Cerebral hierarchies: predictive processing, precision and the pulvinar

By Ryota Kanai, Y. Komura, S. Shipp, Karl J. Friston. Philosophical Transactions of the Royal Society B: Biological Sciences, 2015

416 citations recorded by the source corpus.

This paper considers neuronal architectures from a computational perspective and asks what aspects of neuroanatomy and neurophysiology can be disclosed by the nature of neuronal computations? In particular, we extend current formulations of the brain as an organ of inference—based upon hierarchical predictive coding—and consider how these inferences are orchestrated. In other words, what would the brain require to dynamically coordinate and contextualize its message passing to optimize its computational goals? The answer that emerges rests on the delicate (modulatory) gain control of neuronal populations that select and coordinate (prediction error) signals that ascend cortical hierarchies. This is important because it speaks to a hierarchical anatomy of extrinsic (between region) connections that form two distinct classes, namely a class of driving (first-order) connections that are concerned with encoding the content of neuronal representations and a class of modulatory (second-order) connections that establish context—in the form of the salience or precision ascribed to content. We explore the implications of this distinction from a formal perspective (using simulations of feature–ground segregation) and consider the neurobiological substrates of the ensuing precision-engineered dynamics, with a special focus on the pulvinar and attention.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/8e114ccd0c3f44c4933ee03c656abad40e35e43e

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In Cognitive Neuroscience

Citations in the corpus

Self-Evaluation of Decision-Making: A General Bayesian Framework for Metacognitive Computation (2017, methodology): “This is potentially consistent with a confidence variable being encoded in cortico-thalamic loops (Kanai, Komura, Shipp, & Friston, 2015), and similar findings have been obtained through OFC inactivation in rodents (Lak et al., 2014).”
The graphical brain: Belief propagation and active inference (2017, neutral): “…attenuation and attention (Auksztulewicz & Friston, 2015; Bauer, Stenner, Friston, & Dolan, 2014; Brown, Adams, Parees, Edwards, & Friston, 2013; Kanai, Komura, Shipp, & Friston, 2015; Pinotsis et al., 2014); namely, a precision engineered gain control of prediction error units (i.e.,…”
From cognitivism to autopoiesis: towards a computational framework for the embodied mind (2016, background): “,) dopamine, norepinephrine, and other neurotransmitters are argued to communicate the fidelity or precision of beliefs (Feldman and Friston 2010; Friston et al. 2014a, b, 2012a, b; Kanai et al. 2015; Moran et al. 2013), individually these systems can be more or less specialized for a colloquial rol”
Neural Elements for Predictive Coding (2016, methodology): “…coordinate the gain of ascending pathways through direct effects upon superficial error units, or by regulation of effective connectivity through the induction of coherence in transareal oscillatory relationships (Shipp, 2003; Purushothaman et al., 2012; Saalmann et al., 2012; Kanai et al., 2015).”
Hierarchical Active Inference: A Theory of Motivated Control (2018, background): “Formally, we appeal to exactly the same (precision-based) mechanisms that are thought to underlie attention and figure-ground segregation [52,64,65].”