Neural Elements for Predictive Coding

By S. Shipp. Frontiers in Psychology, 2016

306 citations recorded by the source corpus.

Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backward in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many ‘illusory’ instances of perception where what is seen (heard, etc.) is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forward and backward pathways should be completely separate, given their functional distinction; this aspect of circuitry – that neurons with extrinsically bifurcating axons do not project in both directions – has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy) formulation of predictive coding is combined with the classic ‘canonical microcircuit’ and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a) updates in the microcircuitry of primate visual cortex, and (b) rapid technical advances made possible by transgenic neural engineering in the mouse. The exercise highlights a number of recurring themes, amongst them the consideration of interneuron diversity as a spur to theoretical development and the potential for specifying a pyramidal neuron’s function by its individual ‘connectome,’ combining its extrinsic projection (forward, backward or subcortical) with evaluation of its intrinsic network (e.g., unidirectional versus bidirectional connections with other pyramidal neurons).

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/927c9e05ba5d63692864b0aec3e9b65a6e920363

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction (2018, methodology): “…theory was also expected to be a theoretical mechanism to generate illusions (Notredame et al., 2014; Nour and Nour, 2015; Raman and Sarkar, 2016; Shipp, 2016) and was used as an explanation for certain motion illusions (Watanabe et al., 2010), of bi-stable perception (Weilnhammer et al., 2017)…”
The Predictive Coding Account of Psychosis (2018, background): “In terms of neural implementation (14,15), predictive signals may be sent from higher hierarchical levels predominantly via glutamatergic N-methyl-D-aspartate receptor (NMDAR) signaling; any disparity between prior belief and sensory data is then signaled as a PE to the higher levels, mostly via glu”
The graphical brain: Belief propagation and active inference (2017, background): “Fuller accounts of the anatomical and neurophysiological evidence—upon which these arguments are based—can be found in Adam, Shipp, and Friston (2013), Bastos et al. (2012), Friston (2008), Mumford (1992), Shipp (2005, 2016), and Shipp, Adam, and Friston (2013).”