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Projects

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Brainhack Global 2024

    Projects

    diff --git a/project/index.xml b/project/index.xml index 5340671..e90a291 100644 --- a/project/index.xml +++ b/project/index.xml @@ -1 +1 @@ -Projects | Brainhack Global 2024https://www.brainhack.org/global2024/project/ProjectsWowchemy (https://wowchemy.com)en-us© Brainhack Global 2024https://www.brainhack.org/global2024/images/icon_hu405768a2e56dba1817ce36477148a60d_2978_512x512_fill_lanczos_center_3.pngProjectshttps://www.brainhack.org/global2024/project/behapy: A behavioural neuroscience analysis package for Pythonhttps://www.brainhack.org/global2024/project/project_5/Mon, 01 Jan 0001 00:00:00 +0000https://www.brainhack.org/global2024/project/project_5/BIDS: IP-Freelyhttps://www.brainhack.org/global2024/project/project_1/Mon, 01 Jan 0001 00:00:00 +0000https://www.brainhack.org/global2024/project/project_1/EEL-Hack: Learning to develop an mTRF pipeline with eelbrainhttps://www.brainhack.org/global2024/project/project_19/Mon, 01 Jan 0001 00:00:00 +0000https://www.brainhack.org/global2024/project/project_19/ME-mppca: Building a tensor-based thermal denoising methodhttps://www.brainhack.org/global2024/project/project_21/Mon, 01 Jan 0001 00:00:00 +0000https://www.brainhack.org/global2024/project/project_21/MRI based brain thermometryhttps://www.brainhack.org/global2024/project/project_17/Mon, 01 Jan 0001 00:00:00 +0000https://www.brainhack.org/global2024/project/project_17/MRI denoising using MP-PCAhttps://www.brainhack.org/global2024/project/project_2/Mon, 01 Jan 0001 00:00:00 +0000https://www.brainhack.org/global2024/project/project_2/Neurodesk: build interactive examples to learn about neuroimaging analysishttps://www.brainhack.org/global2024/project/project_8/Mon, 01 Jan 0001 00:00:00 +0000https://www.brainhack.org/global2024/project/project_8/PsyR: An R package for better inference in multivariate statistical analysishttps://www.brainhack.org/global2024/project/project_12/Mon, 01 Jan 0001 00:00:00 +0000https://www.brainhack.org/global2024/project/project_12/TIYA: Tune Into Your heArthttps://www.brainhack.org/global2024/project/project_14/Mon, 01 Jan 0001 00:00:00 +0000https://www.brainhack.org/global2024/project/project_14/ \ No newline at end of file +Projects | Brainhack Global 2024https://www.brainhack.org/global2024/project/ProjectsWowchemy (https://wowchemy.com)en-us© Brainhack Global 2024https://www.brainhack.org/global2024/images/icon_hu405768a2e56dba1817ce36477148a60d_2978_512x512_fill_lanczos_center_3.pngProjectshttps://www.brainhack.org/global2024/project/Bayes and Bach: An RNN-powered affairhttps://www.brainhack.org/global2024/project/project_23/Mon, 01 Jan 0001 00:00:00 +0000https://www.brainhack.org/global2024/project/project_23/behapy: A behavioural neuroscience analysis package for Pythonhttps://www.brainhack.org/global2024/project/project_5/Mon, 01 Jan 0001 00:00:00 +0000https://www.brainhack.org/global2024/project/project_5/BIDS: IP-Freelyhttps://www.brainhack.org/global2024/project/project_1/Mon, 01 Jan 0001 00:00:00 +0000https://www.brainhack.org/global2024/project/project_1/EEL-Hack: Learning to develop an mTRF pipeline with eelbrainhttps://www.brainhack.org/global2024/project/project_19/Mon, 01 Jan 0001 00:00:00 +0000https://www.brainhack.org/global2024/project/project_19/ME-mppca: Building a tensor-based thermal denoising methodhttps://www.brainhack.org/global2024/project/project_21/Mon, 01 Jan 0001 00:00:00 +0000https://www.brainhack.org/global2024/project/project_21/MRI based brain thermometryhttps://www.brainhack.org/global2024/project/project_17/Mon, 01 Jan 0001 00:00:00 +0000https://www.brainhack.org/global2024/project/project_17/MRI denoising using MP-PCAhttps://www.brainhack.org/global2024/project/project_2/Mon, 01 Jan 0001 00:00:00 +0000https://www.brainhack.org/global2024/project/project_2/Neurodesk: build interactive examples to learn about neuroimaging analysishttps://www.brainhack.org/global2024/project/project_8/Mon, 01 Jan 0001 00:00:00 +0000https://www.brainhack.org/global2024/project/project_8/PsyR: An R package for better inference in multivariate statistical analysishttps://www.brainhack.org/global2024/project/project_12/Mon, 01 Jan 0001 00:00:00 +0000https://www.brainhack.org/global2024/project/project_12/TIYA: Tune Into Your heArthttps://www.brainhack.org/global2024/project/project_14/Mon, 01 Jan 0001 00:00:00 +0000https://www.brainhack.org/global2024/project/project_14/ \ No newline at end of file diff --git a/project/project_23/index.html b/project/project_23/index.html new file mode 100644 index 0000000..da0e0e0 --- /dev/null +++ b/project/project_23/index.html @@ -0,0 +1,24 @@ + +Bayes and Bach: An RNN-powered affair | Brainhack Global 2024

    Bayes and Bach: An RNN-powered affair

    Last updated on +Nov 17, 2024

    Title

    Bayes and Bach: An RNN-powered affair

    Leaders

    Alejandro Tabas (mattermost: @atabas)

    Collaborators

    No response

    Brainhack Global 2024 Event

    Brainhack Donostia

    Project Description

    We want to do a proof-of-concept: test whether artificial recurrent neural networks (RNN) learn to predict the next tone when trying to perceive music in a noisy environment.

