Binding by Random Bursts: A Computational Model of Cognitive Control

Verguts, Tom · 2017 · OpenAlex-citations

DOI: 10.1162/jocn_a_01117

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Summary

This paper presents a computational model of cognitive control titled "Binding by Random Bursts," which addresses the mechanistic challenge of how the brain flexibly distinguishes between task-relevant and task-irrelevant processing pathways. The author argues that existing rate-code models fail to account for ubiquitous oscillatory brain signatures, such as theta–gamma coupling. To resolve this, the model proposes that cognitive control emerges from the interaction of rate codes, phase codes, and synaptic connections, specifically utilizing noise-induced synchronization and communication through coherence. The model consists of three modules: a processing module (input and response areas), an integrator module, and a control module comprising the medial frontal cortex (MFC) and lateral frontal cortex (LFC). The MFC generates theta-frequency oscillations that send random, phase-locked bursts to cortical areas. The LFC determines which areas are eligible to receive these bursts, effectively tagging task-relevant pathways. When eligible areas receive these bursts, their gamma-frequency oscillations synchronize. This synchronization facilitates efficient communication between the areas, allowing the relevant input to reach the response area even if the irrelevant pathway has stronger synaptic weights. The model was tested using simulations of the Stroop task, manipulating proactive control (increasing MFC theta power before stimulus onset) and reactive control (increasing power in response to conflict). Results from the simulations demonstrate that increasing theta power in the MFC improves behavioral performance, characterized by higher accuracy and faster reaction times. Neurophysiologically, the model reproduces key empirical findings: increased theta power leads to increased gamma frequency power and synchrony in posterior processing areas via theta–gamma phase–amplitude coupling. Specifically, the model shows that synchronized areas (color and response) communicate efficiently, while unsynchronized areas (word input) do not, despite having stronger synaptic connections. The simulations successfully replicate the congruency effect, where incongruent stimuli result in slower responses and more errors, and show that stronger control reduces these effects. The significance of this work lies in its ability to solve a central computational problem for cognitive control: enabling rapid communication between arbitrary brain areas. By integrating rate and phase coding, the model provides a mechanistic explanation for how top-down control can dynamically bind cortical areas. It aligns closely with behavioral and neurophysiological data, offering a unified framework that accounts for both reactive and proactive control mechanisms through the modulation of theta power. This approach bridges the gap between abstract computational theories of control and the oscillatory dynamics observed in neural recordings.

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• executive function