Neurons learn by predicting future activity.

Abstract

Understanding how the brain learns may lead to machines with human-like intellectual capacities. However, learning mechanisms in the brain are still not well understood. Here we demonstrate that the ability of a neuron to predict its future activity may provide an effective mechanism for learning in the brain. We show that comparing a neurons predicted activity with the actual activity provides a useful learning signal for modifying synaptic weights. Interestingly, this predictive learning rule can be derived from a metabolic principle, where neurons need to minimize their own synaptic activity (cost), while maximizing their impact on local blood supply by recruiting other neurons. This reveals an unexpected connection that learning in neural networks could result from simply maximizing the energy balance by each neuron. We show how this mathematically derived learning rule can provide a theoretical connection between diverse types of brain-inspired algorithms, such as: Hebbs rule, BCM theory, temporal difference learning and predictive coding. Thus, this may offer a step toward development of a general theory of neuronal learning. We validated this predictive learning rule in neural network simulations and in data recorded from awake animals. We found that in the sensory cortex it is indeed possible to predict a neurons activity [~]10-20ms into 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.