Mitsubishi Denki Kabushiki Kaisha

Satoru Shiono

Satoshi Yamada

Yoshio Hanazato

Kenichi Inatomi

Kenji Matsumoto

Manabu Tanifuji

Mamiko Nakako

In a brain activity measuring apparatus, the reflected light from the brain surface is conducted by way of an objective lens and focusing lens and split into two beams by a beam splitter. The light beams are conducted through respective band-pass filters having different transmission wavelengths, and received by CCD cameras in which images are formed from the filtered light beams. The CCD cameras produce signals of the images, and a differential amplifier subtracts one image signal from the other thereby to remove a background noise component. The apparatus is free from noises caused by mechanical vibration, and the resulting differential signal exhibits the brain activity accurately.

Apparatus for measuring brain activity

A glucose sensitive FET sensor is provided which includes a substrate, a source electrode formed in the substrate, a drain electrode formed in the substrate, a hydrogen ion sensitive film formed on the substrate to cover the source electrode and the drain electrode, and a thin enzyme immobilized membrane formed on the hydrogen ion sensitive film and containing glucose oxidase and gluconolactonase. Also provided is a method of producing a glucose sensitive FET sensor which includes the steps of preparing an enzyme immobilized membrane as a thin film containing glucose oxidase and gluconolactonase and forming the membrane on a hydrogen ion sensitive film which is formed on a substrate to cover a source electrode and a drain electrode, both of which are formed in the substrate.

Glucose sensitive FET sensor and method of making same

The present invention provides an interneuron crossrelation identification technique and an interneuron connection-structure estimation technique for inferring a connection-structure and the strengths of the connectivities among a plurality of neurons required for constructing a neural network model, by obtaining crossrelations among time-course data of neurons. The interneuron crossrelation detection technique may include steps of: calculating conditional probabilities by, among other things, normalizing crosscoincidence histograms calculated from time-course data of activities of the neurons representing a train of action potentials of the neurons representing a train of action potentials of the neurons, and comparing trains of symbols representing time-course states of the activities of the neurons; distinguishing an inhibitory connectivity form an excitatory connectivity by comparing the conditional probabilities to each other; and quantitatively estimating the magnitude of crossrelation among the time-course data. The interneuron connection-structure estimation technique may include steps of: computing conditional probabilities by normalizing cross-coincidence histograms calculated from time-course data of activities of the neurons representing a train of action potentials of the neurons; computing conditional mutual information and three-point mutual information from the computed conditional probabilities; and inferring a connection structure among the neurons.

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Hyogo, Japan

Satoru Shiono