Abstract
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.
Highlights
To fully understand how neural processes give rise to cognitive operations, it is essential to reconstruct the underlying neural network dynamics from electrophysiological or neuroimaging measurements in relation to behavior
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
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
Summary
To fully understand how neural processes give rise to cognitive operations, it is essential to reconstruct the underlying neural network dynamics from electrophysiological or neuroimaging measurements in relation to behavior. Several experimental studies suggested that spatial representations in the rodent hippocampus [6,20,21,22] or olfactory representations in zebrafish [16,23] may have attractor-like properties with sometimes stochastic transitions among them [24,25] In these studies, attractor states were indicated by discrete switches in the population activity patterns eventually attained (after some transient) when stimulus parameters were continuously varied. Attractor states were indicated by discrete switches in the population activity patterns eventually attained (after some transient) when stimulus parameters were continuously varied Speaking, these studies did not attempt to explicitly demonstrate a convergent flow of neural trajectories (as sometimes pointed out by the authors themselves, [23]), as another important signature of attracting states. Experimental evidence for the hypothesis that higher cognitive processes proceed by moving among attracting states is still sparse
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