
    "Exponential profiles create connections that decay exponentially with distance:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-01T16:10:22.767389Z",
     "start_time": "2025-10-01T16:10:22.740026Z"
    }
   },
   "source": [
    "# Exponential distance-dependent connectivity\n",
    "exponential_conn = conn.Exponential(\n",
    "    distance_profile=ExponentialProfile(\n",
    "        150 * u.um,          # Decay length scale\n",
    "        max_distance=500 * u.um    # Maximum connection distance\n",
    "    ),\n",
    "    weight=Constant(1.5 * u.nS),\n",
    "    seed=42\n",
    ")\n",
    "\n",
    "result_exponential = exponential_conn(\n",
    "    pre_size=n_neurons,\n",
    "    post_size=n_neurons,\n",
    "    pre_positions=positions,\n",
    "    post_positions=positions\n",
    ")\n",
    "\n",
    "print(\"Exponential Distance-Dependent Connectivity:\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"Neurons: {n_neurons}\")\n",
    "print(f\"Connections: {len(result_exponential.pre_indices)}\")\n",
    "print(f\"Scale: 150 μm\")\n",
    "print(f\"Max distance: 500 μm\")\n",
    "print(f\"Average connections per neuron: {len(result_exponential.pre_indices) / n_neurons:.1f}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exponential Distance-Dependent Connectivity:\n",
      "==================================================\n",
      "Neurons: 200\n",
      "Connections: 3936\n",
      "Scale: 150 μm\n",
      "Max distance: 500 μm\n",
      "Average connections per neuron: 19.7\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Comparing Distance Profiles\n",
    "\n",
    "Let's visualize how connection probability varies with distance:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-01T16:10:25.829731Z",
     "start_time": "2025-10-01T16:10:25.687761Z"
    }
   },
   "source": [
    "# Create distance arrays for plotting\n",
    "distances = np.linspace(0, 500, 1000) * u.um\n",
    "dist_vals = u.get_mantissa(distances)\n",
    "\n",
    "# Gaussian profile\n",
    "sigma = 100\n",
    "gaussian_prob = np.exp(-(dist_vals ** 2) / (2 * sigma ** 2))\n",
    "gaussian_prob[dist_vals > 300] = 0  # Max distance cutoff\n",
    "\n",
    "# Exponential profile\n",
    "scale = 150\n",
    "exponential_prob = np.exp(-dist_vals / scale)\n",
    "exponential_prob[dist_vals > 500] = 0  # Max distance cutoff\n",
    "\n",
    "# Plot comparison\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "ax.plot(dist_vals, gaussian_prob, label='Gaussian (σ=100μm, max=300μm)', \n",
    "        linewidth=2, color='blue')\n",
    "ax.plot(dist_vals, exponential_prob, label='Exponential (scale=150μm, max=500μm)', \n",
    "        linewidth=2, color='orange')\n",
    "\n",
    "ax.axvline(100, color='blue', linestyle='--', alpha=0.5, label='Gaussian σ')\n",
    "ax.axvline(150, color='orange', linestyle='--', alpha=0.5, label='Exponential scale')\n",
    "ax.axvline(300, color='blue', linestyle=':', alpha=0.5, label='Gaussian max')\n",
    "ax.axvline(500, color='orange', linestyle=':', alpha=0.5, label='Exponential max')\n",
    "\n",
    "ax.set_xlabel('Distance (μm)', fontsize=12)\n",
    "ax.set_ylabel('Connection Probability', fontsize=12)\n",
    "ax.set_title('Distance Profiles Comparison', fontsize=14)\n",
    "ax.legend()\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nKey Differences:\")\n",
    "print(\"- Gaussian: Smooth, bell-shaped, stronger local bias\")\n",
    "print(\"- Exponential: Slower decay, allows more long-range connections\")"
   ],
   "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": [
      "\n",
      "Key Differences:\n",
      "- Gaussian: Smooth, bell-shaped, stronger local bias\n",
      "- Exponential: Slower decay, allows more long-range connections\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Connection Distance Distribution\n",
    "\n",
    "Let's analyze the actual distances of connections created:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-01T16:11:12.703196Z",
     "start_time": "2025-10-01T16:11:12.498019Z"
    }
   },
   "source": [
    "# Calculate connection distances\n",
    "def calculate_connection_distances(result, positions):\n",
    "    \"\"\"Calculate Euclidean distances for all connections.\"\"\"\n",
