New AI brain model mirrors lab animal behaviour without using animal data
Researchers can now explore brain computation and treatments in silico, potentially reducing the need for early-stage animal trials.
A new computational brain model, built entirely from biological principles, has learned a visual categorisation task with accuracy and variability matching that of lab animals. Remarkably, the model achieved these results without being trained on any animal data.
The biomimetic design integrates detailed synaptic rules with large-scale architecture across the cortex, striatum, brainstem, and acetylcholine-modulated systems.
As the model learned, it reproduced neural rhythms observed in real animals, including strengthened beta-band synchrony during correct decisions. The result demonstrates emergent realism in both behaviour and underlying neural activity.
The model also revealed a previously unnoticed set of ‘incongruent neurons’ that predicted errors. When researchers revisited animal data, they found the same signals had gone undetected, highlighting the platform’s potential to uncover hidden neural dynamics.
Beyond neuroscience research, the model offers a powerful tool for testing neurotherapeutic interventions in silico. Simulating disease-related circuits allows scientists to test treatments before costly clinical trials, potentially speeding up the development of next-generation neurotherapeutics.
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