Neuroscientist proposes AI-inspired theory for why we dream

Why we dream is one of science’s most perplexing mysteries, but a neuroscientist thinks he finally has the answer.   

Erik Hoel, a research assistant professor of neuroscience at Tufts University in Massachusetts, has taken inspiration from artificial intelligence (AI) for his theory.

In a new report, he argues that the often hallucinogenic, nonsensical quality of dreaming is like throwing in new, unexpected data to a neural network.    

Professor Hoel calls this the ‘overfitted brain hypothesis’ – and argues that it keeps human minds from ‘fitting too well to their daily distribution of stimuli’.  

This illustration represents the overfitted brain hypothesis of dreaming, which claims that the sparse and hallucinatory quality of dreams helps prevent the brain from ‘overfitting’ to its biased daily sources of learning


Neural networks are a subset of machine learning and are at the heart of deep learning algorithms.

Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.

Neural networks rely on training data to learn and improve their accuracy over time.

Once these learning algorithms are fine-tuned for accuracy, they are powerful tools in computer science and artificial intelligence.

 Source: IBM

The hypothesis, presented in a paper published in the journal Patterns, suggests dreams are a naturally evolved technique to make our understanding of the world less simplistic and more well-rounded. 

Like neural networks, our brains become too familiar with the ‘training set’ of our everyday lives, he argues. 

To counteract the familiarity, the brain creates a weird version of the world in dreams – a kind of introduced ‘chaos’, intended to keep us on our toes. 

‘It is the very strangeness of dreams in their divergence from waking experience that gives them their biological function,’ writes Professor Hoel, who is also an author of fiction. 

‘By hallucinating out-of-distribution sensory stimulation every night, the brain is able to rescue the generalisability of its perceptual and cognitive abilities and increase task performance.’               

It’s hard to prove concretely why dreams occur and the neuroscience field is saturated with hypotheses.

Legendary Austrian neurologist Sigmund Freud famously viewed dreams as guides to the unconscious, arguing that the thoughts, feelings and drives that threaten the waking mind are released as distorted and disguised images in our dreams.  

‘There’s obviously an incredible number of theories of why we dream,’ said Professor Hoel. 

‘But I wanted to bring to attention a theory of dreams that takes dreaming itself very seriously – that says the experience of dreams is why you’re dreaming.’

A neural network is a computer system modelled on the human brain and nervous system (concept image)

 A neural network is a computer system modelled on the human brain and nervous system (concept image)

Sigmund Freud (pictured) famously viewed dreams as guides to the unconscious

Sigmund Freud (pictured) famously viewed dreams as guides to the unconscious

Neural networks rely on training data – for example, photographs of people or animals – to learn and improve their accuracy over time. 

But a common problem when it comes to training neural networks is that they can become too familiar with the data they’ve been trained on. 

As a result, neural networks become in danger of assuming that the training set is a perfect representation of anything it might encounter. 

Data scientists fix this problem, known as ‘overfitting’, by introducing some weird, nonsensical ‘chaos’ into the data – for example, showing a self-driving cars some random black boxes.

This is akin to what’s happening when we’re exposed to bizarre concepts during dreams, which get particularly vivid during REM sleep – the fourth stage of sleep.     

While comparing the brain to technology is not new, Professor Hoel said that using deep neural networks to describe the overfitted brain hypothesis was a natural connection. 

‘If you look at the techniques that people use in regularisation of deep learning, it’s often the case that those techniques bear some striking similarities to dreams,’ he said. 

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