    "    pos_vals = u.get_mantissa(positions)\n",
    "    pre_pos = pos_vals[result.pre_indices]\n",
    "    post_pos = pos_vals[result.post_indices]\n",
    "    distances = np.sqrt(np.sum((pre_pos - post_pos) ** 2, axis=1))\n",
    "    return distances\n",
    "\n",
    "# Calculate distances for both profiles\n",
    "distances_gaussian = calculate_connection_distances(result_gaussian, positions)\n",
    "distances_exponential = calculate_connection_distances(result_exponential, positions)\n",
    "\n",
    "# Plot distributions\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "vis.distribution_plot(\n",
    "    distances_gaussian,\n",
    "    bins=30,\n",
    "    alpha=0.7,\n",
    "    colors=['blue',],\n",
    "    edgecolor='black',\n",
    "    ax=axes[0],\n",
    "    xlabel='Connection Distance (μm)',\n",
    "    title='Gaussian Profile Connection Distances'\n",
    ")\n",
    "\n",
    "vis.distribution_plot(\n",
    "    distances_exponential,\n",
    "    bins=30,\n",
    "    alpha=0.7,\n",
    "    colors=['orange',],\n",
    "    edgecolor='black',\n",
    "    ax=axes[1],\n",
    "    xlabel='Connection Distance (μm)',\n",
    "    title='Exponential Profile Connection Distances'\n",
    ")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"\\nGaussian - Mean distance: {np.mean(distances_gaussian):.1f} μm\")\n",
    "print(f\"Exponential - Mean distance: {np.mean(distances_exponential):.1f} μm\")"
   ],
   "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
     }
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gaussian - Mean distance: 109.7 μm\n",
      "Exponential - Mean distance: 191.0 μm\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 4. Spatial Topologies <a name=\"topologies\"></a>\n",
    "\n",
    "Spatial topologies impose regular connectivity patterns based on position in space.\n",
    "\n",
    "### 4.1 Ring Topology\n",
    "\n",
    "Ring topology connects neurons in a circular arrangement, useful for modeling orientation preference or phase:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-01T16:11:16.713215Z",
     "start_time": "2025-10-01T16:11:16.689988Z"
    }
   },
   "source": [
    "# Create ring connectivity\n",
    "n_neurons_ring = 100\n",
    "\n",
    "ring_conn = conn.Ring(\n",
    "    neighbors=3,  # Connect to 3 neighbors on each side\n",
    "    weight=Constant(1.0 * u.nS),\n",
    "    bidirectional=True\n",
    ")\n",
    "\n",
    "result_ring = ring_conn(pre_size=n_neurons_ring, post_size=n_neurons_ring)\n",
    "\n",
    "print(\"Ring Topology:\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"Neurons: {n_neurons_ring}\")\n",
    "print(f\"Neighbors on each side: 3\")\n",
    "print(f\"Bidirectional: Yes\")\n",
    "print(f\"Total connections: {len(result_ring.pre_indices)}\")\n",
    "print(f\"Expected: {n_neurons_ring * 3 * 2} (100 neurons × 3 neighbors × 2 directions)\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ring Topology:\n",
      "==================================================\n",
      "Neurons: 100\n",
      "Neighbors on each side: 3\n",
      "Bidirectional: Yes\n",
      "Total connections: 600\n",
      "Expected: 600 (100 neurons × 3 neighbors × 2 directions)\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Grid2D Topology\n",
    "\n",
    "2D grid connectivity connects neurons to their spatial neighbors:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-01T16:11:20.093883Z",
     "start_time": "2025-10-01T16:11:20.048434Z"
    }
   },
   "source": [
    "# Von Neumann neighborhood (4 neighbors)\n",
    "grid_von_neumann = conn.Grid2d(\n",
    "    connectivity='von_neumann',\n",
    "    weight=Constant(1.0 * u.nS),\n",
    "    periodic=False\n",
    ")\n",
    "\n",
    "result_von_neumann = grid_von_neumann(pre_size=(10, 10), post_size=(10, 10))\n",
    "\n",
    "print(\"Grid2D - Von Neumann (4-connected):\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"Grid size: 10 × 10 = 100 neurons\")\n",
    "print(f\"Connectivity: Von Neumann (up, down, left, right)\")\n",
    "print(f\"Periodic boundaries: No\")\n",
    "print(f\"Total connections: {len(result_von_neumann.pre_indices)}\")\n",
    "\n",