    This is an accessible project that does not require an extensive coding expertise. Participants that want to contribute with coding will be recommended to do this in python, although data retrieval can be done in any other language. Participants who do not wish to contribute with code can contribute either with a) their musical expertise in Data Preprocessing and during the interpretation of the Testing, or b) their patience and web-browsing skills to manual data retrieval.

    We will start by collecting a lot of Bach’s compositions. We will transform each composition into a sequence of tokens: each token will be one note or several notes (i.e., a chord) that were meant to be played together in the original composition. For this proof-of-concept we will ignore information about the duration or loudness of each note/chord. For each sequence, we will generate two items: a ground-truth (the sequence itself), and a set of observations (the sequence plus some noise that will corrupt part of the information).

    We will then present the observations to the RNN and train its weights so that, for each of the observation sequences, the RNN is able to extract the ground-truth sequence. In other words: we will train the RNNs to perceive the original musical sequence usign only the noisy observations.

    After the training we will test whether the RNN is encoding, in its internal states, a set of predictions on the next token. If that is the case, we will conclude that the RNN is actively attempting to predict the next item to implement perception.

    The results will help us generating hypotheses on how the brain decodes music. The neuroscientific community will be able to use these hypotheses to inform the design of experiments IRL brains.

    More details and resources in the github repository

    https://github.com/qtabs/BayesPlusBach/ +https://mattermost.brainhack.org/brainhack/channels/bh-donostia-2024--bayes-3-bach

    Goals for Brainhack Global

    Milestones:

    • A web scrapper that collects MIDI files of Bach’s compositions
    • A collection of MIDI files of Bach’s compositions
    • A MIDI-to-data pipeline that transforms MIDI files in pairs of (ground-truth, observation) that can be used for training
    • Several RNN architectures designed to retrieve the ground truth from noisy observations of sequences of tones/chords
    • The trained RNN(s)
    • Validation/refutation of the starting hypothesis

    Good first issues

    1. Automatise the retrieval of Bach’s MIDIs
    2. Automatise the encoding of a MIDI into a training sample for the RNN
    3. Build an RNN architecture to predict the training samples.

    Communication channels

    https://mattermost.brainhack.org/brainhack/channels/bh-donostia-2024--bayes-3-bach

    Skills

    Python: all levels

    Onboarding documentation

    https://github.com/qtabs/BayesPlusBach/blob/main/CONTRIBUTING.md

    What will participants learn?

    Web scrapping with python +Training RNNs with pytorch

    Data to use

    Resources on MIDI files: This website +could be a good place to start. This other website +has a lot of links with potentially better quality MIDI files. The better the quality of the MIDI, the easier the work of the preprocesser(s)!

    Resources on data scrappers: I used this tutorial +when I needed to implemet a scraper in the past. This other tutorial +also looks informative and easy to follow.

    Number of collaborators

    3

    Credit to collaborators

    Contributors will be listed in the contributors textfile.

    Image

    00017-3587716162

    Type

    method_development

    Development status

    0_concept_no_content

    Topic

    machine_learning

    Tools

    other

    Programming language

    Python

    Modalities

    behavioral

    Git skills

    0_no_git_skills

    Anything else?

    No response

    Things to do after the project is submitted and ready to review.

    • Add a comment below the main post of your issue saying: Hi @brainhackorg/project-monitors my project is ready!
    • Twitter-sized summary of your project pitch.

    Date
    Jan 1, 0001 12:00 AM
    + + + + + + + + +
    \ No newline at end of file diff --git a/projects/index.html b/projects/index.html index e70c73f..265ba88 100644 --- a/projects/index.html +++ b/projects/index.html @@ -78,7 +78,25 @@ single neuron models statistical modelling systems neuroscience -tractography

    behapy: A behavioural neuroscience analysis package for Python

    Studies using optic fibres to record real-time fluorescent biosensors in-vivo are now commonplace, yet despite an increasing …

     Project URL +tractography

    Bayes and Bach: An RNN-powered affair

    We want to do a proof-of-concept: test whether artificial recurrent neural networks (RNN) learn to predict the next tone when …

     Project URL + +Git level: 0 none + +Hub donostia esp + +behavioral + +documentation + +Python + +Skills: expert + +documentation + +method development + +machine learning

    behapy: A behavioural neuroscience analysis package for Python

    Studies using optic fibres to record real-time fluorescent biosensors in-vivo are now commonplace, yet despite an increasing …

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