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One Response to Write a post

  1. AnnMary Mathew says:

    Hello Neurdon — I tried sending this post via email (includes pictures), but got bounced back several times. If you are interested in publishing, please let me know and I will send over full post. For now, here is the post without pictures:

    **

    Cognitive activity arises from the firing of neurons in ways that are largely unknown.
    A system of coordination between firing neurons is needed to combine information in useful
    ways. Oscillations – periods of synchronous firing at varying frequencies – is one commonly
    proposed solution. Oscillations are indeed readily observed in the brain, with the total power of
    these oscillations distributed in a characteristic way over its range of frequencies. Using a
    simplified but biologically realistic neural network, Neymotin et. al. was able to demonstrate
    that, in the piece of neocortex simulated, the oscillations emerged largely from a specific
    layer. Further manipulations revealed a homeostatic mechanism in the model.

    The model was simplified, and was tuned only to produce realistic firing rates and
    to avoid pathological spiking activity. Remarkably, from the model’s simplified structure
    emerged a biologically realistic spectrum of rhythms. Cells fired in equal measure across the
    frequency spectrum when cells were not connected. When they were connected, frequency peaks emerged in the theta/alpha spectrum in the excitatory cells, and in the gamma spectrum among inhibitory cells. Previous researchers have suggested that gamma oscillation work with slower theta oscillations in a multiplexing mechanism that allows information to be shared between different modalities and internal sources in order to integrate them into coherent
    representations. In telecommunications, multiplexing is the mechanism by which many telephone conversations can be carried over the same telephone wire, for example. In this model, subsets of cells are fired on a particular gamma cycle superimposed on a theta/alpha cycle, the theta/alpha cycle being a physically parsable substrate for a representation composed of information from many different subsets of cells. This multiplexing model is consistent with the physical processes that came about in simulation.

    The group had hypothesized that areas of high neuronal density and connectivity might serve a control function for areas around it. In order to better visualize cortical structure, they
    graphed the structure of a section of the neocortex, taking note of neuron density of excitatory
    and inhibitory cells separately, as well as connection strength between layers and between
    columns. Using this graph-theoretic approach, cortical layer 2/3 (the neocortex has 6 layers, layer 1 being the one closest to the skull) was revealed to have the greatest cell populations and
    strongest connections. But determining whether this area played a special role in determining
    neocortical dynamics was not straightforward. The paper is critical of the ablation experiments often used to determine causal relations in the brain, as removing large portions of cortex as is
    typical in ablation experiments is likely to bring about a new dynamical regime. The addition of
    “hubs” to the network offered a gentler way to perturb the network. Hubs were cells that had the
    three times the number of inputs and outputs as regular cells. Adding hubs to layer 2/3 greatly
    increased the power in the network, but adding hubs to other places did not do the same, supporting the hypothesis that the layer might serve a control function.

    Besides a system to coordinate oscillations, the brain needs a way to return to a dynamic
    balance after a disruption. Adding drive to the model – which would mimic the deployment of
    attention, for example – increased total power, but did not change its spectral profile, which
    may demonstrate a homeostatic mechanism in the neocortex that is intrinsic to its structure. In
    another manipulation, synaptic delays were increased and the power spectrum still remained the same. The paper suggested that these results would be testable in vivo, through behavioral
    attention studies, and in vitro, by cooling brain slices to induce increased synaptic delay.

    The paper highlights the importance of visualization methods to parse out important correlations in what might seem like a meaningless jumble of activity. Neuronal density and the
    connections between neurons were visualized using graph theory. Graphing of correlations of
    frequency fluctuations through time revealed temporal coupling of gamma and theta rhythms. The paper suggested visualization of multiple aspects of cell typology, wiring, and dynamics at
    different scales. When detailed simulations are not feasible, creative modeling can help link
    different levels of detail (eg, between molecular, neuronal, and network levels). Creative
    visualization of various types of activity can lead to better intuition about brain processes,
    which can lead to novel hypotheses for further experimentation and simulation.

    Caption:
    Graphing intracolumnar wiring, where circle size represents the number of cells in the population, and line thickness represents connection strength. This approached revealed a central role for excitatory cells in layer 2.

    Caption:
    Local Field Potential recordings from left medial prefrontal cortex of an awake rat, compared to simulation. Addition of hubs did not change the general shape of the frequency spectra — evidence of homeostatic mechanisms in cortex.

    Reference:

    Neymotin SA, Lee H, Park E, Fenton AA and Lytton WW (2011) Emergence of physiological oscillation frequencies in a computer model of neocortex. Front. Comput. Neurosci. 5:19. doi: 10.3389/fncom.2011.00019

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