{
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    "# SNN Online Learning\n",
    "\n",
    "**Train a spiking neural network using pp-prop**\n",
    "\n",
    "Spiking Neural Networks (SNNs) process information through discrete spike events, mimicking the communication mechanism of biological neurons. Unlike traditional artificial neural networks that operate on continuous activations, SNNs emphasize the timing and frequency of spikes, making them inherently suited for temporal data processing.\n",
    "\n",
    "**Online learning** is a natural fit for SNNs because they process inputs sequentially, one time step at a time. Instead of storing the entire computation graph for backpropagation through time (BPTT), online learning algorithms update weight gradients incrementally at each time step. This eliminates the need to unroll the network over the full sequence length, resulting in constant memory usage with respect to sequence length.\n",
    "\n",
    "In this tutorial, we use `braintrace.pp_prop` (historically exposed as `braintrace.ES_D_RTRL`), an online estimator that factorizes the eligibility trace into input and output components. Its trace memory is **O(B(I+O))** (where B is batch size, I is input dimension, and O is output dimension), subject to the documented model and operator assumptions.\n",
    "\n",
    "**What you will learn:**\n",
    "\n",
    "1. Build an SNN model using `brainstate` neurons and `braintrace.nn` layers.\n",
    "2. Configure online learning with `braintrace.pp_prop`.\n",
    "3. Accumulate gradients over a spike sequence and update the model.\n",
    "4. Distinguish the D-RTRL and input/output-factorized trace regimes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58cdd32f",
   "metadata": {},
   "source": [
    "## 1. Setup\n",
    "\n",
    "First, let us import the required packages. The key components are:\n",
    "- `brainstate`: provides neuron models (LIF), state management, and JAX-based transformations\n",
    "- `braintrace`: provides online learning algorithms and ETP-aware neural network layers\n",
    "- `braintools`: provides initializers, optimizers, surrogate gradient functions, and metrics\n",
    "- `brainunit`: provides physical units (ms, mV, etc.) for biologically meaningful parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b133c828",
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    "execution": {
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   "source": [
    "import os\n",
    "os.environ.setdefault(\"JAX_PLATFORMS\", \"cpu\")\n",
    "\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "import brainstate\n",
    "import braintools\n",
    "import braintrace\n",
    "import brainunit as u\n",
    "import brainpy.state\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "brainstate.random.seed(31)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea5313f3",
   "metadata": {},
   "source": [
    "## 2. SNN Model\n",
    "\n",
    "We build a simple recurrent SNN with the following architecture:\n",
    "\n",
    "1. **Input + Recurrent Projection**: A `braintrace.nn.Linear` layer that projects the concatenation of input spikes and recurrent spikes into the hidden layer. Using `braintrace.nn.Linear` (instead of a plain matrix multiply) marks this projection for participation in online learning via ETP primitives.\n",
    "\n",
    "2. **LIF Neuron**: A Leaky Integrate-and-Fire neuron from `brainpy.state.LIF`. The LIF neuron integrates its input current, fires a spike when the membrane potential exceeds a threshold, and then resets. We use `braintools.surrogate.ReluGrad()` as the surrogate gradient function for differentiability.\n",
    "\n",
    "3. **Readout**: A `braintrace.nn.LeakyRateReadout` that applies leaky integration to the recurrent spikes and produces a continuous output signal for classification. This layer is also ETP-aware.\n",
    "\n",
    "The recurrent connectivity is achieved by concatenating the neuron's own spike output with the external input at each time step."
