Neural circuit basis of flexible behaviour

Year of award: 2023


  • Dr Adil Khan

    King's College London, United Kingdom

Project summary

In order to survive, animals must flexibly update their behaviour in response to changes in the environment. This ability to adapt ongoing behaviour is one of the most fundamental of cognitive processes, yet its underlying neural mechanisms remain poorly understood. The framework of predictive processing provides a simple yet powerful way of describing flexible behaviour. While this account has widespread support across species, the neural circuit basis of this process is largely unknown. In this project, I will address the following question: What brain-wide neural circuits enable animals to compute cognitive prediction-errors, and use these to flexibly adapt their behaviour? My lab's recent work has identified prediction-error encoding neurons in mouse anterior cingulate cortex (ACC) which provide a substrate for the computations underlying flexible behaviour. Building on these results, I hypothesise that ACC is part of a network of brain areas that utilise inhibitory microcircuits to compute prediction-errors. These prediction-error signals drive task switching behaviours by engaging neuromodulatory circuits. I will test this hypothesis by 1) Establishing a causal map of prediction-error signalling across cortex. 2) Identifying the comparator circuit which computes the prediction-error. 3) Identify the specific output route through which the prediction-error signal influences the animal's behaviour.