Growing neuronal networks

Dissociated neurons cultured in vitro autonomously form complicated networks. The video shows an example of growing neuronal networks recorded by time-lapse observation during 10 days. Studies using multi-electrode arrays (MEAs) have demonstrated that the networks spontaneously show synchronized bursts. Bursting activity has also been observed during mammalian development in vivo and is thought to be involved in the formation of neuronal circuits.

  We focus on the problems of the synchronized burst. The problems are studied in terms of cell culture, fluorescence imaging, cell patterning, correlative imaging, gene expression, screening, anesthesia, low-temperature preservation, theoretical analysis, etc.

Fluorescence Imaging

Single Neuron

This movie presents a cultured cortical neuron of the reconstructed 3D micrograph obtained by confocal laser scanning microscope.  The micrograph is merged with the three fluorescence images of MAP2 (blue), VGAT (green), and VGluT1 (red). The distributions of both excitatory and inhibitory synaptic terminals can be measured simultaneously.



Neuronal Networks on MEAs
(Ito D., et al., Neuroscience, 171(1), 2010.  By courtesy of the IBRO.)

Direct observation of cortical cultures on MEAs using fluorescence microscopy is a useful tool with which to analyze spatial factors that affect neuronal dynamics. To investigate the morphological distribution of the cortical networks on MEAs, immunocytochemistry is performed immediately after the final recording of electrical activity. The figure,  which was selected as the front cover of Neuroscience, shows an immunofluorescence micrograph for typical cultures. Neuronal networks grown on MEAs are clearly recognized by this procedure.

Cell Patterning

Neuron Patterning
Neuron Patterning

Generally, neuronal cells are dispersed on MEAs at high densities in order to surely obtain electrical signals from the electrode. In this case, we need to analyze the many signals from a complex neural network, even though the majority of the cells are not involved in the signals. Only signals of cells near the electrodes are involved, and signals of most other cells could not be detected in high-density culture. Patterning neuronal cell on MEAs is one approach to overcome this drawback.

 We studied neuronal cell patterning on a multi-electrode array (MEA).  We conducted electrophysiological measurements and found that the patterned neuronal cells were sufficiently matured and developed neural networks, demonstrating that our patterning method is useful for a neuronal network analysis platform.

Correlative Imaging of Optical and Electron microscopes

The use of both methods,  that is, an optical microscope and an electron microscope,  is an effective technique in elucidating the nature in life. This technique is called correlative imaging. The video shows an example of correlative imaging. An axon of a neuron in  solution of culture medium was observed by time-lapse measurement using an optical microscope. Next immunocytochemistry and fluorescence imaging were executed.  In the end SEM(Scanning Electron Microscope) observation was performed in vacuum.

Synchronized Burst

Synchronized burst is a remarkable phenomenon of neuronal networks  (watch the video). Studies using multi-electrode arrays (MEAs) have demonstrated that synchronized bursts are highly variable in terms of their spatio-temporal firing patterns but highly correlated among neurons. Furthermore, recent studies of synchronous activity have revealed that cortical cultures in vitro showed scale-free topology of connectivity, precisely timed activity, and neuronal avalanches.


Theoretical Analysis

Neuron Theory
Neuron Theory

Dynamical systems are used for modeling in various fields, including physics, biology, economics and physiology. Such systems can be classified into two categories from a number of different viewpoints: e.g. they can be considered conservative or dissipative, discrete or continuous, or autonomous or non-autonomous systems. The conservative dynamical systems conserve their phase volumes, while the dissipative ones, which are characterized by attractors after sufficient time, decrease their phase volumes. The discrete systems are expressed using mappings, while the continuous ones are expressed using ordinary differential equations. The autonomous systems are independent of other systems, i.e. closed, while the non-autonomous ones are dependent on other systems, i.e. open.


