Functionally specific and sparse domain-based micro-networks in monkey V1 and V2

Computational components of visual predictive coding circuitry

If a full visual percept can be said to be a 'hypothesis', so too can a neural 'prediction' - although the latter addresses one particular component of image content (such as 3-dimensional organisation, the interplay between lighting and surface colour, the future trajectory of moving objects, and so on). And, because processing is hierarchical, predictions generated at one level are conveyed in a backward direction to a lower level, seeking to predict, in fact, the neural activity at that prior stage of processing, and learning from errors signalled in the opposite direction. This is the essence of 'predictive coding', at once an algorithm for information processing and a theoretical basis for the nature of operations performed by the cerebral cortex. Neural models for the implementation of predictive coding invoke specific functional classes of neuron for generating, transmitting and receiving predictions, and for producing reciprocal error signals. Also a third general class, 'precision' neurons, tasked with regulating the magnitude of error signals contingent upon the confidence placed upon the prediction, i.e., the reliability and behavioural utility of the sensory data that it predicts. So, what is the ultimate source of a 'prediction'? The answer is multifactorial: knowledge of the current environmental context and the immediate past, allied to memory and lifetime experience of the way of the world, doubtless fine-tuned by evolutionary history too. There are, in consequence, numerous potential avenues for experimenters seeking to manipulate subjects' expectation, and examine the neural signals elicited by surprising, and less surprising visual stimuli. This review focuses upon the predictive physiology of mouse and monkey visual cortex, summarising and commenting on evidence to date, and placing it in the context of the broader field. It is concluded that predictive coding has a firm grounding in basic neuroscience and that, unsurprisingly, there remains much to learn.

Mesoscale functional connectivity in macaque visual areas

Studies of resting-state functional connectivity (rsFC) have provided rich insights into the structures and functions of the human brain. However, most rsFC studies have focused on large-scale brain connectivity. To explore rsFC at a finer scale, we used intrinsic signal optical imaging to image the ongoing activity of the anesthetized macaque visual cortex. Differential signals from functional domains were used to quantify network-specific fluctuations. In 30-60 min resting-state imaging, a series of coherent activation patterns were observed in all three visual areas we examined (V1, V2, and V4). These patterns matched the known functional maps (ocular dominance, orientation, color) obtained in visual stimulation conditions. These functional connectivity (FC) networks fluctuated independently over time and exhibited similar temporal characteristics. Coherent fluctuations, however, were observed from orientation FC networks in different areas and even across two hemispheres. Thus, FC in the macaque visual cortex was fully mapped both on a fine scale and over a long range. Hemodynamic signals can be used to explore mesoscale rsFC in a submillimeter resolution.