   ]
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   "execution_count": 2,
   "id": "cef17cc9",
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    "execution": {
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   "source": [
    "class LIF_SNN(brainstate.nn.Module):\n",
    "    \"\"\"A simple recurrent SNN with LIF neurons for online learning.\"\"\"\n",
    "\n",
    "    def __init__(self, n_in, n_rec, n_out, tau_mem=20. * u.ms, tau_out=20. * u.ms):\n",
    "        super().__init__()\n",
    "\n",
    "        # Input + recurrent projection (ETP-aware: participates in online learning).\n",
    "        # Weights are in current units so that ``I * R`` lands in mV inside the LIF\n",
    "        # neuron (LIF integrates ``-V + I*R``; ``mA * ohm = mV`` matches V_th below).\n",
    "        self.linear = braintrace.nn.Linear(\n",
    "            n_in + n_rec, n_rec,\n",
    "            w_init=braintools.init.KaimingNormal(scale=50., unit=u.mA),\n",
    "            b_init=braintools.init.ZeroInit(unit=u.mA),\n",
    "        )\n",
    "\n",
    "        # LIF neuron with surrogate gradient for differentiability.\n",
    "        self.neuron = brainpy.state.LIF(\n",
    "            n_rec,\n",
    "            tau=tau_mem,\n",
    "            R=1. * u.ohm,\n",
    "            V_th=0.1 * u.mV,\n",
    "            V_reset=0. * u.mV,\n",
    "            V_rest=0. * u.mV,\n",
    "            spk_fun=braintools.surrogate.ReluGrad(),\n",
    "            spk_reset='soft',\n",
    "        )\n",
    "\n",
    "        # Readout layer (ETP-aware: participates in online learning).\n",
    "        self.readout = braintrace.nn.LeakyRateReadout(\n",
    "            n_rec, n_out,\n",
    "            tau=tau_out,\n",
    "            w_init=braintools.init.KaimingNormal(),\n",
    "        )\n",
    "\n",
    "    def update(self, spike_input):\n",
    "        # Concatenate input spikes with recurrent spikes.\n",
    "        rec_spk = self.neuron.get_spike()\n",
    "        x = jnp.concatenate([spike_input, rec_spk], axis=-1)\n",
    "\n",
    "        # Linear projection -> LIF neuron dynamics -> readout.\n",
    "        spike = self.neuron(self.linear(x))\n",
    "        return self.readout(spike)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3938aef7",
   "metadata": {},
   "source": [
    "Let us verify that the model can be instantiated and produce output for a single sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8f0885a3",
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    "execution": {
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    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output shape: (10,)\n",
      "Output values: [ -0.73483455 -29.564928    11.474621     5.180525    21.198288\n",
      "   3.3511796   -3.6710746   -0.11585347  -0.3551005  -11.977058  ]\n",
      "Mean |output|: 5.3924\n"
     ]
    }
   ],
   "source": [
    "with brainstate.environ.context(dt=1. * u.ms):\n",
    "    model = LIF_SNN(n_in=50, n_rec=128, n_out=10)\n",
    "    brainstate.nn.init_all_states(model)\n",
    "\n",
    "    # A short structured spike train verifies non-zero network activity.\n",
    "    test_inputs = brainstate.random.bernoulli(\n",
    "        0.6, size=(20, 50)\n",
    "    ).astype(jnp.float32)\n",
    "    outputs = brainstate.transform.for_loop(model, test_inputs)\n",
    "    output = outputs[-1]\n",
    "    print(f\"Output shape: {output.shape}\")\n",
    "    print(f\"Output values: {output}\")\n",
    "    print(f\"Mean |output|: {float(jnp.mean(jnp.abs(outputs))):.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43f58871",
   "metadata": {},
   "source": [
    "## 3. Training with pp-prop on an aligned temporal window\n",
    "\n",
    "We use `braintrace.pp_prop`, backed by `braintrace.IODimVjpAlgorithm`.\n",
    "`decay_or_rank` controls the trace approximation: a float in `[0, 1)` is the\n",
    "exponential-smoothing decay, while an integer at least 1 maps to\n",
