A new computational model of the brain based closely on its biology and physiology not only learned a simple visual category learning task exactly as well as lab animals, but even enabled the discovery of counterintuitive activity by a group of neurons that researchers working with animals to perform the same task had not noticed in their data before.

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Researchers at Dartmouth, MIT, and Stony Brook developed a computational brain model that mimics biological neural circuits and learned a visual categorization task as effectively as lab animals, without training on animal data. The model incorporates both microscale neuron connections and macroscale brain architecture across four regions (cortex, brainstem, striatum, and TAN structure). It revealed previously unnoticed "incongruent" neurons that predict errors, which researchers then confirmed in existing animal experiment data. The team founded Neuroblox.ai to apply this biomimetic modeling platform for drug development and neurotherapeutics testing before clinical trials.

Biology-based brain model matches animals in learning, enables new discovery