    "# Moore neighborhood (8 neighbors)\n",
    "grid_moore = conn.Grid2d(\n",
    "    connectivity='moore',\n",
    "    weight=Constant(1.5 * u.nS),\n",
    "    periodic=False\n",
    ")\n",
    "\n",
    "result_moore = grid_moore(pre_size=(10, 10), post_size=(10, 10))\n",
    "\n",
    "print(f\"\\nGrid2D - Moore (8-connected):\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"Grid size: 10 × 10 = 100 neurons\")\n",
    "print(f\"Connectivity: Moore (all 8 neighbors)\")\n",
    "print(f\"Periodic boundaries: No\")\n",
    "print(f\"Total connections: {len(result_moore.pre_indices)}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid2D - Von Neumann (4-connected):\n",
      "==================================================\n",
      "Grid size: 10 × 10 = 100 neurons\n",
      "Connectivity: Von Neumann (up, down, left, right)\n",
      "Periodic boundaries: No\n",
      "Total connections: 360\n",
      "\n",
      "Grid2D - Moore (8-connected):\n",
      "==================================================\n",
      "Grid size: 10 × 10 = 100 neurons\n",
      "Connectivity: Moore (all 8 neighbors)\n",
      "Periodic boundaries: No\n",
      "Total connections: 684\n"
     ]
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Periodic vs. Non-Periodic Boundaries\n",
    "\n",
    "Periodic boundaries create a torus topology (wraparound edges):"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-01T16:11:23.868860Z",
     "start_time": "2025-10-01T16:11:23.861357Z"
    }
   },
   "source": [
    "# Non-periodic (has edges)\n",
    "grid_nonperiodic = conn.Grid2d(\n",
    "    connectivity='moore',\n",
    "    periodic=False\n",
    ")\n",
    "result_nonperiodic = grid_nonperiodic(pre_size=(8, 8), post_size=(8, 8))\n",
    "\n",
    "# Periodic (torus)\n",
    "grid_periodic = conn.Grid2d(\n",
    "    connectivity='moore',\n",
    "    periodic=True\n",
    ")\n",
    "result_periodic = grid_periodic(pre_size=(8, 8), post_size=(8, 8))\n",
    "\n",
    "# Calculate in-degrees to see edge effects\n",
    "in_degree_nonperiodic = np.bincount(result_nonperiodic.post_indices, minlength=64)\n",
    "in_degree_periodic = np.bincount(result_periodic.post_indices, minlength=64)\n",
    "\n",
    "print(\"Periodic vs. Non-Periodic Boundaries:\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"Non-periodic (edges):\")\n",
    "print(f\"  Total connections: {len(result_nonperiodic.pre_indices)}\")\n",
    "print(f\"  Min in-degree: {np.min(in_degree_nonperiodic)} (corner neurons)\")\n",
    "print(f\"  Max in-degree: {np.max(in_degree_nonperiodic)} (interior neurons)\")\n",
    "print(f\"  Mean in-degree: {np.mean(in_degree_nonperiodic):.1f}\")\n",
    "\n",
    "print(f\"\\nPeriodic (torus):\")\n",
    "print(f\"  Total connections: {len(result_periodic.pre_indices)}\")\n",
    "print(f\"  Min in-degree: {np.min(in_degree_periodic)}\")\n",
    "print(f\"  Max in-degree: {np.max(in_degree_periodic)}\")\n",
    "print(f\"  Mean in-degree: {np.mean(in_degree_periodic):.1f}\")\n",
    "print(f\"  All neurons have exactly 8 connections!\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Periodic vs. Non-Periodic Boundaries:\n",
      "==================================================\n",
      "Non-periodic (edges):\n",
      "  Total connections: 420\n",
      "  Min in-degree: 3 (corner neurons)\n",
      "  Max in-degree: 8 (interior neurons)\n",
      "  Mean in-degree: 6.6\n",
      "\n",
      "Periodic (torus):\n",
      "  Total connections: 512\n",
      "  Min in-degree: 8\n",
      "  Max in-degree: 8\n",
      "  Mean in-degree: 8.0\n",
      "  All neurons have exactly 8 connections!\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 5. Clustered Connectivity Patterns <a name=\"clustered\"></a>\n",
    "\n",
    "Clustered connectivity creates spatially-localized groups with enhanced connectivity.\n",
    "\n",
    "### 5.1 Clustered Random Connectivity\n",
    "\n",
    "ClusteredRandom enhances connection probability within a spatial radius:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-01T16:11:28.568356Z",
     "start_time": "2025-10-01T16:11:28.518509Z"
    }
   },
   "source": [
    "# Create positions\n",
    "n_neurons = 300\n",
