Dynamics of a mutual inhibition between pyramidal neurons compared to human perceptual competition

Neural competition plays an essential role in active selection processes of noisy and ambiguous input signals and it is assumed to underlie emergent properties of brain functioning such as perceptual organization and decision making. Despite ample theoretical research on neural competition, experimental tools to allow neurophysiological investigation of competing neurons have not been available. We developed a “hybrid” system where real-life neurons and a computer-simulated neural circuit interacted. It enabled us to construct a mutual inhibition circuit between two real life pyramidal neurons. We then asked what dynamics this minimal unit of neural competition exhibits and compared them to the known behavioral-level dynamics of neural competition. We found that the pair of neurons shows bi-stability when activated simultaneously by current injections. The addition of modelled noise and changes in the activation strength showed that the dynamics of the circuit are strikingly similar to the known properties of bi-stable visual perception.

How arousal influences neural competition: What dual competition does not explain

We argue that although the “dual competition” model is useful when considering interactions between emotional and neutral stimuli, it fails to account for the influence of emotional arousal on perceptual or goal-directed behavior involving neutral stimuli. We present the “arousal-biased competition” framework as an alternative that accounts for both scenarios.

Local resource depletion hypothesis as a mechanism for action selection in the brain

As a comment on Kurzban et al.'s opportunity cost model, we propose an alternative view of mental effort and the action selection mechanism in the brain. Our hypothesis utilizes local resource depletion within neuronal networks, which justifies from a neurophysiological perspective why mental fatigue diminishes after switching to a novel task and explains action selection by means of neural competition theory.