{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Receptive Field Analysis\n",
"This notebook demonstrates how to run the `brain_observatory.receptive_field_analysis` module. This module uses a cell's responses to the locally sparse noise stimulus to characterize the spatial receptive field, including on and off subunits. We highly recommend reading through the the stimulus analysis whitepaper to understand the locally sparse noise stimulus and the analysis methodology.\n",
"\n",
"Download this file in .ipynb format here.\n",
"\n",
"First we import packages."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from allensdk.core.brain_observatory_cache import BrainObservatoryCache\n",
"import allensdk.brain_observatory.receptive_field_analysis.visualization as rfvis\n",
"import allensdk.brain_observatory.receptive_field_analysis.receptive_field as rf\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given a cell of interest, we now identify the experiment that contains the locally sparse noise stimulus and download its NWB file. We also look in the NWB file to figure out the position/index of the cell that has the ID we're interested in."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:allensdk.api.queries.brain_observatory_api:Downloading ophys_experiment 501474098 NWB. This can take some time.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"cell 587377366 has index 5\n"
]
}
],
"source": [
"cell_specimen_id = 587377366\n",
"\n",
"boc = BrainObservatoryCache(manifest_file='boc/manifest.json')\n",
"\n",
"exps = boc.get_ophys_experiments(cell_specimen_ids=[cell_specimen_id],\n",
" stimuli=['locally_sparse_noise'])\n",
"\n",
"data_set = boc.get_ophys_experiment_data(exps[0]['id'])\n",
"\n",
"cell_index = data_set.get_cell_specimen_indices([cell_specimen_id])[0]\n",
"\n",
"print(\"cell %d has index %d\" % (cell_specimen_id, cell_index))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compute receptive fields\n",
"The following method in the `receptive_field_analysis` module will characterize on and off receptive fields and perform a per-pixel significance test."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rf_data = rf.compute_receptive_field_with_postprocessing(data_set, \n",
" cell_index, \n",
" 'locally_sparse_noise', \n",
" alpha=0.5, \n",
" number_of_shuffles=10000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Chi^2 significance map\n",
"Per-pixel chi-square tests identify cells that show non-uniform distributions of responses across pixels. The `receptive_field_analysis.visualization` module has function to plot that significance as a heat map."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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WD13RtcxI43HLCYE+69MEzroRNAFgDVkXJ4IAYMIMEzQBoDoyTQAosNbPS5MQNAHUAxNB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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"rfvis.plot_chi_square_summary(rf_data)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Response-triggered stimulus field\n",
"The response-triggered stimulus field shows, for a given pixel, how many trials contained a detected calcium event."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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OAk/Vo4Z9CjbXFuhsvhYKG2nvRiPG+FhiRHJcCTkR9HfW/3St8wSEFCxYXE5ITLB5lZLU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\nZgacaNEgAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, (ax1, ax2) = plt.subplots(1,2)\n",
"rfvis.plot_rts_summary(rf_data, ax1, ax2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Blurred response-triggered stimulus field\n",
"The RTS field is convolved with a Gaussian to pool the contributions of neighboring stimulus pixels. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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4RNsqKfGwy3nDGVrOgSgbK40AcN2O9JpFjnUDfV0jETpMr6Jnix3LAx1mJSbCZkyWHq8I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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, (ax1, ax2) = plt.subplots(1,2)\n",
"rfvis.plot_rts_blur_summary(rf_data, ax1, ax2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## p value field\n",
"\n",
"Per-pixel p-values are estimated from the blurred RTS field to understand the significance of the response to each pixel. "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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hYk/fpLd4WFp2oHn88AWvqdjWikIV+8sSe9VLpbHCYG15pZk7PTlVxQplLzP3hCZ6Un7Q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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, (ax1, ax2) = plt.subplots(1,2)\n",
"rfvis.plot_p_values(rf_data, ax1, ax2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Significance mask\n",
"\n",
"The significance mask is p-value field after applying a binary threshold to remove insignificant pixels."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, (ax1, ax2) = plt.subplots(1,2)\n",
"rfvis.plot_mask(rf_data, ax1, ax2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Gaussian fit\n",
"Each identified subunit of the on and off receptive fields are fit with a Gaussian in the `receptive_field_analysis.postprocessing` module."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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q9XhS+j0vSzxeKioKinCOnWFs6tmucpbShKVmKOp2kpA27U+Tk9tUAYZem7OS4mdu155e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\nwBhjDBYGxhhjsDAwxhiDhYExxhjg/wE+KOMon02OFQAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, (ax1, ax2) = plt.subplots(1,2)\n",
"rfvis.plot_gaussian_fit(rf_data, ax1, ax2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4.65 vs 9.3 degree sparse noise stimuli\n",
"Newer experiments switched from using a single locally sparse noise stimulus with 4.54 visual-degree pixels to two blocks of stimuli with different pixel sizes (a 4.65 degree block and an 9.3 degree block that are each half the length of the original 4.65-degree-only stimulus). You can characterize the receptive fields from reponses to each stimulus block separately."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['locally_sparse_noise_4deg', 'locally_sparse_noise_8deg', 'natural_movie_one', 'natural_movie_two', 'spontaneous']\n",
"['drifting_gratings', 'natural_movie_one', 'natural_movie_three', 'spontaneous']\n",
"['static_gratings', 'natural_scenes', 'natural_movie_one', 'spontaneous']\n"
]
}
],
"source": [
"cell_specimen_id = 559109414\n",
"exps = boc.get_ophys_experiments(cell_specimen_ids=[cell_specimen_id])\n",
"for exp in exps:\n",
" print(boc.get_ophys_experiment_stimuli(exp['id']))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This cell comes from an experiment that has the new 4.65 degree and 9.3 degree stimulus blocks. Let's find the experiment that contains the 9.3 degree stimulus.\n",
"\n",
"**Note:** the NWB files refer to these stimuli as `locally_sparse_noise_4deg` and `locally_sparse_noise_8deg` respectively."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:allensdk.api.queries.brain_observatory_api:Downloading ophys_experiment 558599066 NWB. This can take some time.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"cell 559109414 has index 152\n"
]
}
],
"source": [
"exp = boc.get_ophys_experiments(cell_specimen_ids=[cell_specimen_id],\n",
" stimuli=['locally_sparse_noise_8deg'])\n",
"data_set = boc.get_ophys_experiment_data(exps[0]['id'])\n",
"cell_index = data_set.get_cell_specimen_indices([cell_specimen_id])[0]\n",
"print(\"cell %d has index %d\" % (cell_specimen_id, cell_index))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can run the receptive field analysis as before and see what this looks like."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rf_data = rf.compute_receptive_field_with_postprocessing(data_set, \n",
" cell_index, \n",
" 'locally_sparse_noise_8deg', \n",
" alpha=0.5, \n",
" number_of_shuffles=10000)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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oVTtsseL+yZff2K8E51u6Yx+kdNXc/UouAEnQHMZQ6+n6CntyWW1FxXr1fWwTPBgThnup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\nAAUImgBQgKAJAAUImgBQ4P8Aqm1lQ0hecfYAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"rfvis.plot_chi_square_summary(rf_data)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, (ax1, ax2) = plt.subplots(1,2)\n",
"rfvis.plot_rts_summary(rf_data, ax1, ax2)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 0
}