    "positions = np.random.uniform(0, 1000, (n_neurons, 2)) * u.um\n",
    "\n",
    "# Clustered random connectivity\n",
    "clustered_conn = conn.ClusteredRandom(\n",
    "    prob=0.05,                    # Baseline probability (5%)\n",
    "    cluster_radius=100 * u.um,    # Radius of clustering\n",
    "    cluster_factor=5.0,           # 5× enhancement within radius\n",
    "    weight=Constant(1.0 * u.nS),\n",
    "    seed=42\n",
    ")\n",
    "\n",
    "result_clustered = clustered_conn(\n",
    "    pre_size=n_neurons,\n",
    "    post_size=n_neurons,\n",
    "    pre_positions=positions,\n",
    "    post_positions=positions\n",
    ")\n",
    "\n",
    "print(\"Clustered Random Connectivity:\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"Neurons: {n_neurons}\")\n",
    "print(f\"Baseline probability: 5%\")\n",
    "print(f\"Cluster radius: 100 μm\")\n",
    "print(f\"Enhancement factor: 5×\")\n",
    "print(f\"Effective probability within clusters: {0.05 * 5 * 100:.0f}%\")\n",
    "print(f\"Total connections: {len(result_clustered.pre_indices)}\")\n",
    "\n",
    "# Compare with pure random\n",
    "random_conn = conn.Random(prob=0.05, seed=42)\n",
    "result_random = random_conn(pre_size=n_neurons, post_size=n_neurons)\n",
    "\n",
    "print(f\"\\nComparison with pure random (prob=0.05):\")\n",
    "print(f\"  Pure random connections: {len(result_random.pre_indices)}\")\n",
    "print(f\"  Clustered connections: {len(result_clustered.pre_indices)}\")\n",
    "print(f\"  Increase: {(len(result_clustered.pre_indices) / len(result_random.pre_indices) - 1) * 100:.1f}%\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clustered Random Connectivity:\n",
      "==================================================\n",
      "Neurons: 300\n",
      "Baseline probability: 5%\n",
      "Cluster radius: 100 μm\n",
      "Enhancement factor: 5×\n",
      "Effective probability within clusters: 25%\n",
      "Total connections: 5034\n",
      "\n",
      "Comparison with pure random (prob=0.05):\n",
      "  Pure random connections: 4444\n",
      "  Clustered connections: 5034\n",
      "  Increase: 13.3%\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Radial Patches\n",
    "\n",
    "RadialPatches creates multiple discrete patches of connectivity:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-01T16:11:31.583435Z",
     "start_time": "2025-10-01T16:11:31.555551Z"
    }
   },
   "source": [
    "# Radial patches connectivity\n",
    "patches_conn = conn.RadialPatches(\n",
    "    patch_radius=60 * u.um,      # Radius of each patch\n",
    "    n_patches=3,                 # Number of patches per neuron\n",
    "    prob=0.7,                    # Connection probability within patches\n",
    "    weight=Normal(mean=1.5 * u.nS, std=0.3 * u.nS),\n",
    "    seed=42\n",
    ")\n",
    "\n",
    "result_patches = patches_conn(\n",
    "    pre_size=n_neurons,\n",
    "    post_size=n_neurons,\n",
    "    pre_positions=positions,\n",
    "    post_positions=positions\n",
    ")\n",
    "\n",
    "print(\"Radial Patches Connectivity:\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"Neurons: {n_neurons}\")\n",
    "print(f\"Patches per neuron: 3\")\n",
    "print(f\"Patch radius: 60 μm\")\n",
    "print(f\"Connection prob within patches: 70%\")\n",
    "print(f\"Total connections: {len(result_patches.pre_indices)}\")\n",
    "print(f\"Average connections per neuron: {len(result_patches.pre_indices) / n_neurons:.1f}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Radial Patches Connectivity:\n",
      "==================================================\n",
      "Neurons: 300\n",
      "Patches per neuron: 3\n",
      "Patch radius: 60 μm\n",
      "Connection prob within patches: 70%\n",
      "Total connections: 2750\n",
      "Average connections per neuron: 9.2\n"
     ]
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 Comparing Clustered Patterns\n",
    "\n",
    "Let's compare the distance distributions:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-01T16:12:05.346615Z",
     "start_time": "2025-10-01T16:12:05.046099Z"
    }
   },
   "source": [
    "# Calculate distances for each pattern\n",
    "distances_random = calculate_connection_distances(result_random, positions)\n",
