The brain is using a learning algorithm which is yet to be discovered. Here we demonstrate that the ability of a neuron to predict its expected future activity may be an important missing component to understand learning in the brain. We show that comparing predicted activity with the actual activity can provide an error signal for modifying synaptic weights. Importantly, this learning rule can be derived from minimizing neuron metabolic cost. This reveals an unexpected connection, that learning in neural networks could result from simply maximizing energy balance by each neuron. We validated this predictive learning rule in neural network simulations and in data recorded from awake animals. We found that neurons in the sensory cortex can indeed predict their activity ~10-20ms in the future. Moreover, in response to stimuli, cortical neurons changed their firing rate to minimize surprise: i.e. the difference between actual and expected activity, as predicted by our model. Our results also suggest that spontaneous brain activity provides "training data" for neurons to learn to predict cortical dynamics. Thus, this work demonstrates that the ability of a neuron to predict its future inputs could be an important missing element to understand computation in the brain.
bioRxiv Subject Collection: Neuroscience