P171 Partial Information Decomposition of amplitude and phase stimulus encoding in oscillator models
V. LIMA¹*, D. MARINAZZO², A. BROVELLI¹,
1. Institut de neurosciences de la Timone, Aix Marseille Université, UMR 7289 CNRS, 13005, Marseille, France.
2. Faculty of Psychology and Educational Sciences, Department of Data Analysis, University of Ghent, Ghent, Belgium
* vinicius.lima.cordeiro@gmail.com
Introduction
Synchrony between oscillatory activity is thought to be the primary mechanism enabling widespread cortical regions to route information [1]. Such a mechanism would require either oscillations between a target and a sending area to increase their amplitude while maintaining a stable phase relationship or to shift their phase difference when a stimulus is presented [2]. Nonetheless, whether the “communication” established between the pair of areas can be used to encode stimulus-specific information remains unclear.
Methods
To address this question, we construct a whole-brain model in which nodes are connected using macaque structural connectivity [3], and their dynamics are governed by the Stuart-Landau (SL) model [4]. The SL model describes nonlinear oscillators near a Hopf bifurcation and models both the evolution of their amplitude and phase terms. In addition to enabling the characterization of interactions in terms of phase and/or amplitude, the distance to the Hopf bifurcation is controlled by a single parameter,a, which determines the stability of the oscillations:a< 0 leads to transient oscillations, whereasa≥ 0 results in stable oscillations, allowing to explore the role of both types of activity in stimulus encoding [5].To disentangle phase and amplitude encoding in the model, we use the framework of partial information decomposition (PID) [6] to estimate the information that the phase and amplitude components of simulated neuronal activity uniquely carry about the stimulus. Briefly, for two nodes indexed byjandk, we consider the product of their amplitude termsAjk, phase differencejk, and stimulusS. The three-variable PID allows us to decompose their total mutual informationI(S;Ajk,jk)into terms representing how they encode the stimulus redundantly or synergistically, as well as the unique information contained in the amplitude and phase interactions. Additionally, this framework could be extended to study non-dyadic interactions by operating at edge rather than node level [7]. In this case, PID decomposition is performed between two edge time series, each given byEjk=Ajkejk, allowing to either decompose the following mutual information terms:I(S;Ajk,Aml),I(S;Ajk,ml), andI(S;jk,ml)or performing multivariate PID.
Results
In the whole-brain model, we found that even though the stimulus is generally encoded by the signals’ amplitude, in areas that are hierarchically far apart, the initial encoding in amplitude is later found in the phase relation between the two areas in a weaker but more persistent form. These effects highly depend on the nodes’ dynamics and are most favorable when they exhibit transient oscillations (a < 0).
Discussion
Introducing a scaling of the natural oscillation frequency also appeared to enhance the effect, suggesting that different time scales across the cortex may promote the establishment of functional coupling through phase synchrony [8].
Acknowledgements
None
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