The highly regular grid structure of initial connections in the neocortex found in a National Institutes of Health study.
Another view of the regular grid structure of neocortical connections.
The grid structure found in the neocortex is remarkably similar to what is called crossbar switching, which is used in integrated circuits and circuit boards.
Our understanding of the lower hierarchical levels of the visual neocortex is consistent with the PRTM I described in the previous chapter, and observation of the hierarchical nature of neocortical processing has recently extended far beyond these levels. University of Texas neurobiology professor Daniel J. Felleman and his colleagues traced the “hierarchical organization of the cerebral cortex…[in] 25 neocortical areas,” which included both visual areas and higher-level areas that combine patterns from multiple senses. What they found as they went up the neocortical hierarchy was that the processing of patterns became more abstract, comprised larger spatial areas, and involved longer time periods. With every connection they found communication both up and down the hierarchy.8
Recent research allows us to substantially broaden these observations to regions well beyond the visual cortex and even to the association areas, which combine inputs from multiple senses. A study published in 2008 by Princeton psychology professor Uri Hasson and his colleagues demonstrates that the phenomena observed in the visual cortex occur across a wide variety of neocortical areas: “It is well established that neurons along the visual cortical pathways have increasingly larger spatial receptive fields. This is a basic organizing principle of the visual system…. Real-world events occur not only over extended regions of space, but also over extended periods of time. We therefore hypothesized that a hierarchy analogous to that found for spatial receptive field sizes should also exist for the temporal response characteristics of different brain regions.” This is exactly what they found, which enabled them to conclude that “similar to the known cortical hierarchy of spatial receptive fields, there is a hierarchy of progressively longer temporal receptive windows in the human brain.”9
The most powerful argument for the universality of processing in the neocortex is the pervasive evidence of plasticity (not just learning but interchangeability): In other words, one region is able to do the work of other regions, implying a common algorithm across the entire neocortex. A great deal of neuroscience research has been focused on identifying which regions of the neocortex are responsible for which types of patterns. The classical technique for determining this has been to take advantage of brain damage from injury or stroke and to correlate lost functionality with specific damaged regions. So, for example, when we notice that someone with newly acquired damage to the fusiform gyrus region suddenly has difficulty recognizing faces but is still able to identify people from their voices and language patterns, we can hypothesize that this region has something to do with face recognition. The underlying assumption has been that each of these regions is designed to recognize and process a particular type of pattern. Particular physical regions have become associated with particular types of patterns, because under normal circumstances that is how the information happens to flow. But when that normal flow of information is disrupted for any reason, another region of the neocortex is able to step in and take over.
Plasticity has been widely noted by neurologists, who observed that patients with brain damage from an injury or a stroke can relearn the same skills in another area of the neocortex. Perhaps the most dramatic example of plasticity is a 2011 study by American neuroscientist Marina Bedny and her colleagues on what happens to the visual cortex of congenitally blind people. The common wisdom has been that the early layers of the visual cortex, such as V1 and V2, inherently deal with very low-level patterns (such as edges and curves), whereas the frontal cortex (that evolutionarily new region of the cortex that we have in our uniquely large foreheads) inherently deals with the far more complex and subtle patterns of language and other abstract concepts. But as Bedny and her colleagues found, “Humans are thought to have evolved brain regions in the left frontal and temporal cortex that are uniquely capable of language processing. However, congenitally blind individuals also activate the visual cortex in some verbal tasks. We provide evidence that this visual cortex activity in fact reflects language processing. We find that in congenitally blind individuals, the left visual cortex behaves similarly to classic language regions…. We conclude that brain regions that are thought to have evolved for vision can take on language processing as a result of early experience.”10
Consider the implications of this study: It means that neocortical regions that are physically relatively far apart, and that have also been considered conceptually very different (primitive visual cues versus abstract language concepts), use essentially the same algorithm. The regions that process these disparate types of patterns can substitute for one another.
University of California at Berkeley neuroscientist Daniel E. Feldman wrote a comprehensive 2009 review of what he called “synaptic mechanisms for plasticity in the neocortex” and found evidence for this type of plasticity across the neocortex. He writes that “plasticity allows the brain to learn and remember patterns in the sensory world, to refine movements…and to recover function after injury.” He adds that this plasticity is enabled by “structural changes including formation, removal, and morphological remodeling of cortical synapses and dendritic spines.”11
Another startling example of neocortical plasticity (and therefore of the uniformity of the neocortical algorithm) was recently demonstrated by scientists at the University of California at Berkeley. They hooked up implanted microelectrode arrays to pick up brain signals specifically from a region of the motor cortex of mice that controls the movement of their whiskers. They set up their experiment so that the mice would get a reward if they controlled these neurons to fire in a certain mental pattern but not to actually move their whiskers. The pattern required to get the reward involved a mental task that their frontal neurons would normally not do. The mice were nonetheless able to perform this mental feat essentially by thinking with their motor neurons while mentally decoupling them from controlling motor movements.12 The conclusion is that the motor cortex, the region of the neocortex responsible for coordinating muscle movement, also uses the standard neocortical algorithm.