  • D. Ito, T. Komatsu, K. Gohara:
    Measurement of saturation processes in glutamatergic and GABAergic synapse densities during long-term development of cultured rat cortical networks,
    Brain Research, 1534, 22-32, 2013.
    doi: 10.1016/j.brainres.2013.08.004
  • M. Suzuki, K. Ikeda, M. Yamaguchi, S. N. Kudoh, K. Yokoyama, R. Satoh, D. Ito, M. Nagayama, T. Uchida, K. Gohara:
    Neuronal cell patterning on a multi-electrode array for a network analysis platform,
    Biomaterials, 34(21), 5210-5217, 2013.
  • T. Uchida, S. Suzuki, Y. Hirano, D. Ito, M. Nagayama, and K. Gohara:
    Xenon-induced inhibition of synchronized bursts in a rat cortical neuronal network,
    Neuroscience, 214, 149-158, 2012.
  • T. Uchida, M. Nagayama, T. Taira, K. Shimizu, M. Sakai, K. Gohara:
    Optimal temperature range for low-temperature preservation f dissociated neonatal rat cardiomyocytes,
    Cryobiology, 63(3), 279-284, 2011.
  • M. Yamaguchi, K. Ikeda, M. Suzuki, A. Kiyohara, S. Kudoh, K. Shimizu, T. Taira, D. Ito, T. Uchida, and K. Gohara:
    Cell patterning using a template of microstructured organosilane layer fabricated by vacuum ultraviolet light lithography,
    Langmuir, 27 (20), 12521–12532, 2011.
    doi: 10.1021/la202904g
  • D. Ito, H. Tamate, M. Nagayama, T. Uchida, S. Kudoh, and K. Gohara:
    Minimum neuron density for synchronized bursts in a rat cortical culture on multi-electrode arrays,
    Neuroscience, 171(1), 50-61, 2010.
  • Masaki Nomura, Daisuke Ito, Hiroki Tamate, Kazutoshi Gohara and Toshio Aoyagi:
    Estimation of functional connectivity that causes burst-like population activities,
    FORMA, 24(1), pp.11-16, 2009.
  • J. Nishikawa and K. Gohara:
    Automata on Fractal Sets Observed in Hybrid Dynamical Systems,
    Int, J. Bifurcation and Chaos, 18(12), pp.3665-3678, 2008.
  • J. Nishikawa and K. Gohara:
    Anomaly of fractal dimensions observed in stochastically switched systems,
    Physical Review E, 77, 036210.1-036210. 8, 2008.
  • H. Oka and K. Gohara:
    Approximation of Fractal Transition Using Attractors Excited by Periodic Inputs,
    Int. J. Bifurcation and Chaos, 13(4), pp.943-950, 2003.
  • J. Nishikawa and K. Gohara:
    Fractals in an Electronic Circuit Driven by Switching Inputs,
    Int. J. Bifurcation and Chaos12(4), pp.827-834, 2002.
  • R. Wada and K. Gohara:
    Closures of  Fractal Sets in Nonlinear Dynamical Systems with Switched Inputs,
    Int. J. Bifurcation and Chaos, 11(8), pp.2205-2215, 2001.
  • R. Wada and K. Gohara:
    Fractals and Closures of Linear Dynamical Systems Excited Stochastically  by Temporal Inputs,
    Int.  J. Bifurcation and Chaos,11(3), pp.755-779, 2001.
  • S. Sato and K. Gohara:
    Fractal Transition in Continuous Recurrent Neural Networks,
    Int. J. Bifurcation and Chaos, 11(2), pp.421-434, 2001.
  • S. Sato and K. Gohara:
    Poincare Mapping of Continuous Recurrent Neural Networks Excited by Temporal External Input,
    Int. J. Bifurcation and Chaos 10(7), pp.1677-1695, 2000.
  • K. Gohara, H. Sakurai, and S. Sato:
    Experimental Verification for Fractal Transition Using a Forced Damped Oscillator,
    Fractals, 8(1), pp.67-72, 2000.
  • K. Gohara and A. Okuyama:
    Fractal Transition -Hierarchical Structure and Noise Effect,
    Fractals, 7(3), pp.313-326, 1999.
  • K. Gohara and A. Okuyama:
    Dynamical Systems Excited by Temporal Inputs,
    Fractals, 7(2), pp.205-220, 1999.

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