{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ``Delay`` Protocol\n",
    "\n",
    "## Introduction\n",
    "\n",
    "Delays are fundamental in neural systems, arising from:\n",
    "- **Axonal conduction delays**: Signal propagation along axons takes time\n",
    "- **Synaptic delays**: Chemical transmission across synapses introduces latency\n",
    "- **Network delays**: Multi-step information processing creates temporal lags\n",
    "\n",
    "BrainState provides three powerful APIs for handling delays:\n",
    "\n",
    "1. **`brainstate.nn.Delay`**: General-purpose delay buffer for any data\n",
    "2. **`brainstate.nn.DelayAccess`**: Named accessor for specific delay entries\n",
    "3. **`brainstate.nn.StateWithDelay`**: Automatic delay tracking for module states\n",
    "\n",
    "### Learning Objectives\n",
    "\n",
    "By the end of this tutorial, you will:\n",
    "\n",
    "- ✅ Understand the two delay buffer methods: rotation vs concatenation\n",
    "- ✅ Use `Delay` to create flexible delay buffers with multiple delay taps\n",
    "- ✅ Access delayed values using `DelayAccess` and named entries\n",
    "- ✅ Leverage `StateWithDelay` for automatic state history tracking\n",
    "- ✅ Implement realistic neural models with synaptic and axonal delays\n",
    "- ✅ Choose between step-based and time-based delay retrieval\n",
    "- ✅ Use linear vs round interpolation for continuous-time delays\n",
    "\n",
    "---\n",
    "\n",
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-11T10:28:07.554591Z",
     "start_time": "2025-10-11T10:28:05.717854Z"
    }
   },
   "outputs": [],
   "source": [
    "import brainstate\n",
    "import brainunit as u\n",
    "import jax.numpy as jnp\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "brainstate.environ.set(dt=0.1 * u.ms)  # 0.1 ms time step"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Part 1: Understanding `brainstate.nn.Delay`\n",
    "\n",
    "### 1.1 What is a Delay Buffer?\n",
    "\n",
    "A delay buffer stores a **rolling history** of values over time. Think of it as a time window looking into the past:\n",
    "\n",
    "```\n",
    "Time:  t-3    t-2    t-1    t (now)\n",
    "       │      │      │      │\n",
    "Data:  [10] → [20] → [30] → [40]\n",
    "       ↑      ↑      ↑      ↑\n",
    "     delay=3 delay=2 delay=1 delay=0\n",
    "```\n",
    "\n",
    "The `Delay` class maintains this history efficiently using two methods:\n",
    "\n",
    "1. **Rotation method** (default): Uses a ring buffer with modulo indexing\n",
    "2. **Concatenation method**: Shifts data by concatenating new values\n",
    "\n",
    "### 1.2 Creating a Basic Delay\n",
    "\n",
    "Let's create a simple delay buffer for a scalar signal:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-11T10:28:07.714380Z",
     "start_time": "2025-10-11T10:28:07.560491Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum delay time: 5.0 * msecond\n",
      "Maximum delay length (steps): 51\n",
      "History buffer shape: (51, 1)\n"
     ]
    }
   ],
   "source": [
    "# Create a delay buffer for a scalar value\n",
    "# We need to specify the shape/dtype of data we'll store\n",
    "target_data = jnp.array([0.0])  # Example: scalar in array form\n",
    "\n",
    "# Create delay with 5ms maximum delay\n",
    "delay_buffer = brainstate.nn.Delay(\n",
    "    target_info=target_data,  # Shape/dtype template\n",
    "    time=5.0 * u.ms,          # Maximum delay time\n",
    "    init=0.0,                 # Initial history value\n",
    "    delay_method='rotation'   # Use ring buffer (default)\n",
    ")\n",
    "\n",
    "# Initialize the state\n",
    "delay_buffer.init_state()\n",
    "\n",
    "print(f\"Maximum delay time: {delay_buffer.max_time}\")\n",
    "print(f\"Maximum delay length (steps): {delay_buffer.max_length}\")\n",
    "print(f\"History buffer shape: {delay_buffer.history.value.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 Registering Delay Entries\n",
    "\n",
    "You can register **multiple named entries** for different delay times:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-11T10:28:07.782494Z",
     "start_time": "2025-10-11T10:28:07.717895Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Registered delay entries:\n",
      "  immediate: (Array(0, dtype=int32),) steps\n",
      "  short: (Array(20, dtype=int32),) steps\n",
      "  medium: (Array(50, dtype=int32),) steps\n",
      "  long: (Array(100, dtype=int32),) steps\n"
     ]
    }
   ],
   "source": [
    "# Create delay buffer with multiple taps\n",
    "delay_buffer = brainstate.nn.Delay(\n",
    "    target_info=jnp.array([0.0]),\n",
    "    time=10.0 * u.ms,  # Support delays up to 10ms\n",
    "    init=0.0\n",
    ")\n",
    "\n",
    "# Register different delay times with names\n",
    "delay_buffer.register_entry('immediate', 0.0 * u.ms)   # No delay\n",
    "delay_buffer.register_entry('short', 2.0 * u.ms)       # 2ms delay\n",
    "delay_buffer.register_entry('medium', 5.0 * u.ms)      # 5ms delay\n",
    "delay_buffer.register_entry('long', 10.0 * u.ms)       # 10ms delay\n",
    "\n",
    "delay_buffer.init_state()\n",
    "\n",
    "print(\"Registered delay entries:\")\n",
    "for name, delay_info in delay_buffer._registered_entries.items():\n",
    "    print(f\"  {name}: {delay_info} steps\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Updating and Accessing Delayed Values\n",
