Ilan Breskin

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We study living neural networks by measuring the neurons' response to a global electrical stimulation. Neural connectivity is lowered by reducing the synaptic strength, chemically blocking neurotransmitter receptors. We use a graph-theoretic approach to show that the connectivity undergoes a percolation transition. This occurs as the giant component(More)
Optical based methods for non-invasive measurement of regional blood flow tend to incorrectly assess cerebral blood flow, due to contribution of extra-cerebral tissues to the obtained signal. We demonstrate that spectral analysis of phase-coded light signals, tagged by specific ultrasound patterns, enables differentiation of flow patterns at different(More)
Cerebral autoregulation is a mechanism which maintains constant cerebral blood flow (CBF) despite changes in mean arterial pressure (MAP). Assessing whether this mechanism is intact or impaired and determining its boundaries is important in many clinical settings, where primary or secondary injuries to the brain may occur. Herein we describe the development(More)
We introduce, validate and demonstrate a new software correlator for high-speed measurement of blood flow in deep tissues based on diffuse correlation spectroscopy (DCS). The software correlator scheme employs standard PC-based data acquisition boards to measure temporal intensity autocorrelation functions continuously at 50 − 100 Hz, the fastest blood flow(More)
We study neural connectivity in cultures of rat hippocampal neurons. We measure the neurons' response to an electric stimulation for gradual lower connectivity, and characterize the size of the giant cluster in the network. The connectivity undergoes a percolation transition described by the critical exponent β ≃ 0.65. We use a theoretic approach based on(More)
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