    "distances_clustered = calculate_connection_distances(result_clustered, positions)\n",
    "distances_patches = calculate_connection_distances(result_patches, positions)\n",
    "\n",
    "# Plot comparison\n",
    "fig, axes = plt.subplots(1, 3, figsize=(16, 5))\n",
    "\n",
    "vis.distribution_plot(\n",
    "    distances_random,\n",
    "    bins=30,\n",
    "    alpha=0.7,\n",
    "    colors=['gray'],\n",
    "    edgecolor='black',\n",
    "    ax=axes[0],\n",
    "    xlabel='Connection Distance (μm)',\n",
    "    title='Pure Random'\n",
    ")\n",
    "\n",
    "vis.distribution_plot(\n",
    "    distances_clustered,\n",
    "    bins=30,\n",
    "    alpha=0.7,\n",
    "    colors=['purple'],\n",
    "    edgecolor='black',\n",
    "    ax=axes[1],\n",
    "    xlabel='Connection Distance (μm)',\n",
    "    title='Clustered Random'\n",
    ")\n",
    "\n",
    "vis.distribution_plot(\n",
    "    distances_patches,\n",
    "    bins=30,\n",
    "    alpha=0.7,\n",
    "    colors=['green'],\n",
    "    edgecolor='black',\n",
    "    ax=axes[2],\n",
    "    xlabel='Connection Distance (μm)',\n",
    "    title='Radial Patches'\n",
    ")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nMean Connection Distances:\")\n",
    "print(f\"  Pure Random: {np.mean(distances_random):.1f} μm\")\n",
    "print(f\"  Clustered Random: {np.mean(distances_clustered):.1f} μm\")\n",
    "print(f\"  Radial Patches: {np.mean(distances_patches):.1f} μm\")\n",
    "print(\"\\nNote: Clustered patterns have shorter mean distances (local bias)\")"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x500 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",
      "Mean Connection Distances:\n",
      "  Pure Random: 534.0 μm\n",
      "  Clustered Random: 471.2 μm\n",
      "  Radial Patches: 525.5 μm\n",
      "\n",
      "Note: Clustered patterns have shorter mean distances (local bias)\n"
     ]
    }
   ],
   "execution_count": 14
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 6. Spatial Visualization Techniques <a name=\"visualization\"></a>\n",
    "\n",
    "Visualizing spatial connectivity reveals network structure.\n",
    "\n",
    "### 6.1 Connection Length Maps\n",
    "\n",
    "Visualize how connection distances vary across the network:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-01T16:12:11.034481Z",
     "start_time": "2025-10-01T16:12:10.856263Z"
    }
   },
   "source": [
    "# Calculate mean outgoing connection distance for each neuron\n",
    "def calculate_mean_outgoing_distance(result, positions):\n",
    "    \"\"\"Calculate mean distance of outgoing connections for each neuron.\"\"\"\n",
    "    n_neurons = len(positions)\n",
    "    pos_vals = u.get_mantissa(positions)\n",
    "    mean_distances = np.zeros(n_neurons)\n",
    "    \n",
    "    for i in range(n_neurons):\n",
    "        # Find outgoing connections from neuron i\n",
    "        mask = result.pre_indices == i\n",
    "        if np.any(mask):\n",
    "            post_neurons = result.post_indices[mask]\n",
    "            # Calculate distances\n",
    "            pre_pos = pos_vals[i]\n",
    "            post_pos = pos_vals[post_neurons]\n",
    "            distances = np.sqrt(np.sum((post_pos - pre_pos) ** 2, axis=1))\n",
    "            mean_distances[i] = np.mean(distances)\n",
    "    \n",
    "    return mean_distances\n",
    "\n",
    "# Create a smaller network for visualization\n",
    "n_viz = 150\n",
    "positions_viz = np.random.uniform(0, 1000, (n_viz, 2)) * u.um\n",
    "\n",
    "gaussian_viz = conn.Gaussian(\n",
    "    distance_profile=GaussianProfile(sigma=100 * u.um, max_distance=300 * u.um),\n",
    "    weight=Constant(1.0 * u.nS),\n",
    "    seed=42\n",
    ")\n",
    "result_viz = gaussian_viz(\n",
    "    pre_size=n_viz,\n",
    "    post_size=n_viz,\n",
    "    pre_positions=positions_viz,\n",
    "    post_positions=positions_viz\n",
    ")\n",
    "\n",
    "mean_dists = calculate_mean_outgoing_distance(result_viz, positions_viz)\n",
    "pos_viz_vals = u.get_mantissa(positions_viz)\n",
    "\n",
    "# Plot\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "scatter = ax.scatter(\n",