    "`decay = (rank - 1) / (rank + 1)`. The decay is part of the estimator and must\n",
    "be reported with results.\n",
    "\n",
    "This workflow trains one sequence at a time, so it needs neither a batch axis\n",
    "nor state mapping. The learner is compiled from one feature vector, not an\n",
    "entire temporal sequence. The warm-up mask drives neuron and eligibility states\n",
    "without scoring the prefix; the returned loss and accuracy both use the same\n",
    "post-warm-up time steps. `braintrace.D_RTRL` is the parameter-dimensional\n",
    "alternative; neither estimator is generally gradient-equivalent to BPTT\n",
    "outside its documented assumptions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2e58fc27",
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    "execution": {
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   "source": [
    "def make_spike_classification_sequences(\n",
    "    n_updates=80, n_steps=60, n_in=50, n_out=10\n",
    "):\n",
    "    \"\"\"Create class-specific spike sequences without a batch axis.\"\"\"\n",
    "    labels = jnp.arange(n_updates, dtype=jnp.int32) % n_out\n",
    "    channel_class = jnp.arange(n_in) * n_out // n_in\n",
    "    active = labels[:, None] == channel_class[None, :]\n",
    "    firing_probability = jnp.where(active, 0.6, 0.0)\n",
    "    inputs = brainstate.random.bernoulli(\n",
    "        firing_probability[:, None, :],\n",
    "        size=(n_updates, n_steps, n_in),\n",
    "    ).astype(jnp.float32)\n",
    "    return inputs, labels\n",
    "\n",
    "\n",
    "def train_snn(input_sequences, targets, n_rec=128, lr=3e-3):\n",
    "    \"\"\"Train one recurrent SNN sequence at a time with pp-prop.\"\"\"\n",
    "    n_in = input_sequences.shape[-1]\n",
    "    n_out = int(targets.max()) + 1\n",
    "\n",
    "    with brainstate.environ.context(dt=1. * u.ms):\n",
    "        brainstate.random.seed(37)\n",
    "        model = LIF_SNN(n_in, n_rec, n_out)\n",
    "        brainstate.nn.init_all_states(model)\n",
    "        learner = braintrace.pp_prop(model, decay_or_rank=0.5)\n",
    "        learner.compile_graph(input_sequences[0, 0])\n",
    "\n",
    "        opt = braintools.optim.Adam(lr)\n",
    "        opt.register_trainable_weights(learner.param_states)\n",
    "\n",
    "        warmup = input_sequences.shape[1] // 5\n",
    "        loss_mask = (\n",
    "            jnp.arange(input_sequences.shape[1]) >= warmup\n",
    "        ).astype(jnp.float32)\n",
    "\n",
    "        @brainstate.transform.jit\n",
    "        def train_step(inputs, target):\n",
    "            brainstate.nn.reset_all_states(model)\n",
    "            learner.reset_state()\n",
    "\n",
    "            def step_loss(inp):\n",
    "                output = learner(inp)\n",
    "                loss = braintools.metric.softmax_cross_entropy_with_integer_labels(\n",
    "                    output, target\n",
    "                ).mean()\n",
    "                return loss, output\n",
    "\n",
    "            grads, objective, outputs = learner.etrace_grad(\n",
    "                inputs,\n",
    "                step_fn=step_loss,\n",
    "                has_aux=True,\n",
    "                mask=loss_mask,\n",
    "                reduction='mean',\n",
    "                loss_output='scalar',\n",
    "                return_value=True,\n",
    "            )\n",
    "            opt.update(brainstate.nn.clip_grad_norm(grads, 1.0))\n",
    "            predictions = jnp.argmax(outputs, axis=-1)\n",
    "            correct = (predictions == target).astype(jnp.float32)\n",
    "            accuracy = jnp.sum(correct * loss_mask) / jnp.sum(loss_mask)\n",
    "            return objective, accuracy\n",
    "\n",
    "        return brainstate.transform.for_loop(\n",
    "            train_step, input_sequences, targets\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72b54601",
   "metadata": {},
   "source": [
