{ "cells": [ { "cell_type": "markdown", "id": "9482b8e9", "metadata": {}, "source": [ "# Wingert 2026 — ferret auditory cortex encoding subspace\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/explore_wingert2026.ipynb)\n", "\n", "This notebook is a **visual smoke test** of the deepSTRF loader for the\n", "**[Wingert et al. 2026](https://doi.org/10.1038/s41593-026-02216-0)**\n", "(*Nat Neurosci*) ferret AC dataset — single-unit Kilosort-sorted spikes\n", "from 67 high-density silicon-probe + Neuropixels recordings spanning\n", "A1, PEG (and ancillary AC / HC), in 4 ferrets passively listening to\n", "natural-sound segments.\n", "\n", "We cover:\n", "\n", "- the two **stim-duration cohorts** (20 s vs 22 s sessions) and the\n", " est / val subset semantics;\n", "- per-cell metadata: area, layer, depth, narrow / regular spike-width\n", " classes, the published `goodpred` flag;\n", "- the **block-diagonal** layout that pools two recording sessions —\n", " cross-session `(stim, cell)` pairs are NaN sentinels;\n", "- how the **two-probe SLJ032a** recording is exposed as two siteids\n", " that share one .tgz / one stim set.\n" ] }, { "cell_type": "markdown", "id": "9201831f", "metadata": {}, "source": [ "## Setup — Google Colab\n", "\n", "The cell below installs deepSTRF from source on Colab. On a local\n", "install (`pip install -e .`) it's a no-op.\n", "\n", "**Note on data.** The Zenodo archive's `recordings.zip` is **~4.35 GB**;\n", "`download=True` fetches it on demand and skips the much larger\n", "`wav.zip` and `models.zip` that the loader doesn't use. On a\n", "workstation with the data already unpacked, point `WINGERT2026_DATA`\n", "at the directory holding `recordings/` and `cell_list.csv`.\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "515e7c47", "metadata": { "execution": { "iopub.execute_input": "2026-05-25T19:27:21.594988Z", "iopub.status.busy": "2026-05-25T19:27:21.594282Z", "iopub.status.idle": "2026-05-25T19:27:21.618258Z", "shell.execute_reply": "2026-05-25T19:27:21.614691Z" } }, "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": "686f246c", "metadata": {}, "source": [ "## Imports + paths" ] }, { "cell_type": "code", "execution_count": 2, "id": "eafcc5d9", "metadata": { "execution": { "iopub.execute_input": "2026-05-25T19:27:21.626185Z", "iopub.status.busy": "2026-05-25T19:27:21.625480Z", "iopub.status.idle": "2026-05-25T19:27:24.769129Z", "shell.execute_reply": "2026-05-25T19:27:24.765809Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ulysse/miniconda3/envs/deepstrf_dev/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] } ], "source": [ "%matplotlib inline\n", "import os\n", "import warnings\n", "from collections import Counter\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import torch\n", "\n", "from deepSTRF.datasets.audio import Wingert2026Dataset\n", "from deepSTRF.utils import plot_stim_with_response\n", "\n", "# Where the Zenodo dump lives. None → platformdirs cache + download=True\n", "# (4.35 GB recordings.zip + 5 MB cell_list.csv).\n", "DATA = os.environ.get(\"WINGERT2026_DATA\", None)\n", "DOWNLOAD = False # flip to True on first run if the data isn't local\n", "\n", "# Silence the (expected) PRN018b duplicate-drop UserWarning emitted on\n", "# the first .tgz scan — see README §\"Note on the PRN018a / PRN018b\n", "# duplicate\".\n", "warnings.filterwarnings(\"ignore\", category=UserWarning,\n", " module=r\"deepSTRF\\.datasets\\.audio\\.wingert2026\")\n" ] }, { "cell_type": "markdown", "id": "8dc2652f", "metadata": {}, "source": [ "## 1. Per-cell metadata — A1 + PEG headline cohort (enumerate-only)\n", "\n", "The `_enumerate_only=True` path reads only `cell_list.csv` (~5 MB)\n", "and populates `nrn_meta` / `N_neurons` without touching the per-site\n", ".tgz archives. Useful for fast metadata exploration and sanity checks\n", "before the heavy load. The `area=` and `site=` filters apply at this\n", "stage.