    "    pos_viz_vals[:, 0],\n",
    "    pos_viz_vals[:, 1],\n",
    "    c=mean_dists,\n",
    "    s=100,\n",
    "    cmap='viridis',\n",
    "    alpha=0.7,\n",
    "    edgecolors='black',\n",
    "    linewidth=0.5\n",
    ")\n",
    "plt.colorbar(scatter, label='Mean Outgoing Connection Distance (μm)', ax=ax)\n",
    "ax.set_xlabel('X Position (μm)', fontsize=12)\n",
    "ax.set_ylabel('Y Position (μm)', fontsize=12)\n",
    "ax.set_title('Spatial Map of Mean Connection Distances', fontsize=14)\n",
    "ax.set_aspect('equal')\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 15
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2 Connection Visualization for Individual Neurons\n",
    "\n",
    "Visualize connections from specific neurons:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-01T16:12:14.994733Z",
     "start_time": "2025-10-01T16:12:14.727587Z"
    }
   },
   "source": [
    "# Select a few neurons to visualize\n",
    "example_neurons = [10, 50, 100]\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
    "\n",
    "for idx, neuron_id in enumerate(example_neurons):\n",
    "    ax = axes[idx]\n",
    "    \n",
    "    # Plot all neurons in gray\n",
    "    ax.scatter(pos_viz_vals[:, 0], pos_viz_vals[:, 1], \n",
    "              c='lightgray', s=30, alpha=0.5, zorder=1)\n",
    "    \n",
    "    # Highlight the source neuron\n",
    "    ax.scatter(pos_viz_vals[neuron_id, 0], pos_viz_vals[neuron_id, 1],\n",
    "              c='red', s=200, marker='*', zorder=3, edgecolors='black', linewidth=1)\n",
    "    \n",
    "    # Find and plot target neurons\n",
    "    mask = result_viz.pre_indices == neuron_id\n",
    "    target_neurons = result_viz.post_indices[mask]\n",
    "    \n",
    "    if len(target_neurons) > 0:\n",
    "        ax.scatter(pos_viz_vals[target_neurons, 0], pos_viz_vals[target_neurons, 1],\n",
    "                  c='blue', s=80, alpha=0.6, zorder=2, edgecolors='black', linewidth=0.5)\n",
    "        \n",
    "        # Draw connection lines\n",
    "        for target in target_neurons:\n",
    "            ax.plot([pos_viz_vals[neuron_id, 0], pos_viz_vals[target, 0]],\n",
    "                   [pos_viz_vals[neuron_id, 1], pos_viz_vals[target, 1]],\n",
    "                   'b-', alpha=0.2, linewidth=0.5, zorder=0)\n",
    "    \n",
    "    ax.set_xlabel('X Position (μm)')\n",
    "    ax.set_ylabel('Y Position (μm)')\n",
    "    ax.set_title(f'Neuron {neuron_id}\\n({len(target_neurons)} connections)')\n",
    "    ax.set_aspect('equal')\n",
    "    ax.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"Red star: Source neuron\")\n",
    "print(\"Blue dots: Target neurons (postsynaptic)\")\n",
    "print(\"Gray dots: Unconnected neurons\")\n",
    "print(\"Blue lines: Connections\")"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x500 with 3 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Red star: Source neuron\n",
      "Blue dots: Target neurons (postsynaptic)\n",
      "Gray dots: Unconnected neurons\n",
      "Blue lines: Connections\n"
     ]
    }
   ],
   "execution_count": 16
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3 Connectivity Matrix for Grid Topology\n",
    "\n",
    "Grid topologies create structured connectivity matrices:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-01T16:12:18.975879Z",
     "start_time": "2025-10-01T16:12:18.779259Z"
    }
   },
   "source": [
    "# Create small grid for visualization\n",
    "grid_small = conn.Grid2d(\n",
    "    connectivity='moore',\n",
    "    weight=Constant(1.0 * u.nS),\n",
    "    periodic=False\n",
    ")\n",
    "\n",
    "result_grid_small = grid_small(pre_size=(12, 12), post_size=(12, 12))\n",
    "\n",
    "# Create connectivity matrix\n",
    "n_total = 144\n",
    "conn_matrix = np.zeros((n_total, n_total))\n",
    "conn_matrix[result_grid_small.pre_indices, result_grid_small.post_indices] = 1\n",
    "\n",
    "# Plot\n",
    "fig, ax = plt.subplots(figsize=(10, 9))\n",
    "vis.connectivity_matrix(\n",
    "    conn_matrix,\n",
    "    cmap='binary',\n",
    "    center_zero=False,\n",
    "    show_colorbar=False,\n",
    "    ax=ax,\n",
    "    title='Grid2D Connectivity Matrix (12×12 Moore Neighborhood)'\n",
