PT - JOURNAL ARTICLE AU - Abhinav Singh AU - Adrien Peyrache AU - Mark D. Humphries TI - Task Learning Reveals Neural Signatures of Internal Models In Rodent Prefrontal Cortex AID - 10.1101/027102 DP - 2016 Jan 01 TA - bioRxiv PG - 027102 4099 - http://biorxiv.org/content/early/2016/09/01/027102.short 4100 - http://biorxiv.org/content/early/2016/09/01/027102.full AB - The inherent uncertainty of the world suggests that optimally-performing brains should use probabilistic internal models to represent it. This idea has provided a powerful explanation for a range of behavioural phenomena. But describing behaviour in probabilistic terms is not strong evidence that the brain itself explicitly uses probabilistic models. We sought to test whether neurons represent such models in higher cortical regions, learn them, and use them in behaviour. Using a sampling framework, we predicted that trial-evoked and sleeping population activity represent the inferred and expected probabilities generated from an internal model of a behavioural task, and would become more similar as the task was learnt. To test these predictions, we analysed population activity from rodent prefrontal cortex before, during, and after sessions of learning rules on a Y-maze. We found that population activity patterns occurred far in excess of chance on millisecond time-scales. During successful learning, distributions of these activity patterns increased in similarity between trials and post-learning sleep as predicted. Learning-induced changes were greatest for patterns expressed at the maze’s choice point and predicting correct choice of maze arm to obtain reward, consistent with an updated internal model of the task. Our results suggest sample-based internal models are a general computational principle of cortex.Author Summary The cerebral cortex contains billions of neurons. The activity of one neuron is lost in this morass, so it is thought that the co-ordinated activity of groups of neurons – “neural ensembles” – are the basic element of cortical computation, underpinning sensation, cognition, and action. But what do these ensembles represent? Here we show that ensemble activity in rodent prefrontal cortex represents samples from an internal model of the world - a probability distribution that the world is in a specific state. We find that this internal model is updated during learning about changes to the world, and is sampled during sleep. These results suggest that probability-based computation is a generic principle of cortex.