Reconstruction and simulation of neocortical microcircuitry

We present a first-draft digital reconstruction of the microcircuitry of somatosensory cortex of juvenile rat. The reconstruction uses cellular and synaptic organizing principles to algorithmically reconstruct detailed anatomy and physiology from sparse experimental data. An objective anatomical method defines a neocortical volume of 0.29 ± 0.01 mm3 containing ∼31,000 neurons, and patch-clamp studies identify 55 layer-specific morphological and 207 morpho-electrical neuron subtypes. When digitally reconstructed neurons are positioned in the volume and synapse formation is restricted to biological bouton densities and numbers of synapses per connection, their overlapping arbors form ∼8 million connections with ∼37 million synapses. Simulations reproduce an array of in vitro and in vivo experiments without parameter tuning. Additionally, we find a spectrum of network states with a sharp transition from synchronous to asynchronous activity, modulated by physiological mechanisms. The spectrum of network states, dynamically reconfigured around this transition, supports diverse information processing strategies

Wiring Principles of Cerebral Cortex

Cerebral cortex is probably the most complex biological network. Here many millions of individual neurons, the functional units of cortex, are interconnected through a massive yet highly organized pattern of axonal and dendritic wiring. This wiring enables both near and distant cells to coordinate their responses and generate a rich variety of cognitions and behaviours. When the wiring is damaged through disease or trauma it may reorganize but this may lead to characteristic pathological behaviours. While there have been significant advances in mapping cortical connectivity, the organizing principles and function of this connectivity are not well understood. On the one hand, there appears to be general design constraints governing cortical wiring, as first recognised by Rámon y Cajal's in his laws of conduction, material, and volume conservation. Yet on the other hand, particular patterns of cortical wiring exist to serve specific functions. There is a wide gap in understanding how the response and connectivity properties of a single neuron contribute to emergent network functions such as in detecting perceptually relevant features. Unravelling this intimate causal relationship represents one of the major challenges in neuroscience. This Research Topic will examine progress in understanding cortical wiring principles. This Research Topic aims to draw together recent advances in methods and understanding as well as recent challenges to existing ideas about how cerebral cortex is wired. This is particularly timely because new automated techniques may soon yield huge datasets in need of explanation. Recent studies have, for instance, empirically evaluated Rámon y Cajal's conservation laws for cerebral cortex, while others have shown some unexpected connectivity features that may refine the traditional view of how corticocortical connections are organised with regard to functional representations of auditory, somatosensory and visual cortices. Understanding these data will help improve the fidelity of neural models of cerebral cortical function and take into account the diversity of connections at both micro- and mesoscopic scales not seen at such a depth before

Communication and Wiring in the Cortical Connectome

In cerebral cortex, the huge mass of axonal wiring that carries information between near and distant neurons is thought to provide the neural substrate for cognitive and perceptual function. The goal of mapping the connectivity of cortical axons at different spatial scales, the cortical connectome, is to trace the paths of information flow in cerebral cortex. To appreciate the relationship between the connectome and cortical function, we need to discover the nature and purpose of the wiring principles underlying cortical connectivity. A popular explanation has been that axonal length is strictly minimized both within and between cortical regions. In contrast, we have hypothesized the existence of a multi-scale principle of cortical wiring where to optimise communication there is a trade-off between spatial (construction) and temporal (routing) costs. Here, using recent evidence concerning cortical spatial networks we critically evaluate this hypothesis at neuron, local circuit, and pathway scales. We report three main conclusions. First, the axonal and dendritic arbor morphology of single neocortical neurons may be governed by a similar wiring principle, one that balances the conservation of cellular material and conduction delay. Second, the same principle may be observed for fibre tracts connecting cortical regions. Third, the absence of sufficient local circuit data currently prohibits any meaningful assessment of the hypothesis at this scale of cortical organization. To avoid neglecting neuron and microcircuit levels of cortical organization, the connectome framework should incorporate more morphological description. In addition, structural analyses of temporal cost for cortical circuits should take account of both axonal conduction and neuronal integration delays, which appear mostly of the same order of magnitude. We conclude the hypothesized trade-off between spatial and temporal costs may potentially offer a powerful explanation for cortical wiring patterns

New insights into the classification and nomenclature of cortical GABAergic interneurons

A systematic classification and accepted nomenclature of neuron types is much needed but is currently lacking. This article describes a possible taxononnical solution for classifying GABAergic interneurons of the cerebral cortex based on a novel, web-based interactive system that allows experts to classify neurons with pre-determined criteria. Using Bayesian analysis and clustering algorithms on the resulting data, we investigated the suitability of several anatomical terms and neuron names for cortical GABAergic interneurons. Moreover, we show that supervised classification models could automatically categorize interneurons in agreement with experts' assignments. These results demonstrate a practical and objective approach to the naming, characterization and classification of neurons based on community consensus.BBP-COREBBP-GR-HILLLNM