Abstract
This paper assumes that cortical circuits have evolved to enable inference about the causes of sensory input received by the brain. This provides a principled specification of what neural circuits have to achieve. Here, we attempt to address how the brain makes inferences by casting inference as an optimisation problem. We look at how the ensuing recognition dynamics could be supported by directed connections and message-passing among neuronal populations, given our knowledge of intrinsic and extrinsic neuronal connections. We assume that the brain models the world as a dynamic system, which imposes causal structure on the sensorium. Perception is equated with the optimisation or inversion of this internal model, to explain sensory input. Given a model of how sensory data are generated, we use a generic variational approach to model inversion to furnish equations that prescribe recognition; i.e., the dynamics of neuronal activity that represents the causes of sensory input. Here, we focus on a model whose hierarchical and dynamical structure enables simulated brains to recognise and predict sequences of sensory states. We first review these models and their inversion under a variational free-energy formulation. We then show that the brain has the necessary infrastructure to implement this inversion and present stimulations using synthetic birds that generate and recognise birdsongs.
Highlights
This paper looks at the functional architecture of cortical circuits from the point of view of perception; namely, the fitting or inversion of internal models of sensory data by the brain
We introduce a free-energy formulation of model inversion or perception, which is applied to a specific class of models that we assume the brain uses — hierarchical dynamic models
The example we use is birdsong and the empirical measures we focus on are local field potentials (LFP) or evoked (ERP) responses that can be recorded non-invasively
Summary
This paper looks at the functional architecture of cortical circuits from the point of view of perception; namely, the fitting or inversion of internal models of sensory data by the brain. The basic idea that the brain uses hierarchical inference has been described in a series of papers (Friston, 2005; Friston, Kilner, & Harrison, 2006; Mumford, 1992; Rao & Ballard, 1998). These papers suggest that the brain uses empirical Bayes for inference about its sensory input, given the hierarchical organisation of cortical systems. An important aspect of these models is their formulation in generalised coordinates of motion This lends them a hierarchical form in both structure
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