{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "661d1cc5",
   "metadata": {},
   "source": [
    "# STRF gradmaps: 2D-CNN on CRCNS AA2 (OV / conspecific)\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/urancon/deepSTRF/blob/develop/examples/strf_gradmap_aa2.ipynb)\n",
    "\n",
    "This notebook fits the **2D-CNN** of Pennington & David (2023) on a\n",
    "slice of the **CRCNS AA2** auditory dataset (zebra finch ovoidalis,\n",
    "OV — a thalamic nucleus relaying auditory information to forebrain\n",
    "Field L) and extracts a **per-neuron STRF gradient map** from the\n",
    "trained model — the canonical interpretable \"what does this cell\n",
    "respond to?\" figure for nonlinear receptive-field models.\n",
    "\n",
    "The 2D-CNN doesn't have an explicit STRF parameter to plot. Instead,\n",
    "we ask: at the neuron's last-timestep prediction, what change in a\n",
    "null spectrogram (zeros) most increases the response? Autodiff\n",
    "gives that gradient in closed form — a `(F, T)` map per neuron.\n",
    "The deepSTRF base class implements this in\n",
    "`AudioEncodingModel.STRF_gradmap()`; the batch dimension parallelises\n",
    "the calculation across all `N` output neurons in a single\n",
    "forward / backward pass.\n",
    "\n",
    "Scope: 59 OV cells × 20 conspecific stimuli at `dt_ms=5`. 100%\n",
    "valid (s, n) coverage on this subset, so no NaN-aware bookkeeping\n",
    "is exercised here — the same notebook on the full 494-cell, 117-\n",
    "stim AA2 would be the next step (see \"What's next\").\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2e22847",
   "metadata": {},
   "source": [
    "## Setup — Google Colab\n",
    "\n",
    "If you're running on Google Colab, the cell below installs deepSTRF\n",
    "from source. Local installs (`pip install -e .`) are no-ops.\n",
    "\n",
    "**Note on data**: AA2 is an authenticated CRCNS dataset. To\n",
    "auto-download it, set `$CRCNS_USERNAME` and `$CRCNS_PASSWORD` (free\n",
    "account at https://crcns.org/). On a local machine that already has\n",
    "the data extracted, it's picked up from the cache automatically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "858f2e9f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-18T14:01:18.226365Z",
     "iopub.status.busy": "2026-05-18T14:01:18.225990Z",
     "iopub.status.idle": "2026-05-18T14:01:18.239593Z",
     "shell.execute_reply": "2026-05-18T14:01:18.238018Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Local environment — assuming deepSTRF is already importable.\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "if 'google.colab' in sys.modules:\n",
    "    !pip install -q git+https://github.com/urancon/deepSTRF.git\n",
    "    print(\"deepSTRF installed from GitHub.\")\n",
    "else:\n",
    "    print(\"Local environment — assuming deepSTRF is already importable.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b90b26b",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b9702466",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-18T14:01:18.243485Z",
     "iopub.status.busy": "2026-05-18T14:01:18.243125Z",
     "iopub.status.idle": "2026-05-18T14:01:20.965805Z",
     "shell.execute_reply": "2026-05-18T14:01:20.964487Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "from torch.utils.data import DataLoader, Subset\n",
    "\n",
    "from deepSTRF.datasets.audio.crcns_aa2 import CRCNSAA2Dataset\n",
    "from deepSTRF.models.audio import ConvNet2D\n",
    "from deepSTRF.training import Fitter, set_random_seed\n",
    "from deepSTRF.utils import neural_collate, plot_strf_grid\n",
    "\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "print(f\"Using device: {device}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c31cdb7",
   "metadata": {},
   "source": [
    "## 1. Load CRCNS AA2 — OV cells, conspecific stimuli only\n",
    "\n",
    "We narrow the dataset at construction time via the `areas=` and\n",
    "`stimuli=` arguments. A separate notebook covers the `select_*_by_attr`\n",
    "runtime filtering API and the bidirectional rule from\n",
    "`data_paradigm.md` §8; here we just want a clean small subset to\n",
