The neocortical circuit: themes and variations

By Kenneth D. Harris, Gordon M. G. Shepherd. Nature Neuroscience, 2015

1,119 citations recorded by the source corpus.

Similarities in neocortical circuit organization across areas and species suggest a common strategy to process diverse types of information, including sensation from diverse modalities, motor control and higher cognitive processes. Cortical neurons belong to a small number of main classes. The properties of these classes, including their local and long-range connectivity, developmental history, gene expression, intrinsic physiology and in vivo activity patterns, are remarkably similar across areas. Each class contains subclasses; for a rapidly growing number of these, conserved patterns of input and output connections are also becoming evident. The ensemble of circuit connections constitutes a basic circuit pattern that appears to be repeated across neocortical areas, with area- and species-specific modifications. Such 'serially homologous' organization may adapt individual neocortical regions to the type of information each must process.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/d841aeacde142f3ad8ff0c3d0a13b1394f943812

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Citations in the corpus

Dopamine and Cognitive Control in Prefrontal Cortex. (2019, background): “…frontal eye ﬁ eld of monkey PFC, visual signals in V4 show higher amplitude, reduced variability, and increased between-neuron correlation [87,88] – all characteristics of a in layer V neurons [26] which receive cortical input from super ﬁ cial layers and project to downstream target areas [163].”
A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation (2020, neutral): “Glutamatergic excitatory neuronal types are organized by layers and their long-range projection patterns (Harris and Shepherd, 2015), whereas GABAergic inhibitory interneuron types are organized by their firing characteristics and local connectivity patterns (Tremblay et al.”
Predictive Processing: A Canonical Cortical Computation (2018, positive): “Interestingly, these functional and anatomical characteristics are hallmarks of a subset of cortical neurons, prevalent in deeper layers (Harris and Mrsic-Flogel, 2013; Harris and Shepherd, 2015).”
Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function (2016, positive): “In conclusion, this study suggests that neocortical microcircuits process information through a stereotypical progression of clique and cavity formation and disintegration, consistent with a recent hypothesis of common strategies for information processing across the neocortex (Harris and Shepherd, ”
Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex (2020, methodology): “…cerebral cortex where cells residing within different cortical layers show distinct gene expression patterns and exhibit differing patterns of morphology, physiology, and connectivity (DeFelipe and Fariñas, 1992; Harris and Shepherd, 2015; Narayanan et al., 2017; Radnikow and Feldmeyer, 2018).”
A critique of pure learning and what artificial neural networks can learn from animal brains (2019, background): “There has long been speculation that the neocortex consists of many copies of a basic “ canonical ” microcircuit 34,35 , which are wired together to form the entire cortex.”
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review (2017, background): “Moreover, with regard to symmetry breaking, they also observed that differentiated classes of neurons, perhaps like those used by the human brain (Harris & Shepherd, 2015), are superior to undifferentiated neurons commonly used by artificial networks.”