Mapping the Matrix: The Ways of Neocortex

Institute of Neuroinformatics, UZH/ETH, Winterthurerstrasse 190, 8057 Zurich, Switzerland.
Neuron (Impact Factor: 15.05). 11/2007; 56(2):226-38. DOI: 10.1016/j.neuron.2007.10.017
Source: PubMed


While we know that the neocortex occupies 85% of our brains and that its circuits allow an enormous flexibility and repertoire of behavior (not to mention unexplained phenomena like consciousness), a century after Cajal we have very little knowledge of the details of the cortical circuits or their mode of function. One simplifying hypothesis that has existed since Cajal is that the neocortex consists of repeated copies of the same fundamental circuit. However, finding that fundamental circuit has proved elusive, although partial drafts of a "canonical circuit" appear in many different guises of structure and function. Here, we review some critical stages in the history of this quest. In doing so, we consider the style of cortical computation in relation to the neuronal machinery that supports it. We conclude that the structure and function of cortex honors two major computational principles: "just-enough" and "just-in-time."

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    • "As a second feature we considered the spatial proximity of cortical areas. In the 'distance model', the spatial separation of areas is hypothesized to account for the existence (Young, 1992; Klyachko and Stevens, 2003; Markov et al., 2013), strength (Douglas and Martin, 2007; Ercsey- Ravasz et al., 2013) as well as laminar patterns (Salin and Bullier, 1995) of corticocortical projections. According to the distance model, connections between remote areas are less frequent and sparser than connections among close areas. "
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    ABSTRACT: Anatomical connectivity imposes strong constraints on brain function, but there is no general agreement about principles that govern its organization. Based on extensive quantitative data we tested the power of three models to predict connections of the primate cerebral cortex: architectonic similarity (structural model), spatial proximity (distance model) and thickness similarity (thickness model). Architectonic similarity showed the strongest and most consistent influence on connection features. This parameter was strongly associated with the presence or absence of inter-areal connections and when integrated with spatial distance, the model allowed predicting the existence of projections with very high accuracy. Moreover, architectonic similarity was strongly related to the laminar pattern of projections origins, and the absolute number of cortical connections of an area. By contrast, cortical thickness similarity and distance were not systematically related to connection features. These findings suggest that cortical architecture provides a general organizing principle for connections in the primate brain.
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    • "Although only a few millimeters thin, the cerebral cortex is composed of microcircuits whose layered architecture plays a key role in cortical computation (Douglas and Martin, 2007; Heinzle et al., 2007; Bastos et al., 2012 ). Studying layer-specific computations noninvasively in humans would require two key ingredients: First, noninvasive high-resolution imaging to resolve cortical layers and, second, a modeling approach that explains the measured data as a function of neuronal interactions within and across layers. "
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    ABSTRACT: High-resolution blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) at the sub-millimeter scale has become feasible with recent advances in MR technology. In principle, this would enable the study of layered cortical circuits, one of the fundaments of cortical computation. However, the spatial layout of cortical blood supply may become an important confound at such high resolution. In particular, venous blood draining back to the cortical surface perpendicularly to the layered structure is expected to influence the measured responses in different layers. Here, we present an extension of a hemodynamic model commonly used for analyzing fMRI data (in dynamic causal models or biophysical network models) that accounts for such blood draining effects by coupling local hemodynamics across layers. We illustrate the properties of the model and its inversion by a series of simulations and show that it successfully captures layered fMRI data obtained during a simple visual experiment. We conclude that for future studies of the dynamics of layered neuronal circuits with high-resolution fMRI, it will be pivotal to include effects of blood draining, particularly when trying to infer on the layer-specific connections in cortex - a theme of key relevance for brain disorders like schizophrenia and for theories of brain function such as predictive coding.
    Full-text · Article · Oct 2015 · NeuroImage
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    • "These studies indicated the existence of distinct microcircuits, which may act as functional modules. Understanding how such local cortical modules operate contributes to the understanding of the computational capabilities and function of larger networks composed of such modules (Traub et al. 2005; Douglas and Martin 2007; Heinzle et al. 2007, 2010; Buonomano and Maass 2009; Litvak and Ullman 2009; Papoutsi et al. 2013). "
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    ABSTRACT: Layer 5 thick tufted pyramidal cells (TTCs) in the neocortex are particularly electrically complex, owing to their highly excitable dendrites. The interplay between dendritic nonlinearities and recurrent cortical microcircuit activity in shaping network response is largely unknown. We simulated detailed conductance-based models of TTCs forming recurrent microcircuits that were interconnected as found experimentally; the network was embedded in a realistic background synaptic activity. TTCs microcircuits significantly amplified brief thalamocortical inputs; this cortical gain was mediated by back-propagation activated N-methyl-d-aspartate depolarizations and dendritic back-propagation-activated Ca2+ spike firing, ignited by the coincidence of thalamic-activated somatic spike and local dendritic synaptic inputs, originating from the cortical microcircuit. Surprisingly, dendritic nonlinearities in TTCs microcircuits linearly multiplied thalamic inputs—amplifying them while maintaining input selectivity. Our findings indicate that dendritic nonlinearities are pivotal in controlling the gain and the computational functions of TTCs microcircuits, which serve as a dominant output source for the neocortex.
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