    "\n",
    "Now let's simulate a signal and access its delayed versions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-11T10:28:09.041529Z",
     "start_time": "2025-10-11T10:28:07.788438Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Delay buffer successfully stores and retrieves delayed values\n"
     ]
    }
   ],
   "source": [
    "# Simulation parameters\n",
    "duration = 50.0 * u.ms\n",
    "dt = brainstate.environ.get_dt()\n",
    "num_steps = int(duration / dt)\n",
    "\n",
    "# Storage for visualization\n",
    "times = []\n",
    "current_values = []\n",
    "delayed_short = []\n",
    "delayed_medium = []\n",
    "delayed_long = []\n",
    "\n",
    "# Simulate a sinusoidal signal\n",
    "for i in range(num_steps):\n",
    "    t = i * dt\n",
    "    brainstate.environ.set(i=i, t=t)\n",
    "    \n",
    "    # Current signal: sin(2π * 50Hz * t)\n",
    "    current = jnp.sin(2 * jnp.pi * 50.0 * (t / u.second)) \n",
    "    current_array = jnp.array([current])\n",
    "    \n",
    "    # Update delay buffer with current value\n",
    "    delay_buffer.update(current_array)\n",
    "    \n",
    "    # Retrieve delayed values\n",
    "    val_immediate = delay_buffer.at('immediate')[0]\n",
    "    val_short = delay_buffer.at('short')[0]\n",
    "    val_medium = delay_buffer.at('medium')[0]\n",
    "    val_long = delay_buffer.at('long')[0]\n",
    "    \n",
    "    # Store for plotting\n",
    "    times.append(float(t / u.ms))\n",
    "    current_values.append(float(val_immediate))\n",
    "    delayed_short.append(float(val_short))\n",
    "    delayed_medium.append(float(val_medium))\n",
    "    delayed_long.append(float(val_long))\n",
    "\n",
    "# Visualize\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(times, current_values, label='Current (0ms delay)', linewidth=2)\n",
    "plt.plot(times, delayed_short, label='Short (2ms delay)', alpha=0.7)\n",
    "plt.plot(times, delayed_medium, label='Medium (5ms delay)', alpha=0.7)\n",
    "plt.plot(times, delayed_long, label='Long (10ms delay)', alpha=0.7)\n",
    "plt.xlabel('Time (ms)')\n",
    "plt.ylabel('Signal Value')\n",
    "plt.title('Delay Buffer: Multiple Delay Taps on 50Hz Sinusoid')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"✅ Delay buffer successfully stores and retrieves delayed values\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5 Rotation vs Concatenation Methods\n",
    "\n",
    "**Rotation method** (ring buffer):\n",
    "- More memory efficient\n",
    "- Uses modulo indexing: `index = (current_step - delay_step) % max_length`\n",
    "- Default and recommended for most cases\n",
    "\n",
    "**Concatenation method**:\n",
    "- Shifts entire buffer on each update\n",
    "- Easier to understand conceptually\n",
    "- Can be slower for large buffers\n",
    "\n",
    "Let's compare both:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-11T10:28:11.211903Z",
     "start_time": "2025-10-11T10:28:11.071185Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Comparing rotation vs concatenation methods:\n",
      "\n",
      "Step 0: Rotation=0.0, Concat=0.0, Match=True\n",
      "Step 1: Rotation=0.0, Concat=0.0, Match=True\n",
      "Step 2: Rotation=0.0, Concat=0.0, Match=True\n",
      "Step 3: Rotation=0.0, Concat=0.0, Match=True\n",
      "Step 4: Rotation=0.0, Concat=0.0, Match=True\n",
      "Step 5: Rotation=0.0, Concat=0.0, Match=True\n",
      "Step 6: Rotation=0.0, Concat=0.0, Match=True\n",
      "Step 7: Rotation=0.0, Concat=0.0, Match=True\n",
      "Step 8: Rotation=0.0, Concat=0.0, Match=True\n",
      "Step 9: Rotation=0.0, Concat=0.0, Match=True\n",
      "\n",
      "✅ Both methods produce identical results\n"
     ]
    }
   ],
   "source": [
    "# Create two delay buffers with different methods\n",
    "delay_rotation = brainstate.nn.Delay(\n",
    "    target_info=jnp.array([0.0]),\n",
    "    time=5.0 * u.ms,\n",
    "    delay_method='rotation'\n",
    ")\n",
    "delay_rotation.register_entry('test', 3.0 * u.ms)\n",
    "delay_rotation.init_state()\n",
    "\n",
    "delay_concat = brainstate.nn.Delay(\n",
    "    target_info=jnp.array([0.0]),\n",
    "    time=5.0 * u.ms,\n",
    "    delay_method='concat'\n",
    ")\n",
    "delay_concat.register_entry('test', 3.0 * u.ms)\n",
    "delay_concat.init_state()\n",
    "\n",
    "# Simulate 10 steps\n",
    "print(\"Comparing rotation vs concatenation methods:\\n\")\n",
    "for i in range(10):\n",
    "    brainstate.environ.set(i=i)\n",
    "    \n",
    "    # Update both with same value\n",
    "    value = jnp.array([float(i)])\n",
    "    delay_rotation.update(value)\n",
    "    delay_concat.update(value)\n",
    "    \n",
    "    # Retrieve delayed values\n",
    "    val_rotation = delay_rotation.at('test')[0]\n",
    "    val_concat = delay_concat.at('test')[0]\n",
    "    \n",
    "    print(f\"Step {i}: Rotation={val_rotation:.1f}, Concat={val_concat:.1f}, Match={jnp.allclose(val_rotation, val_concat)}\")\n",
    "\n",
    "print(\"\\n✅ Both methods produce identical results\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Part 2: Time-based vs Step-based Retrieval\n",
    "\n",
    "### 2.1 Step-based Retrieval\n",
    "\n",
    "When you know the exact delay in **integer time steps**, use `retrieve_at_step()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-11T10:28:11.237763Z",
