The resurrection of Tweedledum and Tweedledee: Bimodality cannot distinguish serial and parallel processes

Magnetoencephalography decoding reveals structural differences within integrative decision processes

When confronted with complex inputs consisting of multiple elements, humans use various strategies to integrate the elements quickly and accurately. For instance, accuracy may be improved by processing elements one at a time^1-4 or over extended periods^5-8; speed can increase if the internal representation of elements is accelerated^9,10. However, little is known about how humans actually approach these challenges because behavioural findings can be accounted for by multiple alternative process models¹¹ and neuroimaging investigations typically rely on haemodynamic signals that change too slowly. Consequently, to uncover the fast neural dynamics that support information integration, we decoded magnetoencephalographic signals that were recorded as human subjects performed a complex decision task. Our findings reveal three sources of individual differences in the temporal structure of the integration process-sequential representation, partial reinstatement and early computation-each having a dissociable effect on how subjects handled problem complexity and temporal constraints. Our findings shed new light on the structure and influence of self-determined neural integration processes.

Theta-Gamma Coding Meets Communication-through-Coherence: Neuronal Oscillatory Multiplexing Theories Reconciled

Several theories have been advanced to explain how cross-frequency coupling, the interaction of neuronal oscillations at different frequencies, could enable item multiplexing in neural systems. The communication-through-coherence theory proposes that phase-matching of gamma oscillations between areas enables selective processing of a single item at a time, and a later refinement of the theory includes a theta-frequency oscillation that provides a periodic reset of the system. Alternatively, the theta-gamma neural code theory proposes that a sequence of items is processed, one per gamma cycle, and that this sequence is repeated or updated across theta cycles. In short, both theories serve to segregate representations via the temporal domain, but differ on the number of objects concurrently represented. In this study, we set out to test whether each of these theories is actually physiologically plausible, by implementing them within a single model inspired by physiological data. Using a spiking network model of visual processing, we show that each of these theories is physiologically plausible and computationally useful. Both theories were implemented within a single network architecture, with two areas connected in a feedforward manner, and gamma oscillations generated by feedback inhibition within areas. Simply increasing the amplitude of global inhibition in the lower area, equivalent to an increase in the spatial scope of the gamma oscillation, yielded a switch from one mode to the other. Thus, these different processing modes may co-exist in the brain, enabling dynamic switching between exploratory and selective modes of attention.

There is a growing consensus that neuronal oscillations constitute a fundamental computational mechanism in the brain. Beyond this, recent experimental evidence has highlighted interactions between oscillations at high and low frequencies (e.g. gamma oscillations, 40–80 Hz, are modulated by theta oscillations, 4–10 Hz), and two major theories have developed regarding the functional role of this kind of cross-frequency coupling. Here, we present a computational modelling study of these theories with strong implications for biological studies. Firstly, we demonstrate for the first time that each of these theories is physiologically plausible, in that they can be implemented in a spiking network model with parameters guided by experimental data. Secondly, we show that they are each computationally useful, able to overcome a feature-binding ambiguity in a presented stimulus. Finally, we implement both theories within a single network model, and find that only a single parameter change is required to switch between the two processing states. This leads to the exciting new proposal that both theories may be correct, both implemented in the brain, with dynamic switching between modes according to processing and attentional requirements.