• Study shows simple, computationally-ligh

    From ScienceDaily@1:317/3 to All on Monday, April 18, 2022 22:30:46
    Study shows simple, computationally-light model can simulate complex
    brain cell responses

    Date:
    April 18, 2022
    Source:
    Tokyo University of Science
    Summary:
    Studying how brain cells respond to signals from their neighbors
    can aid the understanding of cognition and development. However,
    experimentally measuring the brain's activity is complicated. Neuron
    models provide a non-invasive way to investigate the brain, but
    most existing models are either computationally intensive or
    cannot model complex neuronal responses. Recently, a team has
    used a computationally simple neuron model to simulate some of
    the complex responses of neurons.



    FULL STORY ==========================================================================
    The brain is arguably the single most important organ in the human
    body. It controls how we move, react, think and feel, and enables us to
    have complex emotions and memories. The brain is composed of approximately
    86 billion neurons that form a complex network. These neurons receive,
    process, and transfer information using chemical and electrical signals.


    ========================================================================== Learning how neurons respond to different signals can further the
    understanding of cognition and development and improve the management of disorders of the brain. But experimentally studying neuronal networks
    is a complex and occasionally invasive procedure. Mathematical models
    provide a non-invasive means to accomplish the task of understanding
    neuronal networks, but most current models are either too computationally intensive, or they cannot adequately simulate the different types of
    complex neuronal responses. In a recent study, published in Nonlinear
    Theory and Its Applications, IEICE, a research team led by Prof. Tohru
    Ikeguchi of Tokyo University of Science, has analyzed some of the
    complex responses of neurons in a computationally simple neuron model,
    the Izhikevich neuron model. "My laboratory is engaged in research on neuroscience and this study analyzes the basic mathematical properties
    of a neuron model. While we analyzed a single neuron model in this study,
    this model is often used in computational neuroscience, and not all of its properties have been clarified. Our study fills that gap," explains Prof.

    Ikeguchi. The research team also comprised Mr. Yota Tsukamoto and PhD
    student Ms. Honami Tsushima, also from Tokyo University of Science.

    The responses of a neuron to a sinusoidal input (a signal shaped like a
    sine wave, which oscillates smoothly and periodically) have been clarified experimentally. These responses can be either periodic, quasi-periodic,
    or chaotic. Previous work on the Izhikevich neuron model has demonstrated
    that it can simulate the periodic responses of neurons. "In this work, we analyzed the dynamical behavior of the Izhikevich neuron model in response
    to a sinusoidal signal and found that it exhibited not only periodic
    responses, but non- periodic responses as well," explains Prof. Ikeguchi.

    The research team then quantitatively analyzed how many different types
    of 'inter-spike intervals' there were in the dataset and then used it to distinguish between periodic and non-periodic responses. When a neuron
    receives a sufficient amount of stimulus, it emits 'spikes,' thereby
    conducting a signal to the next neuron. The inter-spike interval refers
    to the interval time between two consecutive spikes.

    They found that neurons provided periodic responses to signals that
    had larger amplitudes than a certain threshold value and that signals
    below this value induced non-periodic responses. They also analyzed the response of the Izhikevich neuron model in detail using a technique called 'stroboscopic observation points,' which helped them identify that the non-periodic responses of the Izhikevich neuron model were actually quasi-periodic responses.

    When asked about the future implications of this study, Prof. Ikeguchi
    says, "This study was limited to the model of a single neuron. In the
    future, we will prepare many such models and combine them to clarify
    how a neural network works. We will also prepare two types of neurons, excitatory and inhibitory neurons, and use them to mimic the actual
    brain, which will help us understand principles of information processing
    in our brain." The use of a simple model for accurate simulations of
    neuronal response is a significant step forward in this exciting field
    of research and illuminates the way towards the future understanding of cognitive and developmental disorders.


    ========================================================================== Story Source: Materials provided by Tokyo_University_of_Science. Note:
    Content may be edited for style and length.


    ========================================================================== Journal Reference:
    1. Yota Tsukamoto, Honami Tsushima, Tohru Ikeguchi. Non-periodic
    responses
    of the Izhikevich neuron model to periodic inputs. Nonlinear
    Theory and Its Applications, IEICE, 2022; 13 (2): 367 DOI:
    10.1587/nolta.13.367 ==========================================================================

    Link to news story: https://www.sciencedaily.com/releases/2022/04/220418093832.htm

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