     "start_time": "2025-10-11T10:28:11.215026Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step-based retrieval at step 19:\n",
      "  0 steps back (current):  190.0 (expected: 190)\n",
      "  5 steps back:            140.0 (expected: 140)\n",
      "  10 steps back:           90.0 (expected: 90)\n"
     ]
    }
   ],
   "source": [
    "delay_buffer = brainstate.nn.Delay(\n",
    "    target_info=jnp.array([0.0]),\n",
    "    time=10.0 * u.ms,\n",
    ")\n",
    "delay_buffer.init_state()\n",
    "\n",
    "# Populate buffer\n",
    "for i in range(20):\n",
    "    brainstate.environ.set(i=i)\n",
    "    delay_buffer.update(jnp.array([float(i * 10)]))\n",
    "\n",
    "# Retrieve by step (integer delay)\n",
    "brainstate.environ.set(i=19)  # At step 19\n",
    "current = delay_buffer.retrieve_at_step(0)      # 0 steps back\n",
    "delayed_5 = delay_buffer.retrieve_at_step(5)    # 5 steps back\n",
    "delayed_10 = delay_buffer.retrieve_at_step(10)  # 10 steps back\n",
    "\n",
    "print(\"Step-based retrieval at step 19:\")\n",
    "print(f\"  0 steps back (current):  {current[0]:.1f} (expected: {19*10})\")\n",
    "print(f\"  5 steps back:            {delayed_5[0]:.1f} (expected: {(19-5)*10})\")\n",
    "print(f\"  10 steps back:           {delayed_10[0]:.1f} (expected: {(19-10)*10})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Time-based Retrieval\n",
    "\n",
    "When delays are specified in **continuous time**, use `retrieve_at_time()`. This supports interpolation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-11T10:28:11.414429Z",
     "start_time": "2025-10-11T10:28:11.238430Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time-based retrieval at t=9.9ms, delay=2.5ms:\n",
      "  Linear interpolation: 74.00 (between steps 74 and 75)\n",
      "  Round interpolation:  74.00 (rounds to nearest step)\n",
      "  Expected exact value: 74 (25 steps back from step 99)\n"
     ]
    }
   ],
   "source": [
    "# Create delay with linear interpolation\n",
    "delay_linear = brainstate.nn.Delay(\n",
    "    target_info=jnp.array([0.0]),\n",
    "    time=5.0 * u.ms,\n",
    "    interp_method='linear_interp'  # Linear interpolation\n",
    ")\n",
    "delay_linear.init_state()\n",
    "\n",
    "# Create delay with round interpolation\n",
    "delay_round = brainstate.nn.Delay(\n",
    "    target_info=jnp.array([0.0]),\n",
    "    time=5.0 * u.ms,\n",
    "    interp_method='round'  # Round to nearest step\n",
    ")\n",
    "delay_round.init_state()\n",
    "\n",
    "# Populate both buffers\n",
    "for i in range(100):\n",
    "    t = i * brainstate.environ.get_dt()\n",
    "    brainstate.environ.set(i=i, t=t)\n",
    "    value = jnp.array([float(i)])\n",
    "    delay_linear.update(value)\n",
    "    delay_round.update(value)\n",
    "\n",
    "# Retrieve at non-integer delay times\n",
    "t_current = 99 * brainstate.environ.get_dt()\n",
    "brainstate.environ.set(i=99, t=t_current)\n",
    "\n",
    "delay_time = 2.5 * u.ms  # 2.5ms = 25 steps (non-integer at 0.1ms resolution)\n",
    "target_time = t_current - delay_time\n",
    "\n",
    "val_linear = delay_linear.retrieve_at_time(target_time)[0]\n",
    "val_round = delay_round.retrieve_at_time(target_time)[0]\n",
    "\n",
    "print(f\"Time-based retrieval at t={float(t_current / u.ms):.1f}ms, delay={float(delay_time / u.ms)}ms:\")\n",
    "print(f\"  Linear interpolation: {val_linear:.2f} (between steps 74 and 75)\")\n",
    "print(f\"  Round interpolation:  {val_round:.2f} (rounds to nearest step)\")\n",
    "print(f\"  Expected exact value: {99 - 25} (25 steps back from step 99)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Interpolation Comparison\n",
    "\n",
    "Let's visualize the difference between linear and round interpolation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-11T10:28:12.194330Z",
     "start_time": "2025-10-11T10:28:11.424944Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Linear interpolation provides smooth continuous values\n",
      "✅ Round interpolation snaps to nearest discrete time step\n"
     ]
    }
   ],
   "source": [
    "# Create delays with different interpolation\n",
    "delay_linear = brainstate.nn.Delay(jnp.array([0.0]), time=10.0 * u.ms, interp_method='linear_interp')\n",
    "delay_round = brainstate.nn.Delay(jnp.array([0.0]), time=10.0 * u.ms, interp_method='round')\n",
    "delay_linear.init_state()\n",
    "delay_round.init_state()\n",
    "\n",
    "# Populate with ramp signal\n",
    "for i in range(200):\n",
    "    t = i * brainstate.environ.get_dt()\n",
    "    brainstate.environ.set(i=i, t=t)\n",
    "    value = jnp.array([float(i)])\n",
    "    delay_linear.update(value)\n",
    "    delay_round.update(value)\n",
    "\n",
    "# Test various delay times (non-integer steps)\n",
    "t_now = 199 * brainstate.environ.get_dt()\n",
    "brainstate.environ.set(i=199, t=t_now)\n",
    "\n",
    "delay_times_ms = jnp.linspace(0.0, 10.0, 101)  # 0 to 10ms\n",
    "linear_vals = []\n",
    "round_vals = []\n",
    "\n",
    "for delay_ms in delay_times_ms:\n",
    "    delay_time = delay_ms * u.ms\n",
    "    target_time = t_now - delay_time\n",
    "    \n",
    "    val_linear = delay_linear.retrieve_at_time(target_time)[0]\n",
    "    val_round = delay_round.retrieve_at_time(target_time)[0]\n",
    "    \n",
    "    linear_vals.append(float(val_linear))\n",
    "    round_vals.append(float(val_round))\n",
