{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "execution": {}, "id": "view-in-github" }, "source": [ "\"Open   \"Open" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "# Data loader" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "## Summary\n", "\n", "Here we will load data from Cichy et al. 2014 [1]. The data consist of fMRI responses from early visual cortex (EVC) and inferior temporal (IT) cortex and MEG responses at different timepoints in the form of representational dissimilarity matrices (RDMs) to 92 images. These images belong to different categories as shown in the Figure below." ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "

" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "## Representational Similarity Analysis (RSA)\n", "\n", "RSA is a method to relate signals from different source spaces (such as behavior, neural responses, DNN activations) by abstracting signals from\n", "separate source spaces into a common similarity space. For this, in each source space, condition-specific responses are compared to each other for dissimilarity (e.g., by calculating Euclidean distances between signals), and the values are aggregated in so-called representational dissimilarity matrices (RDMs) indexed in rows and columns by the conditions compared. RDMs thus summarize the representational geometry of the source space signals. Different from source space signals themselves, RDMs from different sources spaces are directly comparable to each other for similarity and thus can relate signals from different spaces.\n", "\n", "The figure below illustrates how RSA can be applied to different problems by comparing RDMs of different modalities/species." ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "

" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "## Data from Cichy et al., 2014\n", "\n", "In the cells below, we will download and visualize MEG and fMRI RDMs. Please refer Figure 1 in [1] to learn details about the image category order in RDMs." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [], "source": [ "# Imports\n", "import os\n", "import cv2\n", "import glob\n", "import h5py\n", "import time\n", "import random\n", "import urllib\n", "import pickle\n", "import argparse\n", "\n", "import numpy as np\n", "import nibabel as nib\n", "import scipy.io as sio\n", "import matplotlib.pyplot as plt\n", "from tqdm import tqdm\n", "from PIL import Image\n", "\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.decomposition import PCA, IncrementalPCA\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.utils.model_zoo as model_zoo\n", "from torch.autograd import Variable as V\n", "from torchvision import transforms as trn" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "execution": {} }, "outputs": [], "source": [ "# @title Load mat files\n", "def loadmat(matfile):\n", " \"\"\"Function to load .mat files.\n", " Parameters\n", " ----------\n", " matfile : str\n", " path to `matfile` containing fMRI data for a given trial.\n", " Returns\n", " -------\n", " dict\n", " dictionary containing data in key 'vol' for a given trial.\n", " \"\"\"\n", " try:\n", " f = h5py.File(matfile)\n", " except (IOError, OSError):\n", " return sio.loadmat(matfile)\n", " else:\n", " return {name: np.transpose(f.get(name)) for name in f.keys()}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "execution": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downlading data...\n", "Download completed!\n" ] } ], "source": [ "# @title Data download and unzip in `data/`\n", "\n", "import os, zipfile, requests\n", "\n", "fname = 'data.zip'\n", "url = \"https://osf.io/7vpyh/download\"\n", "\n", "if not os.path.isfile(fname):\n", " try:\n", " r = requests.get(url)\n", " except requests.ConnectionError:\n", " print(\"!!! Failed to download data !!!\")\n", " else:\n", " if r.status_code != requests.codes.ok:\n", " print(\"!!! Failed to download data !!!\")\n", " else:\n", " print(\"Downlading data...\")\n", " with open(fname, \"wb\") as fid:\n", " fid.write(r.content)\n", " with zipfile.ZipFile(fname, 'r') as zip_ref:\n", " zip_ref.extractall('data/')\n", " print(\"Download completed!