    "showcase gradmaps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a8b54d40",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-18T14:01:20.967838Z",
     "iopub.status.busy": "2026-05-18T14:01:20.967556Z",
     "iopub.status.idle": "2026-05-18T14:01:27.161569Z",
     "shell.execute_reply": "2026-05-18T14:01:27.160550Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OV + conspecific: N=59 cells, S=20 stims, F=32 bands, dt=5 ms\n",
      "stim T range: 234..511 frames (1.17 s .. 2.56 s)\n",
      "valid (s, n) pairs: 100.0%\n"
     ]
    }
   ],
   "source": [
    "ds = CRCNSAA2Dataset(download=True, dt_ms=5, areas=('OV',), stimuli=('conspecific',))\n",
    "print(f\"OV + conspecific: N={ds.N_neurons} cells, S={len(ds.stims)} stims, \"\n",
    "      f\"F={ds.F} bands, dt={ds.dt} ms\")\n",
    "\n",
    "T_min = min(s.shape[-1] for s in ds.stims)\n",
    "T_max = max(s.shape[-1] for s in ds.stims)\n",
    "print(f\"stim T range: {T_min}..{T_max} frames \"\n",
    "      f\"({T_min*ds.dt/1000:.2f} s .. {T_max*ds.dt/1000:.2f} s)\")\n",
    "\n",
    "valid_frac = ds.nrn_masks.float().mean().item()\n",
    "print(f\"valid (s, n) pairs: {100*valid_frac:.1f}%\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33a3282a",
   "metadata": {},
   "source": [
    "## 2. Train / val / test split + per-band standardisation\n",
    "\n",
    "14 / 3 / 3 split, all conspecific. Standardisation statistics are\n",
    "computed on train+val and applied to all stimuli — the held-out\n",
    "test set is transformed with the same mean and std."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c8ebd0a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-18T14:01:27.163542Z",
     "iopub.status.busy": "2026-05-18T14:01:27.163281Z",
     "iopub.status.idle": "2026-05-18T14:01:27.172327Z",
     "shell.execute_reply": "2026-05-18T14:01:27.171027Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "split: train 14 | val 3 | test 3\n",
      "per-band mean range: [0.007, 1.557]\n",
      "per-band std range:  [0.001, 1.433]\n"
     ]
    }
   ],
   "source": [
    "set_random_seed(0)\n",
    "S = len(ds)\n",
    "g = torch.Generator().manual_seed(0)\n",
    "perm = torch.randperm(S, generator=g).tolist()\n",
    "train_idx = perm[:14]\n",
    "val_idx   = perm[14:17]\n",
    "test_idx  = perm[17:20]\n",
    "print(f\"split: train {len(train_idx)} | val {len(val_idx)} | test {len(test_idx)}\")\n",
    "\n",
    "stats = ds.standardize_stims(stim_indices=train_idx + val_idx, per_band=True)\n",
    "print(f\"per-band mean range: [{stats['mean'].min():.3f}, {stats['mean'].max():.3f}]\")\n",
    "print(f\"per-band std range:  [{stats['std'].min():.3f}, {stats['std'].max():.3f}]\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bce47468",
   "metadata": {},
   "source": [
    "## 3. Build the 2D-CNN and fit\n",
    "\n",
    "`ConvNet2D` is the deepSTRF port of Pennington & David's 2D-CNN:\n",
    "three (Conv2d → CausalLayerNorm → LeakyReLU) blocks over the\n",
    "spectrogram, then a 2-layer FC reading out the population. Default\n",
    "`temporal_window_size = 3·(K_T − 1) = 24` frames (120 ms at our\n",
    "5 ms resolution) sets the gradmap window we'll plot in section 5.\n",
    "\n",
    "Hyperparameters:\n",
    "\n",
    "- `batch_size=4` — with only 14 train stims, larger batches collapse\n",
    "  to gradient descent (≤ 1 step per epoch) and lose SGD noise. On a\n",
    "  bigger AA2 subset (more stims) you can dial this up, on this\n",
    "  particular GPU up to ~32.\n",
    "- `patience=50` on val cc_norm — the demo set is tiny so each\n",
    "  epoch is quick and we can afford a long patience.\n",
    "- `track_train_metrics=False` — skip the per-epoch recomputation of\n",
    "  cc / cc_norm on the training accumulator; not useful at this\n",
    "  scale and saves a few seconds per epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e7e87a0a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-18T14:01:27.174593Z",
     "iopub.status.busy": "2026-05-18T14:01:27.174358Z",
     "iopub.status.idle": "2026-05-18T14:02:07.828911Z",
