Abstract
The agranular architecture of motor cortex lacks a functional interpretation. Here, we consider a 'predictive coding' account of this unique feature based on asymmetries in hierarchical cortical connections. In sensory cortex, layer 4 (the granular layer) is the target of ascending pathways. We theorise that the operation of predictive coding in the motor system (a process termed 'active inference') provides a principled rationale for the apparent recession of the ascending pathway in motor cortex. The extension of this theory to interlaminar circuitry also accounts for a sub-class of 'mirror neuron' in motor cortex--whose activity is suppressed when observing an action--explaining how predictive coding can gate hierarchical processing to switch between perception and action.
Highlights
Cortical architecture and hierarchical connectivity Motor cortex was localised to the precentral gyrus of apes by Sherrington in 1901 [1], and was first identified histologically the following year by Campbell, using the brains of Sherrington’s subjects [2]
There is a consistent asymmetry between forward connections from sensory to motor areas and the reverse backward connections [11,12,13], but the reciprocal connections among motor areas are of a distinct nature: there is a backward pattern of termination for projections from premotor areas to M1 [10], yet the reverse connections (e.g., M1 to SMA, supplementary motor area) are columnar [11,12,13], of the sort normally associated with lateral connections
This reflects the fact that descending axons typically innervate several areas [6,50,51]. Because these projections arise from pyramidal neurons in the higher area, and are excitatory, there has to be an intervening inhibitory neuron somewhere in the circuit. We have shown this pathway to operate through descending input to layer 6 pyramidal neurons, which transmit to superficial error units via layer 3 interneurons
Summary
For example: a high-level face area encodes view-invariant face identity, whereas lower levels are view specific but less identity specific [23]. The high-level face area provides the highest stamp of recognition, guiding and contextualising inference about physical attributes in lower-level areas. The model generates predictions of sensory input from highlevel representations of causes; the expectations at any given level predict the expectations at the level below. Causes are invariant aspects of the world that create regularities in sensory data, such as objects in the visual scene Their correspondence to elements of the scene is concrete at lower levels (e.g., a colour), and increasingly abstract at higher levels of the hierarchy (e.g., a smile). Expectation units encode the expected causes and states describing events (scenarios) in the environment, whereas error units report inconsistencies between expectations at different levels or,
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