\")" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "## Loading MEG RDMs" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(16, 2, 1301, 92, 92)\n" ] } ], "source": [ "# Load MEG RDMs for each time point for all subjects all sessions\n", "MEG_RDMs = loadmat(\"data/MEG_decoding_RDMs.mat\")['MEG_decoding_RDMs']\n", "print(MEG_RDMs.shape)" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "Shape of RDM is `num_subjects x num_sessions x num_timepoints x num_stimulus x num_stimulus`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [], "source": [ "# average RDM across subjects and sessions\n", "MEG_RDM_sub_averaged = np.mean(MEG_RDMs, axis=(0, 1))\n", "del MEG_RDMs" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "execution": {} }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# @title visualize subject averaged MEG RDMs\n", "timepoint = 420 # @param {type:\"slider\", min:-100, max:600, step:20}\n", "\n", "# Load RDM at a given timepoint\n", "# +100 as the RDMs provided are from -100ms to 1000ms after the stimulus onset\n", "RDM = np.array(MEG_RDM_sub_averaged[timepoint+100])\n", "\n", "# Since the matrix is symmetric we set upper triangular values to NaN\n", "RDM[np.triu_indices(RDM.shape[0], 1)] = np.nan\n", "\n", "# plot the RDM at given timepoint\n", "plt.imshow(RDM, cmap=\"bwr\")\n", "plt.title(\"MEG RDM at t = \" + str(timepoint))\n", "cbar = plt.colorbar()\n", "plt.xlabel(\"Stimuli\")\n", "plt.ylabel(\"Stimuli\")\n", "cbar.ax.get_yaxis().labelpad = 15\n", "cbar.ax.set_ylabel('Decoding Accuracy', rotation=270)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "##Loading fMRI RDMs" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dict_keys(['EVC_RDMs', 'IT_RDMs'])\n", "(15, 92, 92)\n" ] } ], "source": [ "fMRI_file = 'data/92_Image_Set/target_fmri.mat' # path of fMRI RDM file\n", "fMRI_RDMs = loadmat(fMRI_file) # load the fMRI RDMs\n", "print(fMRI_RDMs.keys())\n", "print(fMRI_RDMs['EVC_RDMs'].shape)" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "fMRI_RDMs is a dictionary with keys `'EVC_RDMs'` and `'IT_RDMs'` corresponding to ROIs EVC and IT respectively. The shape of each RDM is `num_subjects` $\\times$ `num_stimulus` $\\times$ `num_stimulus`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "execution": {} }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# @title visualize subject averaged fMRI RDMs\n", "ROI = 'EVC' # @param [\"EVC\", \"IT\"]\n", "\n", "# Average the ROI RDM across subjects\n", "RDM = np.array(fMRI_RDMs[ROI + '_RDMs'].mean(axis=0))\n", "\n", "# Since the matrix is symmetric we set upper triangular values to NaN\n", "RDM[np.triu_indices(RDM.shape[0], 1)] = np.nan\n", "\n", "# plot the ROI RDM at given timepoint\n", "plt.imshow(RDM, cmap=\"bwr\")\n", "plt.title(ROI + \" RDM\")\n", "cbar = plt.colorbar()\n", "plt.xlabel(\"Stimuli\")\n", "plt.ylabel(\"Stimuli\")\n", "cbar.ax.get_yaxis().labelpad = 15\n", "cbar.ax.set_ylabel('1-Correlation', rotation=270)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "# Example Analyses\n", "\n", "Below we will perform two analyses:\n", "\n", "1. MEG-fMRI comparison: To find out at which timepoint MEG representation is similar to a given ROI's representation. \n", "2. MEG-Deep Neural Network (DNN) comparison: To find out at which timepoint MEG representation is similar to a given DNN layer's representation. \n", "\n", "In other words, the comparison will inform us about the sequential order of visual feature processing in the cortex." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "execution": {} }, "outputs": [], "source": [ "# @title RDM Comparison functions\n", "from scipy.stats import spearmanr\n", "\n", "def RSA_spearman(rdm1,rdm2):\n", " \"\"\"\n", " computes and returns the spearman correlation between lower triangular\n", " part of the input rdms. We only need to compare either lower or upper\n", " triangular part of the matrix as RDM is symmetric\n", " \"\"\"\n", " # get lower triangular part of the RDM1\n", " lt_rdm1 = get_lowertriangular(rdm1)\n", " # get lower triangular part of the RDM1\n", " lt_rdm2 = get_lowertriangular(rdm2)\n", " # return Spearman's correlation between lower triangular part of rdm1 & rdm2\n", " return spearmanr(lt_rdm1, lt_rdm2)[0]\n", "\n", "def get_lowertriangular(rdm):\n", " \"\"\"\n", " returns lower triangular part of the matrix\n", " \"\"\"\n", " num_conditions = rdm.shape[0]\n", " return