     "shell.execute_reply": "2026-05-18T14:02:07.827932Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ConvNet2D params: 12,401, gradmap window: T=24 frames (120 ms)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch    0 | train_loss=0.0554 | val_loss=0.0456 | val_cc=0.0113 | val_cc_norm=0.0055\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch    1 | train_loss=0.0450 | val_loss=0.0388 | val_cc=0.0360 | val_cc_norm=0.0248\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch    2 | train_loss=0.0385 | val_loss=0.0352 | val_cc=0.0702 | val_cc_norm=0.0615\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch    3 | train_loss=0.0351 | val_loss=0.0329 | val_cc=0.1782 | val_cc_norm=0.1957\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch    4 | train_loss=0.0328 | val_loss=0.0314 | val_cc=0.2422 | val_cc_norm=0.2773\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch    5 | train_loss=0.0314 | val_loss=0.0303 | val_cc=0.2746 | val_cc_norm=0.3216\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch    6 | train_loss=0.0299 | val_loss=0.0288 | val_cc=0.3145 | val_cc_norm=0.3786\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch    7 | train_loss=0.0286 | val_loss=0.0280 | val_cc=0.3576 | val_cc_norm=0.4388\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch    8 | train_loss=0.0278 | val_loss=0.0276 | val_cc=0.3767 | val_cc_norm=0.4663\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch    9 | train_loss=0.0273 | val_loss=0.0270 | val_cc=0.3874 | val_cc_norm=0.4808\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   10 | train_loss=0.0271 | val_loss=0.0274 | val_cc=0.3989 | val_cc_norm=0.4969\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   11 | train_loss=0.0269 | val_loss=0.0269 | val_cc=0.4001 | val_cc_norm=0.5006\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   12 | train_loss=0.0268 | val_loss=0.0262 | val_cc=0.4083 | val_cc_norm=0.5127\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   13 | train_loss=0.0264 | val_loss=0.0258 | val_cc=0.4225 | val_cc_norm=0.5291\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   14 | train_loss=0.0264 | val_loss=0.0257 | val_cc=0.4285 | val_cc_norm=0.5398\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   15 | train_loss=0.0255 | val_loss=0.0253 | val_cc=0.4345 | val_cc_norm=0.5471\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   16 | train_loss=0.0251 | val_loss=0.0248 | val_cc=0.4418 | val_cc_norm=0.5560\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   17 | train_loss=0.0247 | val_loss=0.0252 | val_cc=0.4480 | val_cc_norm=0.5643\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   18 | train_loss=0.0245 | val_loss=0.0247 | val_cc=0.4579 | val_cc_norm=0.5766\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   19 | train_loss=0.0245 | val_loss=0.0242 | val_cc=0.4589 | val_cc_norm=0.5792\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   20 | train_loss=0.0243 | val_loss=0.0240 | val_cc=0.4653 | val_cc_norm=0.5872\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   21 | train_loss=0.0239 | val_loss=0.0249 | val_cc=0.4637 | val_cc_norm=0.5853\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   22 | train_loss=0.0235 | val_loss=0.0235 | val_cc=0.4756 | val_cc_norm=0.6003\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   23 | train_loss=0.0236 | val_loss=0.0247 | val_cc=0.4789 | val_cc_norm=0.6048\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   24 | train_loss=0.0234 | val_loss=0.0238 | val_cc=0.4794 | val_cc_norm=0.6061\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   25 | train_loss=0.0231 | val_loss=0.0240 | val_cc=0.4835 | val_cc_norm=0.6111\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   26 | train_loss=0.0231 | val_loss=0.0252 | val_cc=0.4706 | val_cc_norm=0.5954\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   27 | train_loss=0.0233 | val_loss=0.0246 | val_cc=0.4768 | val_cc_norm=0.6035\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   28 | train_loss=0.0231 | val_loss=0.0240 | val_cc=0.4882 | val_cc_norm=0.6179\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   29 | train_loss=0.0228 | val_loss=0.0233 | val_cc=0.4898 | val_cc_norm=0.6204\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   30 | train_loss=0.0225 | val_loss=0.0244 | val_cc=0.4885 | val_cc_norm=0.6191\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   31 | train_loss=0.0223 | val_loss=0.0235 | val_cc=0.4887 | val_cc_norm=0.6197\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   32 | train_loss=0.0225 | val_loss=0.0233 | val_cc=0.4909 | val_cc_norm=0.6230\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   33 | train_loss=0.0222 | val_loss=0.0230 | val_cc=0.4942 | val_cc_norm=0.6272\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   34 | train_loss=0.0219 | val_loss=0.0234 | val_cc=0.4888 | val_cc_norm=0.6206\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   35 | train_loss=0.0221 | val_loss=0.0234 | val_cc=0.4882 | val_cc_norm=0.6206\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   36 | train_loss=0.0220 | val_loss=0.0231 | val_cc=0.4929 | val_cc_norm=0.6270\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   37 | train_loss=0.0217 | val_loss=0.0231 | val_cc=0.4993 | val_cc_norm=0.6340\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   38 | train_loss=0.0216 | val_loss=0.0229 | val_cc=0.5045 | val_cc_norm=0.6422\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   39 | train_loss=0.0215 | val_loss=0.0232 | val_cc=0.4990 | val_cc_norm=0.6357\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   40 | train_loss=0.0213 | val_loss=0.0224 | val_cc=0.5076 | val_cc_norm=0.6465\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   41 | train_loss=0.0213 | val_loss=0.0221 | val_cc=0.5104 | val_cc_norm=0.6505\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   42 | train_loss=0.0211 | val_loss=0.0222 | val_cc=0.5133 | val_cc_norm=0.6543\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   43 | train_loss=0.0212 | val_loss=0.0226 | val_cc=0.5038 | val_cc_norm=0.6425\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   44 | train_loss=0.0208 | val_loss=0.0228 | val_cc=0.5093 | val_cc_norm=0.6499\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   45 | train_loss=0.0208 | val_loss=0.0239 | val_cc=0.5044 | val_cc_norm=0.6431\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   46 | train_loss=0.0211 | val_loss=0.0225 | val_cc=0.5121 | val_cc_norm=0.6528\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   47 | train_loss=0.0210 | val_loss=0.0220 | val_cc=0.5161 | val_cc_norm=0.6585\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   48 | train_loss=0.0209 | val_loss=0.0232 | val_cc=0.5103 | val_cc_norm=0.6509\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   49 | train_loss=0.0205 | val_loss=0.0222 | val_cc=0.5137 | val_cc_norm=0.6553\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   50 | train_loss=0.0207 | val_loss=0.0231 | val_cc=0.5099 | val_cc_norm=0.6503\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   51 | train_loss=0.0203 | val_loss=0.0225 | val_cc=0.5163 | val_cc_norm=0.6593\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   52 | train_loss=0.0203 | val_loss=0.0219 | val_cc=0.5173 | val_cc_norm=0.6607\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   53 | train_loss=0.0201 | val_loss=0.0222 | val_cc=0.5179 | val_cc_norm=0.6615\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   54 | train_loss=0.0200 | val_loss=0.0218 | val_cc=0.5195 | val_cc_norm=0.6635\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   55 | train_loss=0.0198 | val_loss=0.0231 | val_cc=0.5113 | val_cc_norm=0.6529\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   56 | train_loss=0.0198 | val_loss=0.0219 | val_cc=0.5205 | val_cc_norm=0.6650\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   57 | train_loss=0.0199 | val_loss=0.0224 | val_cc=0.5208 | val_cc_norm=0.6655\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   58 | train_loss=0.0198 | val_loss=0.0221 | val_cc=0.5236 | val_cc_norm=0.6693\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   59 | train_loss=0.0197 | val_loss=0.0217 | val_cc=0.5209 | val_cc_norm=0.6661\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   60 | train_loss=0.0198 | val_loss=0.0219 | val_cc=0.5186 | val_cc_norm=0.6628\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   61 | train_loss=0.0198 | val_loss=0.0219 | val_cc=0.5222 | val_cc_norm=0.6678\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   62 | train_loss=0.0196 | val_loss=0.0231 | val_cc=0.5086 | val_cc_norm=0.6500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   63 | train_loss=0.0196 | val_loss=0.0223 | val_cc=0.5221 | val_cc_norm=0.6670\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   64 | train_loss=0.0195 | val_loss=0.0216 | val_cc=0.5263 | val_cc_norm=0.6730\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   65 | train_loss=0.0193 | val_loss=0.0218 | val_cc=0.5245 | val_cc_norm=0.6709\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   66 | train_loss=0.0194 | val_loss=0.0225 | val_cc=0.5199 | val_cc_norm=0.6647\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   67 | train_loss=0.0194 | val_loss=0.0226 | val_cc=0.5181 | val_cc_norm=0.6625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   68 | train_loss=0.0192 | val_loss=0.0223 | val_cc=0.5268 | val_cc_norm=0.6735\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   69 | train_loss=0.0191 | val_loss=0.0220 | val_cc=0.5247 | val_cc_norm=0.6711\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   70 | train_loss=0.0189 | val_loss=0.0225 | val_cc=0.5242 | val_cc_norm=0.6706\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   71 | train_loss=0.0188 | val_loss=0.0223 | val_cc=0.5169 | val_cc_norm=0.6608\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   72 | train_loss=0.0188 | val_loss=0.0217 | val_cc=0.5240 | val_cc_norm=0.6702\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   73 | train_loss=0.0188 | val_loss=0.0222 | val_cc=0.5252 | val_cc_norm=0.6714\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   74 | train_loss=0.0185 | val_loss=0.0227 | val_cc=0.5195 | val_cc_norm=0.6641\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   75 | train_loss=0.0185 | val_loss=0.0220 | val_cc=0.5242 | val_cc_norm=0.6701\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   76 | train_loss=0.0184 | val_loss=0.0224 | val_cc=0.5198 | val_cc_norm=0.6643\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   77 | train_loss=0.0183 | val_loss=0.0218 | val_cc=0.5263 | val_cc_norm=0.6731\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   78 | train_loss=0.0183 | val_loss=0.0224 | val_cc=0.5164 | val_cc_norm=0.6599\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   79 | train_loss=0.0182 | val_loss=0.0221 | val_cc=0.5209 | val_cc_norm=0.6658\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   80 | train_loss=0.0183 | val_loss=0.0234 | val_cc=0.5122 | val_cc_norm=0.6545\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   81 | train_loss=0.0182 | val_loss=0.0216 | val_cc=0.5288 | val_cc_norm=0.6766\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   82 | train_loss=0.0181 | val_loss=0.0220 | val_cc=0.5198 | val_cc_norm=0.6645\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   83 | train_loss=0.0182 | val_loss=0.0220 | val_cc=0.5241 | val_cc_norm=0.6703\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   84 | train_loss=0.0180 | val_loss=0.0225 | val_cc=0.5182 | val_cc_norm=0.6625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   85 | train_loss=0.0180 | val_loss=0.0219 | val_cc=0.5263 | val_cc_norm=0.6733\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   86 | train_loss=0.0179 | val_loss=0.0222 | val_cc=0.5186 | val_cc_norm=0.6633\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   87 | train_loss=0.0179 | val_loss=0.0218 | val_cc=0.5239 | val_cc_norm=0.6701\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   88 | train_loss=0.0178 | val_loss=0.0225 | val_cc=0.5215 | val_cc_norm=0.6664\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   89 | train_loss=0.0177 | val_loss=0.0227 | val_cc=0.5176 | val_cc_norm=0.6622\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   90 | train_loss=0.0178 | val_loss=0.0224 | val_cc=0.5188 | val_cc_norm=0.6635\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   91 | train_loss=0.0176 | val_loss=0.0227 | val_cc=0.5179 | val_cc_norm=0.6622\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   92 | train_loss=0.0175 | val_loss=0.0220 | val_cc=0.5217 | val_cc_norm=0.6672\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   93 | train_loss=0.0175 | val_loss=0.0228 | val_cc=0.5224 | val_cc_norm=0.6683\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   94 | train_loss=0.0175 | val_loss=0.0228 | val_cc=0.5158 | val_cc_norm=0.6594\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   95 | train_loss=0.0173 | val_loss=0.0221 | val_cc=0.5232 | val_cc_norm=0.6694\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   96 | train_loss=0.0173 | val_loss=0.0223 | val_cc=0.5193 | val_cc_norm=0.6643\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   97 | train_loss=0.0173 | val_loss=0.0224 | val_cc=0.5180 | val_cc_norm=0.6628\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   98 | train_loss=0.0172 | val_loss=0.0228 | val_cc=0.5176 | val_cc_norm=0.6616\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   99 | train_loss=0.0173 | val_loss=0.0220 | val_cc=0.5239 | val_cc_norm=0.6702\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  100 | train_loss=0.0172 | val_loss=0.0223 | val_cc=0.5184 | val_cc_norm=0.6633\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  101 | train_loss=0.0172 | val_loss=0.0229 | val_cc=0.5195 | val_cc_norm=0.6642\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  102 | train_loss=0.0171 | val_loss=0.0227 | val_cc=0.5163 | val_cc_norm=0.6603\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  103 | train_loss=0.0171 | val_loss=0.0224 | val_cc=0.5181 | val_cc_norm=0.6628\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  104 | train_loss=0.0170 | val_loss=0.0223 | val_cc=0.5165 | val_cc_norm=0.6605\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  105 | train_loss=0.0171 | val_loss=0.0229 | val_cc=0.5151 | val_cc_norm=0.6582\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  106 | train_loss=0.0169 | val_loss=0.0225 | val_cc=0.5195 | val_cc_norm=0.6644\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  107 | train_loss=0.0169 | val_loss=0.0221 | val_cc=0.5199 | val_cc_norm=0.6651\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  108 | train_loss=0.0169 | val_loss=0.0231 | val_cc=0.5175 | val_cc_norm=0.6620\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  109 | train_loss=0.0169 | val_loss=0.0231 | val_cc=0.5116 | val_cc_norm=0.6543\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  110 | train_loss=0.0171 | val_loss=0.0225 | val_cc=0.5161 | val_cc_norm=0.6604\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  111 | train_loss=0.0170 | val_loss=0.0232 | val_cc=0.5139 | val_cc_norm=0.6574\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  112 | train_loss=0.0168 | val_loss=0.0225 | val_cc=0.5193 | val_cc_norm=0.6644\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  113 | train_loss=0.0167 | val_loss=0.0223 | val_cc=0.5220 | val_cc_norm=0.6679\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  114 | train_loss=0.0166 | val_loss=0.0227 | val_cc=0.5168 | val_cc_norm=0.6609\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  115 | train_loss=0.0167 | val_loss=0.0225 | val_cc=0.5204 | val_cc_norm=0.6659\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  116 | train_loss=0.0166 | val_loss=0.0222 | val_cc=0.5256 | val_cc_norm=0.6727\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  117 | train_loss=0.0166 | val_loss=0.0231 | val_cc=0.5200 | val_cc_norm=0.6653\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  118 | train_loss=0.0165 | val_loss=0.0226 | val_cc=0.5158 | val_cc_norm=0.6595\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  119 | train_loss=0.0165 | val_loss=0.0229 | val_cc=0.5165 | val_cc_norm=0.6607\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  120 | train_loss=0.0166 | val_loss=0.0228 | val_cc=0.5185 | val_cc_norm=0.6631\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  121 | train_loss=0.0165 | val_loss=0.0223 | val_cc=0.5216 | val_cc_norm=0.6675\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  122 | train_loss=0.0165 | val_loss=0.0232 | val_cc=0.5208 | val_cc_norm=0.6667\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  123 | train_loss=0.0164 | val_loss=0.0229 | val_cc=0.5192 | val_cc_norm=0.6641\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  