    "\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.plot(delay_times_ms, linear_vals, label='Linear Interpolation', linewidth=2)\n",
    "plt.plot(delay_times_ms, round_vals, label='Round Interpolation', linewidth=2, alpha=0.7, linestyle='--')\n",
    "plt.xlabel('Delay Time (ms)')\n",
    "plt.ylabel('Retrieved Value')\n",
    "plt.title('Linear vs Round Interpolation in Time-based Delay Retrieval')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"✅ Linear interpolation provides smooth continuous values\")\n",
    "print(\"✅ Round interpolation snaps to nearest discrete time step\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Part 3: `brainstate.nn.DelayAccess`\n",
    "\n",
    "### 3.1 What is DelayAccess?\n",
    "\n",
    "`DelayAccess` creates a **reusable accessor** for a specific delay entry. It's useful when:\n",
    "- You need to pass delay accessors to other modules\n",
    "- You want to encapsulate delay configuration\n",
    "- Building modular neural network components\n",
    "\n",
    "### 3.2 Creating and Using DelayAccess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-11T10:28:12.365347Z",
     "start_time": "2025-10-11T10:28:12.203264Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DelayAccess retrieval at step 199:\n",
      "  2ms delay (20 steps): [179. 358. 537.]\n",
      "  5ms delay (50 steps): [149. 298. 447.]\n",
      "  Expected 2ms: [179, 358, 537]\n",
      "  Expected 5ms: [149, 298, 447]\n"
     ]
    }
   ],
   "source": [
    "# Create a delay buffer\n",
    "delay_buffer = brainstate.nn.Delay(\n",
    "    target_info=jnp.array([0.0, 0.0, 0.0]),  # 3D vector\n",
    "    time=10.0 * u.ms,\n",
    ")\n",
    "delay_buffer.init_state()\n",
    "\n",
    "# Create DelayAccess objects for different delays\n",
    "# Method 1: Using .access() method\n",
    "accessor_2ms = delay_buffer.access('entry_2ms', 2.0 * u.ms)\n",
    "accessor_5ms = delay_buffer.access('entry_5ms', 5.0 * u.ms)\n",
    "\n",
    "# Simulate signal\n",
    "for i in range(200):\n",
    "    brainstate.environ.set(i=i)\n",
    "    \n",
    "    # Update with [i, i*2, i*3]\n",
    "    value = jnp.array([float(i), float(i*2), float(i*3)])\n",
    "    delay_buffer.update(value)\n",
    "\n",
    "# Access delayed values through DelayAccess objects\n",
    "delayed_2ms = accessor_2ms.update()  # Call update() to retrieve\n",
    "delayed_5ms = accessor_5ms.update()\n",
    "\n",
    "print(\"DelayAccess retrieval at step 199:\")\n",
    "print(f\"  2ms delay (20 steps): {delayed_2ms}\")\n",
    "print(f\"  5ms delay (50 steps): {delayed_5ms}\")\n",
    "print(f\"  Expected 2ms: [{199-20}, {(199-20)*2}, {(199-20)*3}]\")\n",
    "print(f\"  Expected 5ms: [{199-50}, {(199-50)*2}, {(199-50)*3}]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Using DelayAccess in Modules\n",
    "\n",
    "DelayAccess is particularly useful when building modular components:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-11T10:28:12.565497Z",
     "start_time": "2025-10-11T10:28:12.371359Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing DelayedConnection module:\n",
      "\n",
      "Step  0: Input=  0.0, Output=  0.0\n",
      "Step 10: Input= 10.0, Output=  0.0\n",
      "Step 20: Input= 20.0, Output=  0.0\n",
      "Step 30: Input= 30.0, Output=  0.0\n",
      "Step 40: Input= 40.0, Output=  5.0\n",
      "\n",
      "✅ DelayAccess enables modular delay handling in custom modules\n"
     ]
    }
   ],
   "source": [
    "class DelayedConnection(brainstate.nn.Module):\n",
    "    \"\"\"A module that applies delayed weighted connection.\"\"\"\n",
    "    \n",
    "    def __init__(self, size, weight, delay_time):\n",
    "        super().__init__()\n",
    "        self.weight = weight\n",
    "        \n",
    "        # Create delay buffer and accessor\n",
    "        self.delay_buffer = brainstate.nn.Delay(\n",
    "            target_info=jnp.zeros(size),\n",
    "            time=delay_time * 2,  # Buffer size\n",
    "        )\n",
    "        self.delay_access = self.delay_buffer.access('conn', delay_time)\n",
    "    \n",
    "    def update(self, current_input):\n",
    "        # Store current input\n",
    "        self.delay_buffer.update(current_input)\n",
    "        \n",
    "        # Get delayed input\n",
    "        delayed_input = self.delay_access.update()\n",
    "        \n",
    "        # Apply weight to delayed input\n",
    "        return self.weight * delayed_input\n",
    "\n",
    "# Create delayed connection\n",
    "conn = DelayedConnection(size=5, weight=0.5, delay_time=3.0 * u.ms)\n",
    "conn.delay_buffer.init_state()\n",
    "\n",
    "# Simulate\n",
    "print(\"Testing DelayedConnection module:\\n\")\n",
    "for i in range(50):\n",
    "    brainstate.environ.set(i=i)\n",
    "    \n",
    "    # Input: [i, i, i, i, i]\n",
    "    input_signal = jnp.ones(5) * i\n",
    "    output = conn.update(input_signal)\n",
    "    \n",
    "    if i % 10 == 0:\n",
    "        print(f\"Step {i:2d}: Input={float(input_signal[0]):5.1f}, Output={float(output[0]):5.1f}\")\n",
    "\n",
    "print(\"\\n✅ DelayAccess enables modular delay handling in custom modules\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Part 4: `brainstate.nn.StateWithDelay`\n",
    "\n",
    "### 4.1 What is StateWithDelay?\n",
    "\n",
    "`StateWithDelay` is a **specialized delay** that automatically tracks a `State` variable in a module:\n",
    "\n",
