How Machines Learn Like Humans: A Look at Brain and Artificial Neural Networks

Category Science

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A study published last month suggests that natural and artificial networks learn in similar ways, at least when it comes to language. The study used a type of neural network architecture called a generative adversarial network (GAN) and found that both the human and artificial brain signals were uncannily similar, implying human brains may not come already prepared for language processing.

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How do brains learn? It's a mystery, one that applies both to the spongy organs in our skulls and to their digital counterparts in our machines. Even though artificial neural networks (ANNs) are built from elaborate webs of artificial neurons, ostensibly mimicking the way our brains process information, we don't know if they process input in similar ways. "There's been a long-standing debate as to whether neural networks learn in the same way that humans do," said Vsevolod Kapatsinski, a linguist at the University of Oregon.

GANs are an example of an artificial neural networks (ANNs)

Now, a study published last month suggests that natural and artificial networks learn in similar ways, at least when it comes to language. The researchers - led by Gašper Beguš, a computational linguist at the University of California, Berkeley - compared the brain waves of humans listening to a simple sound to the signal produced by a neural network analyzing the same sound. The results were uncannily alike. "To our knowledge," Beguš and his colleagues wrote, the observed responses to the same stimulus "are the most similar brain and ANN signals reported thus far." .

The researchers in the study used GANs to model language processing

Most significantly, the researchers tested networks made up of general-purpose neurons that are suitable for a variety of tasks. "They show that even very, very general networks, which don't have any evolved biases for speech or any other sounds, nevertheless show a correspondence to human neural coding," said Gary Lupyan, a psychologist at the University of Wisconsin, Madison who was not involved in the work. The results not only help demystify how ANNs learn, but also suggest that human brains may not come already equipped with hardware and software specially designed for language.

The discriminator in GANs is initially trained on a collection of sounds to detect differences in the input

To establish a baseline for the human side of the comparison, the researchers played a single syllable - "bah" - repeatedly in two eight-minute blocks for 14 English speakers and 15 Spanish speakers. While it played, the researchers recorded fluctuations in the average electrical activity of neurons in each listener's brainstem - the part of the brain where sounds are first processed.

In addition, the researchers fed the same "bah" sounds to two different sets of neural networks - one trained on English sounds, the other on Spanish. The researchers then recorded the processing activity of the neural network, focusing on the artificial neurons in the layer of the network where sounds are first analyzed (to mirror the brainstem readings). It was these signals that closely matched the human brain waves.

The generator is trained to respond to the feedback given by the discriminator to produce the desired output

The researchers chose a kind of neural network architecture known as a generative adversarial network (GAN), originally invented in 2014 to generate images. A GAN is composed of two neural networks - a discriminator and a generator - that compete against each other. The generator creates a sample, which could be an image or a sound. The discriminator determines how close it is to a training sample and offers feedback, resulting in another try from the generator, and so on until the GAN can deliver the desired output.

The study found that both the human and artificial brain signals were uncannily similar

In this study, the discriminator was initially trained on a collection of either English or Spanish sounds. Then the generator - which was initialized with random weights - used that feedback to eventually generate a sound that matched the input.

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