124 | train_loss=0.0163 | val_loss=0.0225 | val_cc=0.5250 | val_cc_norm=0.6720\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  125 | train_loss=0.0164 | val_loss=0.0238 | val_cc=0.5144 | val_cc_norm=0.6582\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  126 | train_loss=0.0165 | val_loss=0.0231 | val_cc=0.5100 | val_cc_norm=0.6519\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  127 | train_loss=0.0165 | val_loss=0.0226 | val_cc=0.5192 | val_cc_norm=0.6642\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  128 | train_loss=0.0164 | val_loss=0.0230 | val_cc=0.5174 | val_cc_norm=0.6618\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  129 | train_loss=0.0162 | val_loss=0.0230 | val_cc=0.5169 | val_cc_norm=0.6613\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  130 | train_loss=0.0161 | val_loss=0.0226 | val_cc=0.5217 | val_cc_norm=0.6681\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  131 | train_loss=0.0161 | val_loss=0.0230 | val_cc=0.5150 | val_cc_norm=0.6587\n",
      "\n",
      "fit completed: 132 epochs in 0.7 min\n"
     ]
    }
   ],
   "source": [
    "N = ds.N_neurons\n",
    "train_loader = DataLoader(Subset(ds, train_idx), batch_size=1,\n",
    "                          shuffle=True, collate_fn=neural_collate)\n",
    "val_loader   = DataLoader(Subset(ds, val_idx),   batch_size=1,\n",
    "                          shuffle=False, collate_fn=neural_collate)\n",
    "test_loader  = DataLoader(Subset(ds, test_idx),  batch_size=1,\n",
    "                          shuffle=False, collate_fn=neural_collate)\n",
    "\n",
    "set_random_seed(0)\n",
    "model = ConvNet2D(\n",
    "    n_frequency_bands=ds.F, kernel_size=(3, 9),\n",
    "    c_hidden=10, n_hidden=20, out_neurons=N,\n",
    ")\n",
    "print(f\"ConvNet2D params: {model.count_trainable_params():,}, \"\n",
    "      f\"gradmap window: T={model.T} frames ({model.T * ds.dt} ms)\")\n",
    "\n",
    "optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=0.0)\n",
    "fitter = Fitter(\n",
    "    model, train_loader, val_loader,\n",
    "    optimizer=optimizer, device=device,\n",
    "    max_epochs=2000, patience=50,\n",
    "    monitor=\"val_cc_norm\", mode=\"max\",\n",
    "    track_train_metrics=False,\n",
    "    # default log_fn prints per-epoch lines\n",
    ")\n",
    "\n",
    "t0 = time.time()\n",
    "history = fitter.fit()\n",
    "elapsed = time.time() - t0\n",
    "print(f\"\\nfit completed: {len(history)} epochs in {elapsed/60:.1f} min\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a710969d",
   "metadata": {},
   "source": [
    "## 4. Test-set summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3f60d3fe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-18T14:02:07.831201Z",
     "iopub.status.busy": "2026-05-18T14:02:07.831010Z",
     "iopub.status.idle": "2026-05-18T14:02:08.187951Z",
     "shell.execute_reply": "2026-05-18T14:02:08.186127Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test cc:      +0.497\n",
      "test cc_norm: +0.653\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test = fitter.evaluate(test_loader)\n",
    "test_cc_norm = test[\"cc_norm\"].cpu()\n",
    "test_cc      = test[\"cc\"].cpu()\n",
    "print(f\"test cc:      {torch.nanmean(test_cc):+.3f}\")\n",
    "print(f\"test cc_norm: {torch.nanmean(test_cc_norm):+.3f}\")\n",
    "\n",
    "# Training curve\n",
    "fig, ax = plt.subplots(figsize=(8, 3.5))\n",
    "ep = [h[\"epoch\"] for h in history]\n",
    "vC = [torch.nanmean(h[\"val_cc_norm\"]).item() for h in history]\n",
    "ax.plot(ep, vC, lw=1.6, color='tab:purple')\n",
    "ax.set_xlabel(\"epoch\"); ax.set_ylabel(\"val cc_norm (mean over cells)\")\n",
    "ax.set_title(\"ConvNet2D on AA2 OV / conspecific — training curve\")\n",
    "plt.tight_layout(); plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51d0920a",
   "metadata": {},
   "source": [
    "## 5. STRF gradmaps for the best-predicted cells\n",
    "\n",
    "`model.STRF_gradmap()` returns a `(N, 1, F, T)` tensor — for each\n",
    "output neuron, the gradient $\\partial \\mathcal{L}_n / \\partial \\mathbf{x}_0$\n",
    "of the negative last-timestep activity with respect to a null\n",