    ")\n",
    "\n",
    "# Add grid lines to show structure\n",
    "for i in range(0, n_total, 12):\n",
    "    ax.axhline(i, color='red', linewidth=0.5, alpha=0.3)\n",
    "    ax.axvline(i, color='red', linewidth=0.5, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nThe block-diagonal structure reflects local connectivity.\")\n",
    "print(\"Red lines separate the 12 rows/columns of the 2D grid.\")"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x900 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The block-diagonal structure reflects local connectivity.\n",
      "Red lines separate the 12 rows/columns of the 2D grid.\n"
     ]
    }
   ],
   "execution_count": 17
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.4 Degree Distribution in Spatial Networks\n",
    "\n",
    "Compare degree distributions across different spatial patterns:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-01T16:12:54.132721Z",
     "start_time": "2025-10-01T16:12:53.856249Z"
    }
   },
   "source": [
    "# Calculate in-degrees for different patterns\n",
    "def get_in_degrees(result, n_neurons):\n",
    "    return np.bincount(result.post_indices, minlength=n_neurons)\n",
    "\n",
    "# Use previous results\n",
    "in_deg_gaussian = get_in_degrees(result_gaussian, n_neurons)\n",
    "in_deg_exponential = get_in_degrees(result_exponential, n_neurons)\n",
    "in_deg_clustered = get_in_degrees(result_clustered, n_neurons)\n",
    "\n",
    "# Plot\n",
    "fig, axes = plt.subplots(1, 3, figsize=(16, 5))\n",
    "\n",
    "vis.distribution_plot(\n",
    "    in_deg_gaussian,\n",
    "    bins=20,\n",
    "    alpha=0.7,\n",
    "    colors=['blue'],\n",
    "    edgecolor='black',\n",
    "    ax=axes[0],\n",
    "    xlabel='In-Degree',\n",
    "    title='Gaussian Distance-Dependent'\n",
    ")\n",
    "\n",
    "vis.distribution_plot(\n",
    "    in_deg_exponential,\n",
    "    bins=20,\n",
    "    alpha=0.7,\n",
    "    colors=['orange'],\n",
    "    edgecolor='black',\n",
    "    ax=axes[1],\n",
    "    xlabel='In-Degree',\n",
    "    title='Exponential Distance-Dependent'\n",
    ")\n",
    "\n",
    "vis.distribution_plot(\n",
    "    in_deg_clustered,\n",
    "    bins=20,\n",
    "    alpha=0.7,\n",
    "    colors=['purple'],\n",
    "    edgecolor='black',\n",
    "    ax=axes[2],\n",
    "    xlabel='In-Degree',\n",
    "    title='Clustered Random'\n",
    ")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nDegree distribution characteristics:\")\n",
    "print(f\"Gaussian - CV: {np.std(in_deg_gaussian) / np.mean(in_deg_gaussian):.3f}\")\n",
    "print(f\"Exponential - CV: {np.std(in_deg_exponential) / np.mean(in_deg_exponential):.3f}\")\n",
    "print(f\"Clustered - CV: {np.std(in_deg_clustered) / np.mean(in_deg_clustered):.3f}\")\n",
    "print(\"\\nCV = Coefficient of Variation (std/mean)\")\n",
    "print(\"Higher CV indicates more heterogeneity in connectivity.\")"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x500 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",
      "Degree distribution characteristics:\n",
      "Gaussian - CV: 0.826\n",
      "Exponential - CV: 0.813\n",
      "Clustered - CV: 0.242\n",
      "\n",
      "CV = Coefficient of Variation (std/mean)\n",
      "Higher CV indicates more heterogeneity in connectivity.\n"
     ]
    }
   ],
   "execution_count": 19
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 7. Exercises <a name=\"exercises\"></a>\n",
    "\n",
    "Try these exercises to reinforce your understanding:\n",
    "\n",
    "### Exercise 1: Custom Distance Profile\n",
    "\n",
    "Create a distance-dependent connectivity with a custom distance profile (e.g., Mexican hat):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mexican_hat_profile(distance, sigma_center=50, sigma_surround=150, amplitude_center=1.0, amplitude_surround=0.5):\n",
    "    \"\"\"\n",
    "    Mexican hat (difference of Gaussians) distance profile.\n",
    "    \n",
    "    Creates excitatory center and inhibitory surround.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    distance : array\n",
    "        Distances in μm\n",
    "    sigma_center, sigma_surround : float\n",