    "Run the fixed-seed sequence updates. The first update is slower because the transformed training step is compiled.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3113f9a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-07T14:19:56.511616Z",
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     "shell.execute_reply": "2026-08-07T14:19:58.880645Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Update  0, Loss: 5.3968, Accuracy: 0.208\n",
      "Update 20, Loss: 1.1892, Accuracy: 0.500\n",
      "Update 40, Loss: 0.8719, Accuracy: 0.708\n",
      "Update 60, Loss: 0.6732, Accuracy: 0.771\n",
      "Update 79, Loss: 0.1277, Accuracy: 1.000\n"
     ]
    }
   ],
   "source": [
    "brainstate.random.seed(41)\n",
    "spike_inputs, spike_targets = make_spike_classification_sequences()\n",
    "losses, accuracies = train_snn(spike_inputs, spike_targets)\n",
    "for update in (0, 20, 40, 60, 79):\n",
    "    print(\n",
    "        f\"Update {update:2d}, Loss: {float(losses[update]):.4f}, \"\n",
    "        f\"Accuracy: {float(accuracies[update]):.3f}\"\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6b876ff",
   "metadata": {},
   "source": [
    "Plot the masked objective and post-warm-up\n",
    "accuracy over 80 single-sequence parameter updates. This fixed task is an\n",
    "optimization smoke check: it can establish finite execution and task-specific\n",
    "descent, but not pp-prop/BPTT equality or general convergence.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "516e6f4a",
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 900x380 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context(\"default\"), plt.rc_context({\n",
    "    \"figure.facecolor\": \"white\",\n",
    "    \"axes.facecolor\": \"white\",\n",
    "    \"savefig.facecolor\": \"white\",\n",
    "}):\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(9, 3.8))\n",
    "    axes[0].plot(losses, color=\"#2563eb\")\n",
    "    axes[0].set(xlabel=\"Parameter update\", ylabel=\"Cross-entropy loss\")\n",
    "    axes[0].set_title(\"pp-prop training loss\")\n",
    "    axes[1].plot(accuracies, color=\"#15803d\")\n",
    "    axes[1].set(xlabel=\"Parameter update\", ylabel=\"Accuracy\", ylim=(0, 1.05))\n",
    "    axes[1].set_title(\"Structured-spike accuracy\")\n",
    "    for axis in axes:\n",
    "        axis.grid(True, alpha=0.3)\n",
    "    fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdbdc871",
   "metadata": {},
   "source": [
    "## 4. Key differences: D-RTRL vs pp-prop\n",
    "\n",
    "| Aspect | D-RTRL (`ParamDimVjpAlgorithm`) | pp-prop (`pp_prop`) |\n",
    "|---|---|---|\n",
    "| Eligibility trace | Parameter-shaped, diagonal hidden-Jacobian approximation | Input/output-factorized with smoothing |\n",
    "| Main cost | Grows with traced parameter and hidden dimensions | Grows with retained input/output factors |\n",
    "| Main approximation | Drops cross-position hidden-Jacobian terms | Drops correlations outside the factorization |\n",
    "| Appropriate use | Parameter-shaped trace is affordable | Lower trace memory is required and validated |\n",
    "\n",
    "`decay_or_rank` is part of the pp-prop estimator. Validate either rule with a\n",
    "finite-window oracle on a reduced version of the intended model before making a\n",
    "gradient-fidelity claim. See {class}`braintrace.D_RTRL`,\n",
    "{class}`braintrace.pp_prop`, and {func}`braintrace.compile`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa87fa0a",
   "metadata": {},
   "source": [
    "## 5. Summary\n",
    "\n",
    "This workflow advances the LIF neuron exactly once per logical time step,\n",
    "compiles from one time-step input, and evaluates the same post-warm-up window\n",
    "used by the training objective. Repeated execution is owned by\n",
    "`brainstate.transform` and `learner.etrace_grad`, not a Python model loop.\n",
    "\n",
    "Continue with the focused [pp-prop algorithm tutorial](pp_prop.ipynb),\n",
    "[e-prop](eprop.ipynb), and the [algorithm API reference](../apis/algorithms.rst).\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
