Neuroscientists recovered speech from brain signals

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Researchers reconstructed songs of birds by recording the brain activity from a sensory-motor nucleus.

#Neuroscience #Brain #YuriNeuro

Timecodes:
0:00-Introdudction
0:28-Original publication
0:36-Biomechanical model and FFNN
1:30- Results of the song reconstruction by biomechanical model and FFNN
2:47- Song reconstruction directly from the brain activity using LSTM neural networks
3:21- Results of the direct prediction from LSTM. Comparison to biomechanical model
3:58- Further advantages of the combination of FFNN and biomechanical model
4:27- Potential applications to humans
5:47- Share your ideas

Neurobiologists from University of California, San Diego recorded neuronal activity in the sensorimotor part of the brain (HVC) of songbirds, which controls the muscles responsible for singing. Also, scientists employed a biomechanical model of the vocal organ that allowed to link the brain activity to the ultimate changes in pressure and tension in the vocal organ of birds during singing. By using used a shallow feedforward neural network (FFNN) with one hidden layer neuroscientists estimated parameters of the biomechanical model and used this model to reproduce the song of the bird.

The spectrograms of the songs produced by the birds and predicted by the model exhibited a close similarity. Also, neuroscientists used recurrent, long-short-term memory neural network (LSTM) to reconstruct the song directly from the brain activity. In summary, the best result was achieved by combination of a biomechanical model and FFNN to estimate model parameters.

This techonolgy provides an opportunity to capture the voice with its pitch, volume and timbre in real time from the analysis of the brain activity, which is feasible for humans. This scientific work makes an important step toward the development of the vocal prosthesis, that in the future may allow people who lost their voices to get back an ability to speak without a temporal delay, as well as to exhibit emotions.

Explore other intersting facts about the brain by checking the other neuroscience videos

Train your brain by solving logical and math puzzles

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Timecodes:
0:00-Introdudction
0:28-Original publication
0:36-Biomechanical model and FFNN
1:30- Results of the song reconstruction by biomechanical model and FFNN
2:47- Song reconstruction directly from the brain activity using LSTM neural networks
3:21- Results of the direct prediction from LSTM. Comparison to biomechanical model
3:58- Further advantages of the combination of FFNN and biomechanical model
4:27- Potential applications to humans
5:47- Share your ideas

yurineuro
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Highly underrated!!! I love how upbeat it is. I was honestly surprised to see that you only had ~1500 subs.

arkfish
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I got this as an ad, this looks interesting

want-diversecontent
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I saw a clip of reconstructed sounds from brain activity and fell down the youtube rabbit hole.

These reconstructed sounds are really unsettling

Octolithic
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Thanks man. It will help to explore the neuroscience.

nurulhuda
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I like the possibility of this technology being used inside the Neuralink

EzzieMoore
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So basically we have a mind reading machine now. Scary

MacetazzOpina
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So hard to understand because you speak so fast...slow down and pause between thoughts please

DIAMONDGOIL
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I would love to interview you more on this topic

ashmichael
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I liked the video. You might want to invest in a better mic for clarity.
The topic is very interesting.

sbsnate
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А представьте как это могут использовать музыканты! Придумал симфонию в голове, проиграл, и готово! Ничего даже делать не нужно😄😄 мечты-мечты...

utof
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Блин, я сразу понял что ты русский)
Добавь русские субтитры пж)))

liltousin
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You speak too fast, I cannot follow... :(

dariaglushkina
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Dude chill down a bit, this video is really hard to follow due to your way to speech, to stressed out and fast, be more chill and lower the music
Also just wear a t-shirt/do not show the video, if you as want to be well dresses that's okay but be sure to actually be well dresses otherwise that's just another element of distraction for the viewer
Aside from that the things are interesting, if you work a bit on the part of the video that I've told you you could easely gain a lot of follow imho

NHCH
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