    "        Standard deviations of center and surround Gaussians\n",
    "    amplitude_center, amplitude_surround : float\n",
    "        Amplitudes of center and surround\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    probability : array\n",
    "        Connection probability for each distance\n",
    "    \"\"\"\n",
    "    # YOUR CODE HERE\n",
    "    # Hint: DoG = amplitude_center * exp(-d²/2σ_c²) - amplitude_surround * exp(-d²/2σ_s²)\n",
    "    \n",
    "    pass\n",
    "\n",
    "# Test your profile\n",
    "# distances = np.linspace(0, 500, 1000)\n",
    "# probs = mexican_hat_profile(distances)\n",
    "# \n",
    "# plt.figure(figsize=(10, 6))\n",
    "# plt.plot(distances, probs, linewidth=2)\n",
    "# plt.axhline(0, color='black', linestyle='--', alpha=0.3)\n",
    "# plt.xlabel('Distance (μm)')\n",
    "# plt.ylabel('Connection Probability')\n",
    "# plt.title('Mexican Hat Distance Profile')\n",
    "# plt.grid(True, alpha=0.3)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 2: Spatial Clustering Analysis\n",
    "\n",
    "Analyze whether a connectivity pattern exhibits spatial clustering:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze_spatial_clustering(result, positions, n_bins=10):\n",
    "    \"\"\"\n",
    "    Analyze spatial clustering by comparing observed vs. expected distance distribution.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    result : ConnectionResult\n",
    "        Connectivity result\n",
    "    positions : array\n",
    "        Neuron positions\n",
    "    n_bins : int\n",
    "        Number of distance bins\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    clustering_index : float\n",
    "        Positive indicates local clustering, negative indicates anti-clustering\n",
    "    \"\"\"\n",
    "    # YOUR CODE HERE\n",
    "    # Hint:\n",
    "    # 1. Calculate actual connection distances\n",
    "    # 2. Calculate all possible pairwise distances\n",
    "    # 3. Compare distributions (e.g., mean distance ratio)\n",
    "    \n",
    "    pass\n",
    "\n",
    "# Test on different patterns\n",
    "# clustering_gaussian = analyze_spatial_clustering(result_gaussian, positions)\n",
    "# clustering_random = analyze_spatial_clustering(result_random, positions)\n",
    "# \n",
    "# print(f\"Gaussian pattern clustering index: {clustering_gaussian:.3f}\")\n",
    "# print(f\"Random pattern clustering index: {clustering_random:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 3: Topographic Mapping\n",
    "\n",
    "Create a topographic mapping between two populations (e.g., retinotopic projection):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_topographic_map(source_positions, target_positions, sigma, weight_init):\n",
    "    \"\"\"\n",
    "    Create topographic mapping where nearby neurons in source connect to \n",
    "    nearby neurons in target.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    source_positions : array\n",
    "        Source population positions\n",
    "    target_positions : array\n",
    "        Target population positions  \n",
    "    sigma : Quantity\n",
    "        Gaussian width for topographic spread\n",
    "    weight_init : Initializer\n",
    "        Weight initialization\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    result : ConnectionResult\n",
    "        Topographic connectivity\n",
    "    \"\"\"\n",
    "    # YOUR CODE HERE\n",
    "    # Hint: Use Gaussian distance-dependent connectivity between\n",
    "    # source and target populations\n",
    "    \n",
    "    pass\n",
    "\n",
    "# Test topographic mapping\n",
    "# # Create two 2D grids (e.g., retina and V1)\n",
    "# retina_pos = create_grid_positions(20, 20, spacing=50) * u.um  # 20×20 grid\n",
    "# v1_pos = create_grid_positions(30, 30, spacing=30) * u.um      # 30×30 grid (magnification)\n",
    "# \n",
    "# topo_result = create_topographic_map(\n",
    "#     retina_pos, v1_pos, \n",
    "#     sigma=100*u.um, \n",
    "#     weight_init=Constant(1.0*u.nS)\n",
    "# )\n",
    "# \n",
    "# print(f\"Topographic projection: {len(topo_result.pre_indices)} connections\")"
   ]
  }
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