rdm[np.tril_indices(num_conditions, -1)]" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "##MEG-fMRI Comparison" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "execution": {} }, "outputs": [], "source": [ "# @title Correlating MEG RDMs with fMRI RDMs\n", "num_timepoints = MEG_RDM_sub_averaged.shape[0] #get number of timepoints\n", "\n", "# initialize a dictionary to store MEG and ROI RDM correlation at each timepoint\n", "MEG_correlation = {}\n", "ROIs = ['EVC','IT']\n", "for ROI in ROIs:\n", " MEG_correlation[ROI] = []\n", "\n", "# for loop that goes over MEG RDMs at all time points and correlate with ROI RDMs\n", "for t in range(num_timepoints):\n", " MEG_RDM_t = MEG_RDM_sub_averaged[t, :, :]\n", " for ROI in ROIs:\n", " ROI_RDM = np.mean(fMRI_RDMs[ROI + '_RDMs'], axis=0)\n", " MEG_correlation[ROI].append(RSA_spearman(ROI_RDM, MEG_RDM_t))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "execution": {} }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# @title Plotting MEG-fMRI comparison\n", "\n", "plt.rc('font', size=12)\n", "fig, ax = plt.subplots(figsize=(10, 6))\n", "\n", "time_range = range(-100, 1201)\n", "ax.plot(time_range, MEG_correlation['IT'], color='tab:orange', label='IT')\n", "ax.plot(time_range, MEG_correlation['EVC'], color='tab:blue', label='EVC')\n", "\n", "# Same as above\n", "ax.set_xlabel('Time')\n", "ax.set_ylabel('Spearmans Correlation')\n", "ax.set_title('MEG-fMRI fusion')\n", "ax.grid(True)\n", "ax.legend(loc='upper left')\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "## MEG-DNN Comparison" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "### Creating DNN (AlexNet) RDMs" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "execution": {} }, "outputs": [], "source": [ "# @title AlexNet Definition\n", "__all__ = ['AlexNet', 'alexnet']\n", "\n", "model_urls = {\n", " 'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',\n", "}\n", "\n", "# Here we redefine AlexNet differently from torchvision code for better understanding\n", "class AlexNet(nn.Module):\n", " def __init__(self, num_classes=1000):\n", " super(AlexNet, self).__init__()\n", " self.conv1 = nn.Sequential(\n", " nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),\n", " nn.ReLU(inplace=True),\n", " nn.MaxPool2d(kernel_size=3, stride=2),\n", " )\n", " self.conv2 = nn.Sequential(\n", " nn.Conv2d(64, 192, kernel_size=5, padding=2),\n", " nn.ReLU(inplace=True),\n", " nn.MaxPool2d(kernel_size=3, stride=2),\n", " )\n", " self.conv3 = nn.Sequential(\n", " nn.Conv2d(192, 384, kernel_size=3, padding=1),\n", " nn.ReLU(inplace=True),\n", " )\n", " self.conv4 = nn.Sequential(\n", " nn.Conv2d(384, 256, kernel_size=3, padding=1),\n", " nn.ReLU(inplace=True),\n", " )\n", " self.conv5 = nn.Sequential(\n", " nn.Conv2d(256, 256, kernel_size=3, padding=1),\n", " nn.ReLU(inplace=True),\n", " nn.MaxPool2d(kernel_size=3, stride=2),\n", " )\n", " self.fc6 = nn.Sequential(\n", " nn.Dropout(),\n", " nn.Linear(256 * 6 * 6, 4096),\n", " nn.ReLU(inplace=True),\n", " )\n", " self.fc7 =nn.Sequential(\n", " nn.Dropout(),\n", " nn.Linear(4096, 4096),\n", " )\n", " self.fc8 = nn.Sequential(\n", " nn.ReLU(inplace=True),\n", " nn.Linear(4096, num_classes),\n", " )\n", "\n", " def forward(self, x):\n", " out1 = self.conv1(x)\n", " out2 = self.conv2(out1)\n", " out3 = self.conv3(out2)\n", " out4 = self.conv4(out3)\n", " out5 = self.conv5(out4)\n", "\n", " out5_reshaped = out5.view(out5.size(0), 256 * 6 * 6)\n", " out6= self.fc6(out5_reshaped)\n", " out7= self.fc7(out6)\n", " out8 = self.fc8(out7)\n", " return out1, out2, out3,out4, out5, out6,out7,out8\n", "\n", "\n", "def alexnet(pretrained=False, **kwargs):\n", " \"\"\"AlexNet model architecture from the\n", " `\"One weird trick...\" `_ paper.\n", " Args:\n", " pretrained (bool): If True, returns a model pre-trained on ImageNet\n", " \"\"\"\n", " model = AlexNet(**kwargs)\n", " if pretrained:\n", " model.load_state_dict(model_zoo.load_url(model_urls['alexnet']))\n", " return model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "execution": {} }, "outputs": [], "source": [ "# @title Feature extraction code\n", "\n", "def load_alexnet(model_checkpoints):\n", " \"\"\"This function initializes an Alexnet and load\n", " its weights from a pretrained model. Since we redefined model in a different\n", " way we have to rename the weights that were in the pretrained checkpoint.