    "- Automatically bound to a module's state (e.g., membrane potential `V`)\n",
    "- Updates history buffer after each simulation step\n",
    "- Commonly created via `prefetch_delay()` helper\n",
    "- Ideal for delayed feedback and recurrent connections\n",
    "\n",
    "### 4.2 Using StateWithDelay via `prefetch_delay()`\n",
    "\n",
    "The easiest way to use `StateWithDelay` is through the Dynamics `prefetch_delay()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-11T10:28:53.495505Z",
     "start_time": "2025-10-11T10:28:50.079065Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ StateWithDelay automatically tracks module states for delayed feedback\n"
     ]
    }
   ],
   "source": [
    "class LIFWithDelayedFeedback(brainstate.nn.Dynamics):\n",
    "    \"\"\"LIF neuron with delayed self-feedback.\"\"\"\n",
    "    \n",
    "    def __init__(self, size, feedback_delay=5.0 * u.ms, feedback_strength=0.3):\n",
    "        super().__init__(in_size=size)\n",
    "        \n",
    "        # Neuron parameters\n",
    "        self.tau = 10.0 * u.ms\n",
    "        self.V_rest = -65.0 * u.mV\n",
    "        self.V_th = -50.0 * u.mV\n",
    "        self.V_reset = -65.0 * u.mV\n",
    "        self.R = 1.0 * u.ohm\n",
    "        self.feedback_strength = feedback_strength\n",
    "        \n",
    "        # States\n",
    "        self.V = brainstate.State(jnp.ones(size) * self.V_rest)\n",
    "        self.spike = brainstate.State(jnp.zeros(size, dtype=bool))\n",
    "        \n",
    "        # Create StateWithDelay for V using prefetch_delay()\n",
    "        # This automatically creates a StateWithDelay and registers it\n",
    "        self.V_delayed = self.prefetch_delay('V', feedback_delay)\n",
    "    \n",
    "    def update(self, I):\n",
    "        # Get delayed voltage for feedback\n",
    "        V_delayed = self.V_delayed()  # Call to retrieve delayed value\n",
    "        \n",
    "        # Add delayed feedback to input current\n",
    "        feedback_current = (V_delayed - self.V_rest) * self.feedback_strength / self.R\n",
    "        I_total = I + feedback_current\n",
    "        \n",
    "        # Update voltage (exponential Euler)\n",
    "        dt = brainstate.environ.get_dt()\n",
    "        alpha = jnp.exp(-dt / self.tau)\n",
    "        V_inf = self.V_rest + I_total * self.R\n",
    "        self.V.value = self.V.value * alpha + V_inf * (1 - alpha)\n",
    "        \n",
    "        # Spike detection and reset\n",
    "        self.spike.value = self.V.value >= self.V_th\n",
    "        self.V.value = u.math.where(self.spike.value, self.V_reset, self.V.value)\n",
    "        \n",
    "        return self.spike.value\n",
    "\n",
    "# Create neuron with delayed feedback\n",
    "neuron = LIFWithDelayedFeedback(size=1, feedback_delay=3.0 * u.ms, feedback_strength=0.4)\n",
    "brainstate.nn.init_all_states(neuron)\n",
    "\n",
    "# Simulate\n",
    "duration = 100.0 * u.ms\n",
    "num_steps = int(duration / brainstate.environ.get_dt())\n",
    "\n",
    "times = []\n",
    "voltages = []\n",
    "spikes = []\n",
    "\n",
    "for i in range(num_steps):\n",
    "    t = i * brainstate.environ.get_dt()\n",
    "    brainstate.environ.set(i=i, t=t)\n",
    "    \n",
    "    # Constant input current\n",
    "    I_input = 1.5 * u.nA * jnp.ones(1)\n",
    "    \n",
    "    spike_output = neuron.update(I_input)\n",
    "    \n",
    "    times.append(float(t / u.ms))\n",
    "    voltages.append(float(neuron.V.value[0] / u.mV))\n",
    "    spikes.append(float(spike_output[0]))\n",
    "\n",
    "# Visualize\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 6), sharex=True)\n",
    "\n",
    "# Voltage trace\n",
    "ax1.plot(times, voltages, linewidth=1.5)\n",
    "ax1.axhline(y=-50.0, color='r', linestyle='--', alpha=0.5, label='Threshold')\n",
    "ax1.set_ylabel('Membrane Potential (mV)')\n",
    "ax1.set_title('LIF Neuron with Delayed Self-Feedback (3ms delay)')\n",
    "ax1.legend()\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# Spike raster\n",
    "spike_times = [times[i] for i in range(len(spikes)) if spikes[i] > 0.5]\n",
    "ax2.eventplot([spike_times], lineoffsets=0.5, linelengths=0.8, colors='red')\n",
    "ax2.set_ylabel('Spikes')\n",
    "ax2.set_xlabel('Time (ms)')\n",
    "ax2.set_ylim([0, 1])\n",
    "ax2.set_yticks([])\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"✅ StateWithDelay automatically tracks module states for delayed feedback\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Direct StateWithDelay Creation\n",
    "\n",
    "You can also create `StateWithDelay` explicitly (advanced usage):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-11T10:28:56.977529Z",
     "start_time": "2025-10-11T10:28:56.818741Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Manual StateWithDelay creation:\n",
      "\n",
      "Step   0: Current V=[0. 0. 0.], Delayed V=[0. 0. 0.]\n",
      "Step  20: Current V=[12.094189 24.188377 36.282566], Delayed V=[0. 0. 0.]\n",
      "Step  40: Current V=[31.133022 62.266045 93.39906 ], Delayed V=[0. 0. 0.]\n",
      "Step  60: Current V=[ 51.016167 102.03233  153.0485  ], Delayed V=[ 4.1381054  8.276211  12.414316 ]\n",
      "Step  80: Current V=[ 71.00195 142.0039  213.00584], Delayed V=[21.381517 42.763035 64.14455 ]\n",
      "\n",
      "✅ StateWithDelay can be created explicitly for fine-grained control\n"
     ]
    }
