Neurons receive precisely tailored teaching signals as we learn

MIT neuroscientists have found the first biological evidence that the brain sends individualized, vectorized error signals to specific neurons during learning — similar to backpropagation in artificial neural networks. Using a brain-computer interface that linked the activity of 8–10 neurons in mice directly to rewards, researchers observed that neurons requiring increased activity and those requiring decreased activity received opposing instructive signals at their dendrites. Blocking these dendritic signals prevented learning. The findings bridge neuroscience and machine learning, suggesting the brain uses a targeted, cell-specific feedback mechanism rather than only the broad neuromodulator-based reinforcement previously understood.