Tracing the Flow of Perceptual Features in an Algorithmic Brain Network

Robin A A Ince,Guillaume A Rousselet,Gregor Thut,Stefano Panzeri,Nicola J Van Rijsbergen,Philippe G Schyns,Joachim Gross

doi:10.1038/srep17681

Abstract

The model of the brain as an information processing machine is a profound hypothesis in which neuroscience, psychology and theory of computation are now deeply rooted. Modern neuroscience aims to model the brain as a network of densely interconnected functional nodes. However, to model the dynamic information processing mechanisms of perception and cognition, it is imperative to understand brain networks at an algorithmic level–i.e. as the information flow that network nodes code and communicate. Here, using innovative methods (Directed Feature Information), we reconstructed examples of possible algorithmic brain networks that code and communicate the specific features underlying two distinct perceptions of the same ambiguous picture. In each observer, we identified a network architecture comprising one occipito-temporal hub where the features underlying both perceptual decisions dynamically converge. Our focus on detailed information flow represents an important step towards a new brain algorithmics to model the mechanisms of perception and cognition.

Highlights

Since the seminal work of Turing[1], most influential models in cognitive neuroscience are implicitly cast in terms of information processing[2] in a densely connected, hierarchically organized network[3] with bottom-up and top-down flows of information[4,5]
To build information processing models of perception and cognition, we require a level of functional understanding with at least this level of specificity: “Node A codes stimulus feature F and sends it to node B.”
The Directed Feature Information (DFI) measure we develop and apply to perceptual decisions provides this level of specificity

Summary

Introduction

Since the seminal work of Turing[1], most influential models in cognitive neuroscience are implicitly cast in terms of information processing[2] in a densely connected, hierarchically organized network[3] with bottom-up and top-down flows of information[4,5]. Consider that in predictive coding, a prediction implies explicit knowledge of what specific information is propagated down the visual hierarchy[4,6]. Existing models of prediction, memory, sensory coding and decision[10,11] are all implicitly cast within an information processing framework, reverse engineering the flow of this specific information from brain data remains a considerable challenge.

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Conclusion