\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "a5bf5431", "metadata": { "execution": { "iopub.execute_input": "2026-05-25T19:27:24.777476Z", "iopub.status.busy": "2026-05-25T19:27:24.776494Z", "iopub.status.idle": "2026-05-25T19:27:25.174269Z", "shell.execute_reply": "2026-05-25T19:27:25.173423Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A1 + PEG cohort: N = 2874 (True)\n", " by area: {'PEG': 746, 'A1': 2128}\n", " by layer: {'56': 1084, '44': 744, '13': 1046}\n", " by narrow: {False: 2397, True: 477}\n", " goodpred=True fraction: 81.3%\n" ] } ], "source": [ "ds_meta = Wingert2026Dataset(\n", " path=DATA, download=DOWNLOAD, area=[\"A1\", \"PEG\"], _enumerate_only=True,\n", ")\n", "print(f\"A1 + PEG cohort: N = {ds_meta.N_neurons} ({ds_meta.N_neurons == 2128 + 746})\")\n", "\n", "# breakdown by area\n", "by_area = Counter(m[\"area\"] for m in ds_meta.nrn_meta)\n", "print(f\" by area: {dict(by_area)}\")\n", "\n", "# layer breakdown (column is a string like '1-3', '4', '56')\n", "by_layer = Counter(m[\"layer\"] for m in ds_meta.nrn_meta)\n", "print(f\" by layer: {dict(by_layer)}\")\n", "\n", "# narrow vs broad spike width\n", "by_narrow = Counter(m[\"narrow\"] for m in ds_meta.nrn_meta)\n", "print(f\" by narrow: {dict(by_narrow)}\")\n", "\n", "# published auditory-responsive flag\n", "goodpred_frac = sum(1 for m in ds_meta.nrn_meta if m[\"goodpred\"]) / len(ds_meta.nrn_meta)\n", "print(f\" goodpred=True fraction: {goodpred_frac:.1%}\")\n" ] }, { "cell_type": "markdown", "id": "edcaf81f", "metadata": {}, "source": [ "### Cell-depth + spike-width distribution\n", "\n", "The David lab classifies cells as Regular vs Narrow spiking based on\n", "spike-waveform width (cutoff 0.35 ms for 64-channel silicon probes,\n", "0.375 ms for Neuropixels). Narrow-spiking cells are putative inhibitory\n", "interneurons.\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "104f7246", "metadata": { "execution": { "iopub.execute_input": "2026-05-25T19:27:25.176080Z", "iopub.status.busy": "2026-05-25T19:27:25.175930Z", "iopub.status.idle": "2026-05-25T19:27:25.616321Z", "shell.execute_reply": "2026-05-25T19:27:25.615349Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Depth + spike-width scatter, coloured by area\n", "fig, ax = plt.subplots(figsize=(7, 5))\n", "for area, color in [(\"A1\", \"tab:blue\"), (\"PEG\", \"tab:orange\")]:\n", " sub = [m for m in ds_meta.nrn_meta\n", " if m[\"area\"] == area and m[\"depth\"] is not None and m[\"sw\"] is not None]\n", " ax.scatter(\n", " [m[\"sw\"] for m in sub], [m[\"depth\"] for m in sub],\n", " s=4, alpha=0.4, color=color, label=f\"{area} (n={len(sub)})\",\n", " )\n", "ax.axvline(0.35, color=\"grey\", lw=1, linestyle=\"--\", label=\"0.35 ms cutoff\")\n", "ax.set_xlabel(\"Spike width (ms)\")\n", "ax.set_ylabel(\"Depth below L3/4 boundary (μm)\")\n", "ax.invert_yaxis() # superficial layers up\n", "ax.set_title(\"Wingert 2026 A1 + PEG cell library\")\n", "ax.legend(loc=\"best\", fontsize=9)\n", "plt.tight_layout(); plt.show()\n" ] }, { "cell_type": "markdown", "id": "5086cf2e", "metadata": {}, "source": [ "## 2. Load one small site (CLT027c, 20 cells in A1)\n", "\n", "A single-site `Wingert2026Dataset` instance is cheap (~7 s on a\n", "laptop, <500 MB peak RSS). Because every cell saw every stim, the\n", "response grid has **no NaN sentinels** at this scale.