   ],
   "source": [
    "class SimpleNeuron(brainstate.nn.Dynamics):\n",
    "    \"\"\"Simple neuron for demonstration.\"\"\"\n",
    "    \n",
    "    def __init__(self, size):\n",
    "        super().__init__(in_size=size)\n",
    "        self.V = brainstate.State(jnp.zeros(size))\n",
    "    \n",
    "    def update(self, x):\n",
    "        self.V.value = self.V.value * 0.9 + x\n",
    "        return self.V.value\n",
    "\n",
    "# Create neuron\n",
    "neuron = SimpleNeuron(size=3)\n",
    "neuron.init_state()\n",
    "\n",
    "# Manually create StateWithDelay for neuron.V\n",
    "state_delay = brainstate.nn.StateWithDelay(\n",
    "    target=neuron,          # The module owning the state\n",
    "    item='V',               # Name of the state attribute\n",
    "    delay_method='rotation'\n",
    ")\n",
    "\n",
    "# Register a delay time\n",
    "state_delay.register_entry('V_5ms', 5.0 * u.ms)\n",
    "\n",
    "# Initialize\n",
    "state_delay.init_state()\n",
    "\n",
    "# Simulate\n",
    "print(\"Manual StateWithDelay creation:\\n\")\n",
    "for i in range(100):\n",
    "    brainstate.environ.set(i=i)\n",
    "    \n",
    "    # Update neuron\n",
    "    neuron.update(jnp.array([1.0, 2.0, 3.0]) * (i / 10.0))\n",
    "    \n",
    "    # Update delay buffer (must be done manually in this case)\n",
    "    state_delay.update()\n",
    "    \n",
    "    if i % 20 == 0:\n",
    "        current_V = neuron.V.value\n",
    "        delayed_V = state_delay.at('V_5ms')\n",
    "        print(f\"Step {i:3d}: Current V={current_V}, Delayed V={delayed_V}\")\n",
    "\n",
    "print(\"\\n✅ StateWithDelay can be created explicitly for fine-grained control\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Part 5: Practical Example - Synaptic Transmission with Delays\n",
    "\n",
    "Let's build a realistic synapse model with axonal and dendritic delays:\n",
    "\n",
    "### 5.1 Delayed Synaptic Connection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-11T10:31:02.524571Z",
     "start_time": "2025-10-11T10:30:56.766790Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "✅ Delayed synaptic transmission: presynaptic spikes appear at postsynaptic neuron after 5ms delay\n",
      "   First pre spike: ~13.8ms\n",
      "   First syn current rise: ~18.8ms (5ms delay)\n"
     ]
    }
   ],
   "source": [
    "class SimpleLIF(brainstate.nn.Dynamics):\n",
    "    \"\"\"Simple LIF neuron.\"\"\"\n",
    "    \n",
    "    def __init__(self, size, tau=10.0 * u.ms, V_rest=-65.0 * u.mV,\n",
    "                 V_th=-50.0 * u.mV, V_reset=-65.0 * u.mV, R=1.0 * u.ohm):\n",
    "        super().__init__(in_size=size)\n",
    "        \n",
    "        self.tau = tau\n",
    "        self.V_rest = V_rest\n",
    "        self.V_th = V_th\n",
    "        self.V_reset = V_reset\n",
    "        self.R = R\n",
    "        \n",
    "        self.V = brainstate.State(jnp.ones(size) * V_rest)\n",
    "        self.spike = brainstate.State(jnp.zeros(size, dtype=bool))\n",
    "    \n",
    "    def update(self, I):\n",
    "        dt = brainstate.environ.get_dt()\n",
    "        alpha = jnp.exp(-dt / self.tau)\n",
    "        \n",
    "        V_inf = self.V_rest + I * self.R\n",
    "        self.V.value = self.V.value * alpha + V_inf * (1 - alpha)\n",
    "        \n",
    "        self.spike.value = self.V.value >= self.V_th\n",
    "        self.V.value = u.math.where(self.spike.value, self.V_reset, self.V.value)\n",
    "        \n",
    "        return self.spike.value\n",
    "\n",
    "\n",
    "class DelayedSynapse(brainstate.nn.Dynamics):\n",
    "    \"\"\"Synapse with conduction delay.\"\"\"\n",
    "    \n",
    "    def __init__(self, pre_size, post_size, weight_matrix, \n",
    "                 axonal_delay=2.0 * u.ms, tau_syn=5.0 * u.ms):\n",
    "        super().__init__(in_size=pre_size)\n",
    "        \n",
    "        self.weight = weight_matrix\n",
    "        self.tau_syn = tau_syn\n",
    "        \n",
    "        # Synaptic current state\n",
    "        self.I_syn = brainstate.State(jnp.zeros(post_size) * u.mA)\n",
    "        \n",
    "        # Create delay buffer for presynaptic spikes\n",
    "        self.spike_delay = brainstate.nn.Delay(\n",
    "            target_info=jnp.zeros(pre_size, dtype=bool),\n",
    "            time=axonal_delay * 2,\n",
    "            init=False\n",
    "        )\n",
    "        self.spike_delay.register_entry('axonal', axonal_delay)\n",
    "    \n",
    "    def update(self, pre_spike):\n",
    "        # Store presynaptic spikes\n",
    "        self.spike_delay.update(pre_spike)\n",
    "        \n",
    "        # Retrieve delayed spikes\n",
    "        delayed_spike = self.spike_delay.at('axonal')\n",
    "        \n",
    "        # Synaptic current injection\n",
    "        I_input = self.weight @ delayed_spike.astype(jnp.float32)\n",
    "        \n",
    "        # Synaptic current decay\n",
    "        dt = brainstate.environ.get_dt()\n",
    "        alpha = jnp.exp(-dt / self.tau_syn)\n",
    "        self.I_syn.value = self.I_syn.value * alpha + I_input\n",
    "        \n",
    "        return self.I_syn.value\n",
    "\n",
    "\n",
    "# Create network: pre -> synapse -> post\n",
    "pre_neuron = SimpleLIF(size=1)\n",
    "post_neuron = SimpleLIF(size=1)\n",
    "\n",
    "# Synaptic weight\n",
    "weight = jnp.array([[2.0]]) * u.mA  # Strong connection\n",
