Dynamics and dimensionality of information representation for higher cognitive function

Many cognitive functions are thought to rely on higher brain regions, in particular the prefrontal cortex. However, neural processing in prefrontal cortex can be notoriously complex, both in single neurons as well as at the population level. This thesis aims to investigate some of the representations and dynamics that emerge from the concerted activity of many neurons during cognitive processing. All experiments in this thesis involved the analysis of electrophysiological recordings from non-human primates taken while the animals performed complex cognitive tasks involving working memory and decision making components. In the first experiment, I analysed prefrontal cortex recordings from animals trained on a cognitively demanding dual-task that required simultaneous performance of two overlapping tasks. I found that many neurons were tuned to mixtures of features from both tasks, thus indicating possible interference on the neural level. However, despite mixed responses in single neurons, population level representations of the two tasks were nearly orthogonal effectively resolving interference between task representations. The temporal dynamics of the dual-task seemed to play a critical role in this orthogonalization process. In a second experiment I took a closer look at the temporal dynamics during cognitive processing by analysing recordings from multiple brain regions while animals performed a working memory task. I found that the heterogeneous dynamics of single neurons i.e. dynamic or stable delay coding could be predicted by their intrinsic temporal firing stability. In the last experiment I investigated how correlations between neurons influence the processing of decision related variables in single neurons. I found that decision related activity in response to a perceptually ambiguous stimulus was predicted by different timescales of interneuronal correlations. Finally, I summarize the major findings of this thesis and discuss future lines of research that aim to explore the circuit mechanisms underlying the observed task representations and how they might arise during learning