Neocortex: a lean mean memory storage machine

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Abstract

<p class="first" id="P1">Connectivity patterns of neocortex exhibit several odd properties: for example, most neighboring excitatory neurons do not connect, which seems curiously wasteful. Brunel’s elegant theoretical treatment reveals how optimal information storage can naturally impose these peculiar properties. </p>

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Most cited references 11

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Principles of connectivity among morphologically defined cell types in adult neocortex.

Xiaolong Jiang, Shan-Shan Shen, Cathryn R Cadwell … (2015)

Since the work of Ramón y Cajal in the late 19th and early 20th centuries, neuroscientists have speculated that a complete understanding of neuronal cell types and their connections is key to explaining complex brain functions. However, a complete census of the constituent cell types and their wiring diagram in mature neocortex remains elusive. By combining octuple whole-cell recordings with an optimized avidin-biotin-peroxidase staining technique, we carried out a morphological and electrophysiological census of neuronal types in layers 1, 2/3, and 5 of mature neocortex and mapped the connectivity between more than 11,000 pairs of identified neurons. We categorized 15 types of interneurons, and each exhibited a characteristic pattern of connectivity with other interneuron types and pyramidal cells. The essential connectivity structure of the neocortical microcircuit could be captured by only a few connectivity motifs.

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The excitatory neuronal network of the C2 barrel column in mouse primary somatosensory cortex.

Sandrine Lefort, Christian Tomm, J-C Floyd Sarria … (2009)

Local microcircuits within neocortical columns form key determinants of sensory processing. Here, we investigate the excitatory synaptic neuronal network of an anatomically defined cortical column, the C2 barrel column of mouse primary somatosensory cortex. This cortical column is known to process tactile information related to the C2 whisker. Through multiple simultaneous whole-cell recordings, we quantify connectivity maps between individual excitatory neurons located across all cortical layers of the C2 barrel column. Synaptic connectivity depended strongly upon somatic laminar location of both presynaptic and postsynaptic neurons, providing definitive evidence for layer-specific signaling pathways. The strongest excitatory influence upon the cortical column was provided by presynaptic layer 4 neurons. In all layers we found rare large-amplitude synaptic connections, which are likely to contribute strongly to reliable information processing. Our data set provides the first functional description of the excitatory synaptic wiring diagram of a physiologically relevant and anatomically well-defined cortical column at single-cell resolution.

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Connectivity reflects coding: a model of voltage-based STDP with homeostasis.

Claudia Clopath, Lars Büsing, Eleni Vasilaki … (2010)

Electrophysiological connectivity patterns in cortex often have a few strong connections, which are sometimes bidirectional, among a lot of weak connections. To explain these connectivity patterns, we created a model of spike timing-dependent plasticity (STDP) in which synaptic changes depend on presynaptic spike arrival and the postsynaptic membrane potential, filtered with two different time constants. Our model describes several nonlinear effects that are observed in STDP experiments, as well as the voltage dependence of plasticity. We found that, in a simulated recurrent network of spiking neurons, our plasticity rule led not only to development of localized receptive fields but also to connectivity patterns that reflect the neural code. For temporal coding procedures with spatio-temporal input correlations, strong connections were predominantly unidirectional, whereas they were bidirectional under rate-coded input with spatial correlations only. Thus, variable connectivity patterns in the brain could reflect different coding principles across brain areas; moreover, our simulations suggested that plasticity is fast.