    "(all-zero) spectrogram. Under this sign convention positive entries\n",
    "are *inhibitory* features — adding mass there would decrease the\n",
    "neuron's response (see\n",
    "[`README_gradmap_strf.md`](../md/README_gradmap_strf.md) §4 for the\n",
    "full derivation). To get the more intuitive *excitatory-as-positive*\n",
    "plot — red = features the cell prefers — we plot $-\\mathbf{g}_n$,\n",
    "i.e. negate the returned gradmaps before passing them to\n",
    "`plot_strf_grid`.\n",
    "\n",
    "This is the closest thing a nonlinear model can give you to a\n",
    "classical reverse-correlation STRF, and it's the right diagnostic\n",
    "for asking \"did the network learn something biologically sensible?\"\n",
    "— particularly when the model has no explicit STRF layer.\n",
    "\n",
    "We pick the 12 best-predicted OV cells (highest test `cc_norm`).\n",
    "Time runs left to right in units of milliseconds of history.\n",
    "Frequency runs bottom to top (low → high band). Diverging colormap\n",
    "`RdBu_r` with per-cell symmetric scale so the spatial structure is\n",
    "easy to read across cells of varying gradient magnitude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "133b3982",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-18T14:02:08.191691Z",
     "iopub.status.busy": "2026-05-18T14:02:08.191308Z",
     "iopub.status.idle": "2026-05-18T14:02:09.324547Z",
     "shell.execute_reply": "2026-05-18T14:02:09.322653Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gradmaps shape: (59, 32, 24)\n",
      "top 12 cells, cc_norm range: 0.967 ... 0.792\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x780 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.eval()\n",
    "gradmaps = model.STRF_gradmap()                        # (N, 1, F, T)\n",
    "gradmaps = gradmaps.squeeze(1).detach().cpu().numpy()  # (N, F, T)\n",
    "print(f\"gradmaps shape: {gradmaps.shape}\")\n",
    "\n",
    "K = 12\n",
    "test_arr = test_cc_norm.numpy()\n",
    "order = np.argsort(np.where(np.isnan(test_arr), -np.inf, test_arr))[::-1]\n",
    "top_idx = order[:K]\n",
    "print(f\"top {K} cells, cc_norm range: \"\n",
    "      f\"{test_arr[top_idx[0]]:.3f} ... {test_arr[top_idx[-1]]:.3f}\")\n",
    "\n",
    "# Negate so that red (positive) = excitatory under RdBu_r — see the\n",
    "# markdown above and README_gradmap_strf.md §4 for the sign convention.\n",
    "titles = [f\"{ds.nrn_meta[n]['cell_id'][:14]} ({ds.nrn_meta[n]['area']})\\n\"\n",
    "          f\"cc_norm={test_arr[n]:.3f}\"\n",
    "          for n in top_idx]\n",
    "plot_strf_grid(\n",
    "    -gradmaps[top_idx], titles=titles, dt_ms=ds.dt, ncols=4,\n",
    "    figsize=(4 * 3.2, 3 * 2.6),\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc6d26f5",
   "metadata": {},
   "source": [
    "## What's next\n",
    "\n",
    "- **Scale up to the full AA2 population.** Drop `areas=` and\n",
    "  `stimuli=` from the dataset constructor to fit the full 494\n",
    "  cells × 117 stims. Each epoch becomes ~80 s on a single GPU\n",
    "  rather than a few seconds, but the gradmap pipeline is\n",
    "  identical. Useful for comparing receptive-field structure\n",
    "  across MLd / Field L / OV / CM at the population level.\n",
    "- **Sustained-loss gradmaps.** The current `STRF_gradmap` uses the\n",
    "  last-timestep activity as the loss target — a Spike-Triggered-\n",
    "  Average view. A complementary diagnostic is the gradient of the\n",
    "  *time-averaged* activity, which highlights features that drive\n",
    "  sustained responses rather than transient ones.\n",
    "- **Cross-model comparison.** Run the same gradmap extraction on a\n",
    "  Linear / LinearNonlinear model fit to the same split, and\n",
    "  overlay the explicit STRF kernel against the gradient map. They\n",
    "  should agree closely on the L/LN models, and the difference\n",
    "  between ConvNet2D's gradmap and an LN's STRF is exactly the\n",
    "  nonlinear contribution.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.15"
  }
 },
 "nbformat": 4,
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}