Brains are made up of many different types of neurons (nerve cells in the brain), each of which are thought to play different roles in processing information. Scientists have long been able to use electrodes to record the activity of neurons by detecting the electrical ‘spikes’ that they generate while performing brain functions. Although recording spikes has proved invaluable for monitoring the activity of individual neurons deep in the brain, until now the method has been ‘blind’ to the type of neuron being recorded – making it impossible to identify how different neurons contribute to the brain’s overall operation.

The research team have overcome this problem by identifying the distinct ‘electrical signatures’ of different neuron types in the mouse brain, using brief pulses of blue light to trigger spikes in specific cell types (a method called optogenetics). They created a library of the different electrical signatures for each type of neuron, which then allowed them to train an AI algorithm that can automatically recognise five different types of neurons with 95% accuracy without further need for genetic tools. The algorithm was also validated on brain recording data from monkeys.

The researchers say they have overcome a major hurdle in being able to use the technology to study neurological conditions such as epilepsy, but that there is still a long way to go before it can be used in practical applications.Dr Maxime Beau, co-first author of the study from the UCL Wolfson Institute for Biomedical Research, said: For decades, neuroscientists have struggled with the fundamental problem of reliably identifying the many different types of neurons that are simultaneously active during behaviour. Our approach now enables us to identify neuron types with over 95% accuracy in mice and in monkeys.

Source: https://www.ucl.ac.uk/news/2025/may/breakthrough-uses-artificial-intelligence-identify-different-brain-cells-action