\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "264a30f1", "metadata": { "execution": { "iopub.execute_input": "2026-05-25T19:27:25.618877Z", "iopub.status.busy": "2026-05-25T19:27:25.618642Z", "iopub.status.idle": "2026-05-25T19:27:33.271308Z", "shell.execute_reply": "2026-05-25T19:27:33.270347Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\r", "Wingert2026 sites: 0%| | 0/1 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# pick the val stim with the highest R\n", "val_indices = [i for i, m in enumerate(ds.stim_meta) if m[\"subset\"] == \"val\"]\n", "s_idx = max(val_indices, key=lambda i: ds.responses[i][0].shape[0])\n", "spec = ds.stims[s_idx][0] # (F, T)\n", "print(f\"showing stim {ds.stim_meta[s_idx]['name']!r} with R={ds.responses[s_idx][0].shape[0]}\")\n", "\n", "# pick the most-active cell across this stim\n", "mean_rates = [ds.responses[s_idx][n].mean().item() for n in range(ds.N_neurons)]\n", "n_idx = int(np.argmax(mean_rates))\n", "cell_meta = ds.nrn_meta[n_idx]\n", "print(f\"showing cell {cell_meta['cell_id']!r} (area={cell_meta['area']}, \"\n", " f\"layer={cell_meta['layer']}, narrow={cell_meta['narrow']})\")\n", "\n", "reps = ds.responses[s_idx][n_idx] # (R, T)\n", "# plot_stim_with_response returns (Figure, axes). It auto-shows the\n", "# raster panel when R > 1 and adds the PSTH overlay.\n", "fig, _ = plot_stim_with_response(\n", " spec, reps, dt_ms=ds.dt,\n", " title=f\"{ds.stim_meta[s_idx]['name']} — {cell_meta['cell_id']}\",\n", ")\n", "plt.show()\n" ] }, { "cell_type": "markdown", "id": "135858fc", "metadata": {}, "source": [ "## 3. Block-diagonal layout — load two sites at once\n", "\n", "Loading more than one site is where the deepSTRF sparse-coverage\n", "paradigm earns its keep. Each cell only has real data for the\n", "~100 stimuli its session presented; for every other session's\n", "stimuli, the loader emits a `(1, 1)` NaN sentinel — and crucially\n", "**all sentinels share the same underlying tensor**, so the in-memory\n", "overhead is one pointer per slot, not a fresh tensor.\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "8b32e2cd", "metadata": { "execution": { "iopub.execute_input": "2026-05-25T19:27:33.606601Z", "iopub.status.busy": "2026-05-25T19:27:33.606402Z", "iopub.status.idle": "2026-05-25T19:27:42.704674Z", "shell.execute_reply": "2026-05-25T19:27:42.703944Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\r", "Wingert2026 sites: 0%| | 0/2 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m = ds2.nrn_masks.numpy() # (S, N) bool\n", "fig, ax = plt.subplots(figsize=(10, 4))\n", "ax.imshow(m.T, aspect=\"auto\", cmap=\"Greys\", interpolation=\"nearest\")\n", "\n", "# Session boundary lines\n", "sess_seen = []\n", "for s, smeta in enumerate(ds2.stim_meta):\n", " sess = smeta[\"session\"]\n", " if sess_seen and sess_seen[-1] != sess:\n", " ax.axvline(s - 0.5, color=\"red\", lw=1)\n", " sess_seen.append(sess)\n", "\n", "cell_seen = []\n", "for n, nmeta in enumerate(ds2.nrn_meta):\n", " sess = nmeta[\"session\"]\n", " if cell_seen and cell_seen[-1] != sess:\n", " ax.axhline(n - 0.5, color=\"blue\", lw=1)\n", " cell_seen.append(sess)\n", "\n", "ax.set_xlabel(\"Stim index\")\n", "ax.set_ylabel(\"Cell index\")\n", "ax.set_title(\"Block-diagonal nrn_masks — CLT027c ⊕ CLT028c\")\n", "plt.tight_layout(); plt.show()\n" ] }, { "cell_type": "markdown", "id": "11d1804e", "metadata": {}, "source": [ "## 4. SLJ032a — one .tgz, two probes, one stim set\n", "\n", "The SLJ032a recording used two simultaneously-inserted probes, A and B.\n", "`cell_list.csv` exposes them as separate siteids (`'SLJ032a'` for\n", "probe A's 76 cells, `'SLJ032a-B'` for probe B's 47 cells), but they\n", "share one `.tgz` / one stim set. Loading both probes gives 123 cells\n", "and **one** stim list (no duplication).\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "f6696e70", "metadata": { "execution": { "iopub.execute_input": "2026-05-25T19:27:43.096647Z", "iopub.status.busy": "2026-05-25T19:27:43.096434Z", "iopub.status.idle": "2026-05-25T19:27:51.588064Z", "shell.execute_reply": "2026-05-25T19:27:51.587214Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\r", "Wingert2026 sites: 0%| | 0/1 [00:00