The brain is often regarded as a soft-matter chemical computer, but the way it processes information is very different to that of conventional silicon circuits. Three groups now describe chemical systems capable of storing information in a manner that resembles the way that neurons communicate with one another at synaptic junctions. Such ‘neuromorphic’ devices could provide very-low-power computation and act as interfaces between conventional electronics and ‘wet’ chemical systems, potentially including neurons and other living cells themselves.

At a synapse, the electrical pulse or action potential that travels along a neuron triggers the release of neurotransmitter molecules that bridge the junction to the next neuron, altering the state of the second neuron by making it more or less likely to fire its own action potential. If one neuron repeatedly influences another, the connection between them may become strengthened. This is how information is thought to become imprinted as a memory, a process called Hebbian learning. The ability of synapses to adjust their connectivity in response to input signals is called plasticity, and in neural networks it typically happens on two timescales. Short-term plasticity (STP) creates connectivity patterns that fade quite fast and are used to filter and process sensory signals, while long-term plasticity (LTP, also called long-term potentiation) imprints more long-lived memories. Both biological processes are still imperfectly understood.

Neuromorphic circuits that display such learning behaviour have been developed previously using solid-state electronic devices called memristors, two-terminal devices in which the relationship between the current that passes through and the voltage applied depends on the charge that passed through previously. Memristors may retain this memory even when no power is applied – they are ‘non-volatile’ – meaning that neuromorphic circuits can potentially process information with very low power consumption, a feature crucial to the way our brains can function without overheating. Typically, memristor behaviour manifests as a current–voltage relationship on a loop, and the response varies depending on whether the voltage is increasing or decreasing: a property called hysteresis, which itself represents a kind of memory as the device behaviour is contingent on its history.