Modeling driver cells in developing neuronal networks

Self-Organized Criticality in Developing Neuronal Networks

Recently evidence has accumulated that many neural networks exhibit self-organized criticality. In this state, activity is similar across temporal scales and this is beneficial with respect to information flow. If subcritical, activity can die out, if supercritical epileptiform patterns may occur. Little is known about how developing networks will reach and stabilize criticality. Here we monito...

Biological Neuronal Networks, Modeling Of

In recent decades, since the seminal work of A. L. Hodgkin and A. F. Huxley (1), the study of the dynamical phenomena emerging in a network of biological neurons has been approached by means of mathematical descriptions, computer simulations (2,3), and neuromorphic electronic hardware implementations (4). Several models have been proposed in the literature, and a large class of them share simil...

Computational modeling of neuronal networks

Human brain contains about 10 billion neurons, each of which has about 10~10,000 nerve endings from which neurotransmitters are released in response to incoming spikes, and the released neurotransmitters then bind to receptors located in the postsynaptic neurons. However, individually, neurons are noisy and synaptic release is in general unreliable. But groups of neurons that are arranged in sp...

Simulation of developing human neuronal cell networks

BACKGROUND Microelectrode array (MEA) is a widely used technique to study for example the functional properties of neuronal networks derived from human embryonic stem cells (hESC-NN). With hESC-NN, we can investigate the earliest developmental stages of neuronal network formation in the human brain. METHODS In this paper, we propose an in silico model of maturating hESC-NNs based on a phenome...

MODELING NEURONAL NETWORKS Spontaneous episodic activity in the developing spinal cord