Is THIS why we dream? The strangeness of our dreams serves to help our brains better generalise our day-to-day experiences, expert claims
- Neuroscientist in the US presents AI-inspired argument for why humans dream
- Professor Erik Hoel believes it’s akin to adding random data to a neural network
- This stops human minds from ‘fitting too well to their daily distribution of stimuli’
Why we dream is one of science’s most perplexing mysteries, but a neuroscientist in the US 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
WHAT ARE NEURAL NETWORKS?
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.’
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.
Professor Hoel also believes people with sleep problems who don’t get enough opportunities to dream could benefit from the effects of psychedelic, abstract films or TV shows that portray dream-like states.
Classic examples include David Lynch’s Eraserhead and Victor Fleming’s The Wizard of Oz, as well as Stanley Kubrick’s 2001 Space Odyssey.
Shot from 2001 A Space Odyssey (1968), directed by Stanley Kubrick, which features strange, dream-like imagery – especially in the film’s final 30 minutes
Their dream-like nature can help delay the cognitive effects of sleep deprivation, by effectively becoming a substitute for dreams, he believes.
Brains are always learning new things – and that’s where the overfitted brain hypothesis comes in.
‘Life is boring sometimes,’ Professor Hoel said. ‘Dreams are there to keep you from becoming too fitted to the model of the world.’
One difference between the human and the machine is that you can simply turn off learning in artificial neural networks, but you can’t do that with the brain, Professor Hoel admits.
The expert has recently published his debut novel called The Revelations, which is about neuroscience, death and ‘the search for the theory of human consciousness’.
THE FOUR STAGES OF SLEEP
Pictured, different steps of the night sleep cycle. Most dreaming occurs during REM sleep (marked in red) although some can also occur in non-REM sleep
Sleep is generally separated in four stages. The first three of these are known as ‘non rapid eye movement’ or NREM sleep.
The last stage is known as rapid eye movement or REM sleep.
A typical night’s sleep goes back and forth between the stages.
Stage 1: In the first five minutes or so after dropping off we are not deeply asleep.
We are still aware of our surroundings but our muscles start to relax, the heart beat slows down and brainwave patterns, known as theta waves, become irregular but rapid.
Although we are asleep during Stage 1, we may wake up from it feeling like we didn’t sleep at all.
After around five minutes our bodies move into stage two.
Stage 2: This is when we have drifted into sleep, and if awakened would know you we been asleep. Waking up is still fairly easy.
This stage is identified by short bursts of electrical activity in the brain known as spindles, and larger waves known as K-complexes, which indicate that the brain is still aware of what is going on around it before turning off to a sub-conscious level.
Heartbeat and breathing is slow, and muscles relax even further.
Our body temperature drops and eye movements stop.
Brain wave activity slows but is marked by brief bursts of electrical activity.
Stage 3: Stage 3 non-REM sleep is the period of deep sleep that we need to feel refreshed in the morning.
It occurs in longer periods during the first half of the night.
Our heartbeat and breathing slow to their lowest levels during sleep and brain waves become even slower.
Our muscles are relaxed and it people may find it difficult to awaken us.
The body repairs muscles and tissues, stimulates growth and development, boosts immune function, and builds up energy for the next day.
Hypnagogia – the transitional state between wakefulness and sleep – is associated with NREM stages one to three.
Mental phenomena during hypnagogia include lucid thought, lucid dreaming, hallucinations and sleep paralysis.
REM sleep: REM sleep first occurs about 90 minutes after falling asleep.
Our eyes move rapidly from side to side behind closed eyelids.
Mixed frequency brain wave activity becomes closer to that seen in wakefulness.
Our breathing becomes faster and irregular, and heart rate and blood pressure increase to near waking levels.
Most dreaming occurs during REM sleep, although some can also occur in non-REM sleep.
Arm and leg muscles become temporarily paralysed, which prevents us from acting out our dreams.
As we age, we spend less of our time in REM sleep.
Memory consolidation most likely requires both non-REM and REM sleep.
Source: US National Institutes of Health
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