    "\n",
    "synapse = DelayedSynapse(\n",
    "    pre_size=1, \n",
    "    post_size=1, \n",
    "    weight_matrix=weight,\n",
    "    axonal_delay=5.0 * u.ms,  # 5ms axonal delay\n",
    "    tau_syn=3.0 * u.ms\n",
    ")\n",
    "\n",
    "# Initialize\n",
    "pre_neuron.init_state()\n",
    "post_neuron.init_state()\n",
    "synapse.spike_delay.init_state()\n",
    "\n",
    "# Simulate\n",
    "duration = 100.0 * u.ms\n",
    "num_steps = int(duration / brainstate.environ.get_dt())\n",
    "\n",
    "times = []\n",
    "pre_voltages = []\n",
    "post_voltages = []\n",
    "syn_currents = []\n",
    "pre_spikes = []\n",
    "post_spikes = []\n",
    "\n",
    "for i in range(num_steps):\n",
    "    t = i * brainstate.environ.get_dt()\n",
    "    brainstate.environ.set(i=i, t=t)\n",
    "    \n",
    "    # Strong input to presynaptic neuron\n",
    "    I_pre = 20. * u.mA * jnp.ones(1)\n",
    "    \n",
    "    # Update presynaptic neuron\n",
    "    pre_spike = pre_neuron.update(I_pre)\n",
    "    \n",
    "    # Update synapse with delayed transmission\n",
    "    I_syn = synapse.update(pre_spike)\n",
    "    \n",
    "    # Update postsynaptic neuron\n",
    "    post_spike = post_neuron.update(I_syn)\n",
    "    \n",
    "    # Record\n",
    "    times.append(float(t / u.ms))\n",
    "    pre_voltages.append(float(pre_neuron.V.value[0] / u.mV))\n",
    "    post_voltages.append(float(post_neuron.V.value[0] / u.mV))\n",
    "    syn_currents.append(float(I_syn[0] / u.nA))\n",
    "    pre_spikes.append(float(pre_spike[0]))\n",
    "    post_spikes.append(float(post_spike[0]))\n",
    "\n",
    "# Visualize\n",
    "fig, axes = plt.subplots(4, 1, figsize=(14, 10), sharex=True)\n",
    "\n",
    "# Presynaptic voltage\n",
    "axes[0].plot(times, pre_voltages, 'b', linewidth=1.5)\n",
    "axes[0].axhline(y=-50.0, color='r', linestyle='--', alpha=0.5)\n",
    "axes[0].set_ylabel('Pre V (mV)')\n",
    "axes[0].set_title('Synaptic Transmission with 5ms Axonal Delay')\n",
    "axes[0].grid(True, alpha=0.3)\n",
    "\n",
    "# Presynaptic spikes\n",
    "pre_spike_times = [times[i] for i in range(len(pre_spikes)) if pre_spikes[i] > 0.5]\n",
    "axes[1].eventplot([pre_spike_times], lineoffsets=0.5, linelengths=0.8, colors='blue')\n",
    "axes[1].set_ylabel('Pre Spikes')\n",
    "axes[1].set_ylim([0, 1])\n",
    "axes[1].set_yticks([])\n",
    "axes[1].grid(True, alpha=0.3)\n",
    "\n",
    "# Synaptic current\n",
    "axes[2].plot(times, syn_currents, 'g', linewidth=1.5)\n",
    "axes[2].set_ylabel('Syn Current (nA)')\n",
    "axes[2].grid(True, alpha=0.3)\n",
    "\n",
    "# Postsynaptic voltage\n",
    "axes[3].plot(times, post_voltages, 'r', linewidth=1.5)\n",
    "axes[3].axhline(y=-50.0, color='r', linestyle='--', alpha=0.5)\n",
    "axes[3].set_ylabel('Post V (mV)')\n",
    "axes[3].set_xlabel('Time (ms)')\n",
    "axes[3].grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n✅ Delayed synaptic transmission: presynaptic spikes appear at postsynaptic neuron after 5ms delay\")\n",
    "if len(pre_spike_times) > 0:\n",
    "    print(f\"   First pre spike: ~{pre_spike_times[0]:.1f}ms\")\n",
    "    print(f\"   First syn current rise: ~{pre_spike_times[0] + 5.0:.1f}ms (5ms delay)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Part 6: Advanced - Heterogeneous Delays\n",
    "\n",
    "### 6.1 Vector Delays\n",
    "\n",
    "BrainState supports **heterogeneous delays** where each element can have a different delay time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-11T10:31:08.054155Z",
     "start_time": "2025-10-11T10:31:07.686924Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Heterogeneous delays at step 199:\n",
      "  Delays: [2ms, 5ms, 8ms] = [20 50 80] steps\n",
      "  Retrieved values: [  179.  1490. 11900.]\n",
      "  Expected values:  [  179  1490 11900]\n",
      "  Match: True\n",
      "\n",
      "✅ Vector delays enable different delay times for each element\n"
     ]
    }
   ],
   "source": [
    "# Create delay buffer for 3 neurons\n",
    "delay_buffer = brainstate.nn.Delay(\n",
    "    target_info=jnp.zeros(3),\n",
    "    time=10.0 * u.ms,\n",
    ")\n",
    "\n",
    "# Register heterogeneous delays: different delay for each neuron\n",
    "# Delays: [2ms, 5ms, 8ms]\n",
    "delay_times = jnp.array([2.0, 5.0, 8.0]) * u.ms\n",
    "neuron_indices = jnp.array([0, 1, 2])  # Which neuron\n",
    "\n",
    "delay_buffer.register_entry('heterogeneous', delay_times, neuron_indices)\n",
    "delay_buffer.init_state()\n",
    "\n",
    "# Simulate\n",
    "for i in range(200):\n",
    "    brainstate.environ.set(i=i)\n",
    "    \n",
    "    # Update with [i, i*10, i*100]\n",
    "    values = jnp.array([float(i), float(i*10), float(i*100)])\n",
    "    delay_buffer.update(values)\n",
    "\n",
    "# Retrieve heterogeneous delays\n",
    "delayed_values = delay_buffer.at('heterogeneous')\n",
    "\n",
    "expected_delays_steps = jnp.array([20, 50, 80])  # in steps (0.1ms each)\n",
    "expected_values = jnp.array([\n",
    "    199 - 20,\n",
    "    (199 - 50) * 10,\n",
    "    (199 - 80) * 100\n",
    "])\n",
    "\n",
    "print(\"Heterogeneous delays at step 199:\")\n",
    "print(f\"  Delays: [2ms, 5ms, 8ms] = {expected_delays_steps} steps\")\n",
    "print(f\"  Retrieved values: {delayed_values}\")\n",
    "print(f\"  Expected values:  {expected_values}\")\n",
