Attracting Dynamics of Frontal Cortex Ensembles during Memory-Guided Decision-Making

A common theoretical view is that attractor-like properties of neuronal dynamics underlie cognitive processing. However, although often proposed theoretically, direct experimental support for the convergence of neural activity to stable population patterns as a signature of attracting states has been sparse so far, especially in higher cortical areas. Combining state space reconstruction theorems and statistical learning techniques, we were able to resolve details of anterior cingulate cortex (ACC) multiple single-unit activity (MSUA) ensemble dynamics during a higher cognitive task which were not accessible previously. The approach worked by constructing high-dimensional state spaces from delays of the original single-unit firing rate variables and the interactions among them, which were then statistically analyzed using kernel methods. We observed cognitive-epoch-specific neural ensemble states in ACC which were stable across many trials (in the sense of being predictive) and depended on behavioral performance. More interestingly, attracting properties of these cognitively defined ensemble states became apparent in high-dimensional expansions of the MSUA spaces due to a proper unfolding of the neural activity flow, with properties common across different animals. These results therefore suggest that ACC networks may process different subcomponents of higher cognitive tasks by transiting among different attracting states. Author Summary: For understanding how neural processes give rise to cognitive operations, it is essential to understand how aspects of the underlying neural network dynamics reconstructed from neurophysiological measurements relate to behavior. For instance, different actions may be represented by neural states characterized by stable population patterns to which activity converges in time, called attractors in the language of dynamical systems. However, experimental demonstrations of neural attractors associated with cognitive entities have been rare so far. One problem may have been that in behaving animals, in-vivo one can access only a relatively small fraction of the total number of neural units comprising the whole system, even with modern multiple single-unit (MSU) recording techniques. Therefore, the neural activity dynamics are necessarily projected from a very high-dimensional into the empirically accessible much lower-dimensional space in which attractor properties may be lost due to ambiguities and entanglement in the flow of trajectories. In the present study, principles from nonlinear time series analysis and statistical learning are applied to MSU recordings from the rat's prefrontal cortex during decision-making tasks. By expanding the empirically accessed neural state space (semi-) attracting properties of neural states corresponding to cognitively defined task-epochs became apparent, in line with many neuro-computational theories.