\n", " ----------\n", " model_checkpoints : str\n", " model checkpoints location.\n", " Returns\n", " -------\n", " model\n", " pytorch model of alexnet\n", " \"\"\"\n", "\n", " model = alexnet()\n", " # Load checkpoint\n", " model_file = model_checkpoints\n", " checkpoint = torch.load(model_file, map_location=lambda storage, loc: storage)\n", "\n", " # Rename the checkpoint keys according to new definition\n", " model_dict = [\"conv1.0.weight\", \"conv1.0.bias\",\n", " \"conv2.0.weight\", \"conv2.0.bias\",\n", " \"conv3.0.weight\", \"conv3.0.bias\",\n", " \"conv4.0.weight\", \"conv4.0.bias\",\n", " \"conv5.0.weight\", \"conv5.0.bias\",\n", " \"fc6.1.weight\", \"fc6.1.bias\",\n", " \"fc7.1.weight\", \"fc7.1.bias\",\n", " \"fc8.1.weight\", \"fc8.1.bias\"]\n", " state_dict = {}\n", " i = 0\n", " for k, v in checkpoint.items():\n", " state_dict[model_dict[i]] = v\n", " i += 1\n", "\n", " # initialize model with pretrained weights\n", " model.load_state_dict(state_dict)\n", " if torch.cuda.is_available():\n", " model.cuda()\n", " model.eval()\n", " return model\n", "\n", "\n", "def get_activations_and_save(model, image_list, activations_dir):\n", " \"\"\"This function generates Alexnet features and save them in a specified directory.\n", " Parameters\n", " ----------\n", " model :\n", " pytorch model : alexnet.\n", " image_list : list\n", " the list contains path to all images.\n", " activations_dir : str\n", " save path for extracted features.\n", " \"\"\"\n", "\n", " resize_normalize = trn.Compose([\n", " trn.Resize((224, 224)),\n", " trn.ToTensor(),\n", " trn.Normalize([0.485, 0.456, 0.406],\n", " [0.229, 0.224, 0.225])\n", " ])\n", " # for all images in the list generate and save activations\n", " for image_file in tqdm(image_list):\n", " # open image\n", " img = Image.open(image_file)\n", " image_file_name = os.path.split(image_file)[-1].split(\".\")[0]\n", " # apply transformations before feeding to model\n", " input_img = V(resize_normalize(img).unsqueeze(0))\n", " if torch.cuda.is_available():\n", " input_img=input_img.cuda()\n", " x = model.forward(input_img)\n", "\n", " activations = []\n", " for i,feat in enumerate(x):\n", " activations.append(feat.data.cpu().numpy().ravel())\n", " for layer in range(len(activations)):\n", " save_path = os.path.join(activations_dir,\n", " f\"{image_file_name}_{layer}_{layer + 1}.npy\")\n", " np.save(save_path, activations[layer])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total Number of Images: 92\n", "-------------Saving activations ----------------------------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 92/92 [00:05<00:00, 16.96it/s]\n" ] } ], "source": [ "# get the paths to all the images in the stimulus set\n", "image_dir = 'data/92_Image_Set/92images'\n", "image_list = glob.glob(image_dir + '/*.jpg')\n", "image_list.sort()\n", "print(f'Total Number of Images: {len(image_list)}')\n", "save_dir = \"activations_alexnet\"\n", "\n", "######### load Alexnet initialized with pretrained weights ###################\n", "# Download pretrained Alexnet from:\n", "# https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth\n", "# and save in the current directory\n", "checkpoint_path = \"alexnet.pth\"\n", "if not os.path.exists(checkpoint_path):\n", " url = \"https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth\"\n", " urllib.request.urlretrieve(url, \"alexnet.pth\")\n", "model = load_alexnet(checkpoint_path)\n", "##############################################################################\n", "\n", "######### get and save activations ################################\n", "\n", "activations_dir = os.path.join(save_dir)\n", "if not os.path.exists(activations_dir):\n", " os.makedirs(activations_dir)\n", "print(\"-------------Saving activations ----------------------------\")\n", "get_activations_and_save(model, image_list, activations_dir)\n", "###################################################################" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/numpy/lib/function_base.py:380: RuntimeWarning: Mean of empty slice.