    "print(f\"  Match: {jnp.allclose(delayed_values, expected_values)}\")\n",
    "\n",
    "print(\"\\n✅ Vector delays enable different delay times for each element\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Summary\n",
    "\n",
    "### Key Concepts\n",
    "\n",
    "1. **`brainstate.nn.Delay`**: General-purpose delay buffer\n",
    "   - Stores rolling history of values\n",
    "   - Supports rotation (ring buffer) or concatenation methods\n",
    "   - Multiple named delay entries via `register_entry()`\n",
    "   - Step-based (`retrieve_at_step`) or time-based (`retrieve_at_time`) retrieval\n",
    "   - Linear or round interpolation for continuous-time queries\n",
    "\n",
    "2. **`brainstate.nn.DelayAccess`**: Named accessor for delay entries\n",
    "   - Created via `delay.access(entry, delay_time)`\n",
    "   - Encapsulates delay configuration\n",
    "   - Useful for modular design\n",
    "   - Call `.update()` to retrieve delayed value\n",
    "\n",
    "3. **`brainstate.nn.StateWithDelay`**: Automatic state tracking\n",
    "   - Bound to a module's `State` variable\n",
    "   - Created via `prefetch_delay(state_name, delay_time)`\n",
    "   - Automatically updates after each step\n",
    "   - Ideal for delayed feedback and recurrent connections\n",
    "   - Call as function `()` to retrieve delayed state\n",
    "\n",
    "### API Comparison Table\n",
    "\n",
    "| Feature | `Delay` | `DelayAccess` | `StateWithDelay` |\n",
    "|---------|---------|---------------|------------------|\n",
    "| **Use Case** | General data buffering | Named accessors | Module state tracking |\n",
    "| **Creation** | Manual instantiation | Via `delay.access()` | Via `prefetch_delay()` |\n",
    "| **Updates** | Manual `update(value)` | Inherits from Delay | Automatic after step |\n",
    "| **Retrieval** | `at(entry)` or `retrieve_at_*` | `.update()` | Call as function `()` |\n",
    "| **Multiple delays** | ✅ Multiple entries | ✅ One per accessor | ✅ Multiple registrations |\n",
    "| **Interpolation** | ✅ Linear or round | ✅ Via underlying Delay | ✅ Via underlying Delay |\n",
    "\n",
    "### Best Practices\n",
    "\n",
    "1. **Choose the right tool**:\n",
    "   - Use `Delay` for general data buffering (synaptic inputs, external signals)\n",
    "   - Use `DelayAccess` when passing delay accessors to other modules\n",
    "   - Use `StateWithDelay` (via `prefetch_delay()`) for module state feedback\n",
    "\n",
    "2. **Delay method selection**:\n",
    "   - Default to `rotation` (ring buffer) for efficiency\n",
    "   - Use `concat` only if you need sequential buffer structure\n",
    "\n",
    "3. **Interpolation**:\n",
    "   - Use `linear_interp` for smooth continuous-time delays\n",
    "   - Use `round` for discrete time steps\n",
    "\n",
    "4. **Buffer sizing**:\n",
    "   - Set `time` parameter larger than maximum expected delay\n",
    "   - Buffer size = `ceil(max_delay / dt) + 1` steps\n",
    "\n",
    "5. **Initialization**:\n",
    "   - Always call `.init_state()` before simulation\n",
    "   - Provide meaningful `init` values for t < 0 history\n",
    "\n",
    "### Common Patterns\n",
    "\n",
    "**Pattern 1: Delayed synaptic connection**\n",
    "```python\n",
    "# In synapse __init__:\n",
    "self.spike_delay = brainstate.nn.Delay(jnp.zeros(pre_size), time=delay_time)\n",
    "self.spike_delay.register_entry('syn', delay_time)\n",
    "\n",
    "# In synapse update:\n",
    "self.spike_delay.update(pre_spike)\n",
    "delayed_spike = self.spike_delay.at('syn')\n",
    "```\n",
    "\n",
    "**Pattern 2: Delayed feedback**\n",
    "```python\n",
    "# In neuron __init__:\n",
    "self.V_delayed = self.prefetch_delay('V', feedback_delay)\n",
    "\n",
    "# In neuron update:\n",
    "V_past = self.V_delayed()\n",
    "feedback = compute_feedback(V_past)\n",
    "```\n",
    "\n",
    "**Pattern 3: Multiple delay taps**\n",
    "```python\n",
    "delay = brainstate.nn.Delay(data, time=max_delay)\n",
    "delay.register_entry('short', 2.0 * u.ms)\n",
    "delay.register_entry('medium', 5.0 * u.ms)\n",
    "delay.register_entry('long', 10.0 * u.ms)\n",
    "```\n",
    "\n",
    "---\n",
    "\n",
    "## Exercise\n",
    "\n",
    "**Challenge**: Implement a network of 3 LIF neurons with the following connections:\n",
    "- Neuron 0 → Neuron 1 (2ms delay, weight=1.0)\n",
    "- Neuron 1 → Neuron 2 (5ms delay, weight=1.5)\n",
    "- Neuron 2 → Neuron 0 (3ms delay, weight=-0.5, inhibitory)\n",
    "\n",
    "Use `brainstate.nn.Delay` for the synaptic connections and simulate for 200ms.\n",
    "\n",
    "**Hints**:\n",
    "1. Create 3 `SimpleLIF` neurons\n",
    "2. Create 3 `DelayedSynapse` objects for the connections\n",
    "3. Drive Neuron 0 with external input\n",
    "4. Plot voltage traces of all 3 neurons\n",
    "5. Observe the sequential activation with delays\n",
    "\n",
    "---\n",
    "\n",
    "## What's Next?\n",
    "\n",
    "- **Tutorial 7: Collective Operations** - Learn about communication patterns in neural populations\n",
    "- **Advanced Delays** - Explore plastic delays and state-dependent delays\n",
    "- **Network Architecture** - Build large-scale networks with distributed delays\n",
    "\n",
    "---\n",
    "\n",
    "**Congratulations!** You now understand BrainState's powerful delay mechanisms for modeling realistic neural dynamics. 🎉"
   ]
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