The human brain is used as a comparison for how computer’s function. But, honestly, computers are nothing like human brains. Not yet, at least.
That could change as researchers have developed computing technology that uses light to mimic the functionality of a nerve’s synapse, opening the way for hardware that combines the speed of modern processors with the efficiency of brainpower.
Brains and computers are both systems that can model, manipulate, and store information. From there, they don’t tend to have all that much in common.
While processors in computers combine electrical impulses with tiny on-off switches to perform functions, neurons use chemical tides to distribute impulses across multiple channels called synapses.
The difference is significant as far as memory and power consumption go – no hardware can come close to the efficiency and storage capabilities of a human brain.
Not that our grey matter is an all-star performer; those waves of electrolytes and neurotransmitters can’t beat the speed of electrons zipping through logic gates.
A team of researchers from Oxford, Münster and Exeter Universities has nailed what it sees as a “holy grail” of computing, creating a photonic integrated circuit that acts like a synapse.
“The development of computers that work more like the human brain has been a holy grail of scientists for decades,” says senior researcher Harish Bhaskaran from the University of Oxford.
“Via a network of neurons and synapses the brain can process and store vast amounts of information simultaneously, using only a few tens of watts of power. Conventional computers can’t come close to this sort of performance.”
To get technical, your desktop computer is based on von Neumann architecture, named after the renowned mathematician and physicist John von Neumann.
That is to say, there are units of processors for handling logic and memory.
Your brain doesn’t have a CPU in the front and a hard drive in the back. Neurons connected in a branching network, separated by tiny synaptic bridges, are all-in-one processors and storage devices.
To function, channels in the nerve’s membrane open and close, sending ripples of charged ions rushing in and out in a low voltage Mexican wave.
These are mediated by other chemical processes at the tips of the nerve’s branches. Depending on factors such as the strength or frequency of the wave, surges of neurotransmitters can continue the message by jumping the gap to other nerves.
That small leap at the end of a nerve is the business end of neural processing, acting as a traffic control officer that stops or accelerates a signal.
Described as synaptic plasticity, changes in this control point can account for how we learn and process new information, strengthening some circuits while allowing others to wither.
So-called neuromorphic computing aspires to replicate this way of combining processing and memory in one system, bringing biology and artificial intelligence even closer together.
The trick has been to make a processor that can do what a synapse can do.
“Since synapses outnumber neurons in the brain by around 10,000 to 1, any brain-like computer needs to be able to replicate some form of synaptic mimic. That is what we have done here,” says Wolfram Pernice from the University of Münster.
The team’s artificial synapse is based on structures made of a phase-change material (PCM), that stores and releases significant amounts of energy as it changes from one state to another.
Light waves are channelled through the material, with optical pulses switching the PCM in such a way that it mimics a synapse’s plasticity.
While the concepts aren’t new, this is the first time the process has been realised in practice.
“Electronic computers are relatively slow, and the faster we make them the more power they consume,” says researcher C David Wright from the University of Exeter.
“Conventional computers are also pretty ‘dumb’, with none of the in-built learning and parallel processing capabilities of the human brain.”
Light-based neuromorphic processors look to be the perfect melding of mind and machine.
The only question is, how long do I need to now wait to get my brain upgraded?
This research was published in Science Advances.