-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathindex.qmd
124 lines (105 loc) · 5.7 KB
/
index.qmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
---
title: "A High-Dimensional View of Neuroscience"
subtitle: "[Tutorial session](https://2023.ccneuro.org/kt3.php) at [Cognitive Computational Neuroscience 2023](https://2023.ccneuro.org/kt3.php)"
abstract: |
Advances in technology enable us to record neural responses to many thousands of stimuli from a huge number of channels (e.g. fMRI in humans, two-photon imaging in mice, neuropixel probes in monkeys). Given the unprecedented scale of these data -- collected with incredible effort at enormous expense -- what computational tools can we use to study neural representations in high dimensions? What theoretical insights can we gain about the nature of neural representations from large-scale datasets?
abstract-title: Motivation
affiliations:
- id: jhu
name: Johns Hopkins University
url: https://cogsci.jhu.edu/
- id: psl
name: École normale supérieure
url: https://www.ens.psl.eu/en/ens
author:
- name:
given: Raj Magesh
family: Gauthaman
url: https://raj-magesh.org/
email: [email protected]
orcid: 0000-0001-7121-1532
affiliation:
- ref: jhu
roles:
- [conceptualization, data curation, methodology, investigation, analysis, software, writing, editing, visualization]
- name:
given: Florentin
family: Guth
email: [email protected]
affiliation:
- ref: psl
roles:
- [conceptualization, data curation, methodology, investigation, analysis, software, writing, editing, visualization]
- name:
given: Atlas
family: Kazemian
url: https://akazemian.github.io/personal_profile/
email: [email protected]
orcid: 0000-0001-7699-2964
affiliation:
- ref: jhu
roles:
- [conceptualization, editing, visualization, validation]
- name:
given: Zirui
family: Chen
url: https://zche377.github.io/
email: [email protected]
orcid: 0000-0003-3666-1719
affiliation:
- ref: jhu
roles:
- [conceptualization, editing, visualization, validation]
- name:
given: Michael
family: Bonner
url: https://bonnerlab.org/
email: [email protected]
orcid: 0000-0002-4992-674X
affiliation:
- ref: jhu
roles:
- [supervision, conceptualization, methodology, editing]
license:
text: "This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License."
type: open-access
url: https://creativecommons.org/licenses/by-sa/4.0/
citation:
type: webpage
format:
html:
code-tools:
source: false
toggle: false
toc: false
---
## Welcome!
This site contains material for a [tutorial](https://2023.ccneuro.org/kt3.php) presented at the conference on [Cognitive Computational Neuroscience 2023](https://2023.ccneuro.org/).
::: {.callout-important}
# Don't miss the tutorial!
Where
: East Schools
When
: Saturday, August 26, 2023 @ 10:45 - 12:30
:::
::: {.callout-tip}
# Run the tutorial interactively -- or just follow along on the website!
Each section is a [computational notebook](https://docs.jupyter.org/en/latest/index.html) that can be run interactively on [Google Colab](https://colab.research.google.com/) or viewed rendered on this site -- just follow the links below!
:::
| Section | Read | Interact | Download |
|:-----|:-------:|:--------:|:-------------:|
| Introducing PCA | [website](https://bonnerlab.github.io/ccn-tutorial/pages/introducing_pca.html) | [Colab](https://colab.research.google.com/github/BonnerLab/ccn-tutorial/blob/main/notebooks/introducing_pca.ipynb) | [download](https://bonnerlab.github.io/ccn-tutorial/pages/introducing_pca.ipynb) |
| Exploring neural data | [website](https://bonnerlab.github.io/ccn-tutorial/pages/exploring_neural_data.html) | [Colab](https://colab.research.google.com/github/BonnerLab/ccn-tutorial/blob/main/notebooks/exploring_neural_data.ipynb) | [download](https://bonnerlab.github.io/ccn-tutorial/pages/exploring_neural_data.ipynb) |
| Dealing with noise | [website](https://bonnerlab.github.io/ccn-tutorial/pages/dealing_with_noise.html) | [Colab](https://colab.research.google.com/github/BonnerLab/ccn-tutorial/blob/main/notebooks/dealing_with_noise.ipynb) | [download](https://bonnerlab.github.io/ccn-tutorial/pages/dealing_with_noise.ipynb) |
| Comparing representations | [website](https://bonnerlab.github.io/ccn-tutorial/pages/comparing_representations.html) | [Colab](https://colab.research.google.com/github/BonnerLab/ccn-tutorial/blob/main/notebooks/comparing_representations.ipynb) | [download](https://bonnerlab.github.io/ccn-tutorial/pages/comparing_representations.ipynb) |
| Analyzing neural networks | [website](https://bonnerlab.github.io/ccn-tutorial/pages/analyzing_neural_networks.html) | [Colab](https://colab.research.google.com/github/BonnerLab/ccn-tutorial/blob/main/notebooks/analyzing_neural_networks.ipynb) | [download](https://bonnerlab.github.io/ccn-tutorial/pages/analyzing_neural_networks.ipynb) |
::: {.callout-note collapse="true"}
# If you'd prefer to run the notebooks locally...
Create a [Python virtual environment](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/#creating-a-virtual-environment) with `Python >=3.10.12` to run the notebooks. The required dependencies will be automatically installed when you run the first cell of each notebook.
:::
::: {.callout-warning}
# Notice a typo? Have any feedback?
Use the `Report an issue` button on the sidebar of each page to contact us. Feel free to suggest edits by using the `Edit this page` button too!
:::
## Acknowledgments {.appendix}
Thanks to the [Natural Scene Dataset](http://naturalscenesdataset.org/) team for permission to use it for this tutorial and to the [Open Science Foundation](https://osf.io/) for hosting the [data files](https://osf.io/zk265/).