Gain neuromodulation of energy landscape topography mediates perceptual switches
Poster No:
1396
Submission Type:
Abstract Submission
Authors:
Christopher Whyte1, Gabriel Wainstein1, Eli Muller1, Brandon Munn1, James Shine1
Institutions:
1University of Sydney, Camperdown, NSW
First Author:
Co-Author(s):
Introduction:
Perceptual updating has been hypothesized to rely on a network reset event modulated by bursts of ascending neuromodulatory neurotransmitters, such as noradrenaline, abruptly altering the brain's susceptibility to changing sensory activity (Bouret & Sara, 2005; Sales et al., 2019). To test this hypothesis at a large-scale, we analysed an ambiguous figures task using pupillometry, functional magnetic resonance imaging (fMRI), and neurobiologically constrained recurrent neural network modelling.
Methods:
To assess the role of the ascending arousal activity during task performance, we analysed a dataset of 35 participants who performed an ambiguous figures (Fig 1A) task whilst simultaneously recording pupil diameter (a non-invasive readout of phasic noradrenergic tone Fig 1B-D; Einhäuser et al., 2008). To explore this hypothesis computationally, we trained a neurobiologically constrained recurrent neural network (RNN) on an analogous perceptual categorisation task, allowing gain to change dynamically with classification uncertainty (Fig 1E-F). We then leveraged a novel statistical physics (Munn et al., 2021) based measure of the energy landscape traversed by the RNN dynamics to derive a set of predictions that could then be tested in fMRI data recorded whilst participants performed the same task (Fig 2A-F).
Results:
Behaviourally, qualitative shifts in the perceptual interpretation of an ambiguous image were associated with peaks in pupil diameter, an indirect readout of phasic bursts in neuromodulatory tone (Fig 1C-D). In the RNN, higher gain (our model-based construct representing neuromodulatory tone) accelerated perceptual switching by transiently destabilizing the network's dynamical regime in periods of maximal uncertainty (Fig 1E-F). We leveraged a low-dimensional readout of the RNN dynamics, to develop two novel macroscale predictions: perceptual switches should occur with peaks in low-dimensional brain state velocity (Fig 2B) and with flattened energy landscape (Fig 2C-D) akin to the transient application of an external "force" injecting kinetic energy into the system. Using fMRI we confirmed these predictions (Fig 2E-G), highlighting the role of the neuromodulatory system in the large-scale network reconfigurations mediating adaptive perceptual updates.
Conclusions:
We provide computational and empirical evidence for the association between neuromodulation, pupil dilation, and energy landscape flattening in task-relevant perceptual switches. Our results strengthen our understanding of the neurobiological processes underpinning moment-by-moment adaptive changes to perception and suggest that the widespread excitatory projections of the ascending arousal system may mediate the systems-level reconfigurations of cortical network architecture via uncertainty driven alterations in neural gain.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 1
Perception, Attention and Motor Behavior:
Perception: Visual
Physiology, Metabolism and Neurotransmission:
Pharmacology and Neurotransmission 2
Keywords:
Computational Neuroscience
Noradrenaline
Perception
1|2Indicates the priority used for review
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Provide references using APA citation style.
Einhäuser, W., Stout, J., Koch, C., & Carter, O. (2008). Pupil dilation reflects perceptual selection and predicts subsequent stability in perceptual rivalry. Proceedings of the National Academy of Sciences, 105(5), 1704–1709. https://doi.org/10.1073/pnas.0707727105
Munn, B. R., Müller, E. J., Wainstein, G., & Shine, J. M. (2021). The ascending arousal system shapes neural dynamics to mediate awareness of cognitive states. Nature Communications, 12(1), 6016. https://doi.org/10.1038/s41467-021-26268-x
Sales, A. C., Friston, K. J., Jones, M. W., Pickering, A. E., & Moran, R. J. (2019). Locus Coeruleus tracking of prediction errors optimises cognitive flexibility: An Active Inference model. PLOS Computational Biology, 15(1), e1006267. https://doi.org/10.1371/journal.pcbi.1006267