\n", " avg = a.mean(axis)\n", "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:182: RuntimeWarning: invalid value encountered in true_divide\n", " ret, rcount, out=ret, casting='unsafe', subok=False)\n", "/usr/local/lib/python3.7/dist-packages/numpy/lib/function_base.py:2683: RuntimeWarning: Degrees of freedom <= 0 for slice\n", " c = cov(x, y, rowvar, dtype=dtype)\n", "/usr/local/lib/python3.7/dist-packages/numpy/lib/function_base.py:2542: RuntimeWarning: divide by zero encountered in true_divide\n", " c *= np.true_divide(1, fact)\n", "/usr/local/lib/python3.7/dist-packages/numpy/lib/function_base.py:2542: RuntimeWarning: invalid value encountered in multiply\n", " c *= np.true_divide(1, fact)\n" ] } ], "source": [ "num_layers = 8 # number of layers in the model\n", "layers = []\n", "\n", "for i in range(num_layers):\n", " layers.append(f\"layer_{i + 1}\")\n", "\n", "model_RDMs = {}\n", "# create RDM for each layer from activations\n", "for j, layer in enumerate(layers):\n", " activation_files = glob.glob(activations_dir + '/*' + f'{j}.npy')\n", " activation_files.sort()\n", " activations = []\n", " # Load all activations\n", " for activation_file in activation_files:\n", " activations.append(np.load(activation_file))\n", " activations = np.array(activations)\n", " # calculate Pearson's distance for all pairwise comparisons\n", " model_RDMs[layer] = 1 - np.corrcoef(activations)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "execution": {} }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# @title visualize model RDMs\n", "layer = 'layer_8' # @param ['layer_1','layer_2','layer_3','layer_4','layer_5','layer_6','layer_7','layer_8']\n", "\n", "# loading layer RDM\n", "RDM = np.array(model_RDMs[layer])\n", "\n", "# Since the matrix is symmetric we set upper triangular values to NaN\n", "RDM[np.triu_indices(RDM.shape[0], 1)] = np.nan\n", "\n", "# Visualize layer RDM\n", "plt.imshow(RDM, cmap=\"bwr\")\n", "plt.title(layer + \" RDM\")\n", "cbar = plt.colorbar()\n", "plt.xlabel(\"Stimuli\")\n", "plt.ylabel(\"Stimuli\")\n", "cbar.ax.get_yaxis().labelpad = 15\n", "cbar.ax.set_ylabel('1-Correlation', rotation=270)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "### Comparing MEG RDMs with AlexNet RDMs" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "execution": {} }, "outputs": [], "source": [ "# @title Correlating MEG RDMs with DNN RDMs\n", "run = False\n", "\n", "if run:\n", " num_timepoints = MEG_RDM_sub_averaged.shape[0] # get number of timepoints\n", "\n", " # initialize a dictionary to store MEG and DNN RDM correlation at each timepoint\n", " for layer in layers:\n", " MEG_correlation[layer] = []\n", "\n", " # for loop that goes over MEG RDMs at all time points and correlate with DNN RDMs\n", " for t in range(num_timepoints):\n", " MEG_RDM_t = MEG_RDM_sub_averaged[t, :, :]\n", " for layer in layers:\n", " model_RDM = model_RDMs[layer]\n", " MEG_correlation[layer].append(RSA_spearman(model_RDM, MEG_RDM_t))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "execution": {} }, "outputs": [], "source": [ "# @title Plotting MEG-DNN comparison\n", "if run:\n", "\n", " plt.rc('font', size=12)\n", " fig, ax = plt.subplots(figsize=(10, 6))\n", "\n", " time_range = range(-100,1201)\n", " ax.plot(time_range, MEG_correlation['layer_1'],\n", " color='tab:orange', label='layer_1')\n", " ax.plot(time_range, MEG_correlation['layer_7'],\n", " color='tab:blue', label='layer_7')\n", "\n", " # Same as above\n", " ax.set_xlabel('Time')\n", " ax.set_ylabel('Spearmans Correlation')\n", " ax.set_title('MEG-model comparison')\n", " ax.grid(True)\n", " ax.legend(loc='upper left')\n", " fig.show()" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "# References\n", "\n", "1. Cichy et al., (2014) Resolving human object recognition in space and time. _Nature Neuroscience_ **17**: 455-462. doi: [10.1038/nn.3635](https://doi.org/10.1038/nn.3635)\n", "2. Kriegeskorte et al., (2008). Representational similarity analysis – connecting the branches of systems neuroscience. _Frontiers in Systems Neuroscience_ **2**: 4. doi: [10.3389/neuro.06.004.2008](https://doi.org/10.3389/neuro.06.004.2008)" ] } ], "metadata": { "colab": { "collapsed_sections": [], "include_colab_link": true, "name": "load_cichy_fMRI_MEG", "provenance": [], "toc_visible": true }, "kernel": { "display_name": "Python 3", "language": "python", "name": "python3" }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 }