39 results about "Interneuron" patented technology

The present invention relates to methods of isolating a purified or enriched population of cortical or striatal immature interneuron progenitor cells and the isolated purified or enriched population of immature interneuron progenitor cells. Methods of treating a condition mediated by a loss or deficiency of interneuron function using the purified or enriched population of immature interneuron progenitor cells are also disclosed.

Restoration or increase of inhibitory interneuron function in vivo is achieved by transplantation of MGE cells into the brain. Compositions containing MGE cells are provided as are uses to treat various diseases characterised by abnormal inhibitory interneuron function or in cases where increase inhibition may ameliorate neural circuits that are abnormally activated.

Provided herein are compositions and methods for disrupting at least one reward-related behavior in an individual through the use of light-responsive opsin proteins used to control the polarization state of the cholinergic intemeurons of the nucleus accumbens or the striatum.

Methods and systems for generating MGE precursor cells in vitro as well as compositions of enriched MGE precursor cells are provided. The methods and systems provide efficient production of MGE precursors. The methods and systems disclosed herein provide functional MGE precursors which differentiate into functional GABAergic interneurons.

The invention relates to in vitro methods for differentiating mammalian pluripotent stem cells into cells displaying a neuronal phenotype, more particularly into cortical-type neurons including inter alia pyramidal neurons and cortical inhibitory interneurons. The invention further encompasses so-obtained neuronal cells and cell population, compositions comprising such, and further uses of said neuronal cells and cell population.

The present invention relates to methods of isolating a purified or enriched population of cortical or striatal immature interneuron progenitor cells and the isolated purified or enriched population of immature interneuron progenitor cells. Methods of treating a condition mediated by a loss or deficiency of interneuron function using the purified or enriched population of immature interneuron progenitor cells are also disclosed.

The invention relates to in vitro methods for differentiating mammalian pluripotent stem cells into cells displaying a neuronal phenotype, more particularly into cortical-type neurons including inter alia pyramidal neurons and cortical inhibitory interneurons. The invention further encompasses so-obtained neuronal cells and cell population, compositions comprising such, and further uses of said neuronal cells and cell population.

The present invention describes systems and methods for predicting and detecting a seizure in a subject. The methods of the invention comprise measuring interneuron synchrony in terms of the coherence between interneuron action potentials and local field potential oscillations. In one embodiment, the detection of specific patterns of coherence, correlation and firing rate of interneurons predicts an upcoming seizures.

Restoration or increase of inhibitory interneuron function in vivo is achieved by transplantation of MGE cells into the brain. Compositions containing MGE cells are provided as are uses to treat various diseases characterised by abnormal inhibitory interneuron function or in cases where increase inhibition may ameliorate neural circuits that are abnormally activated.

The invention discloses a robot stepping priority control method, and relates to the technical field of robots. The method comprises the following steps: firstly, constructing a spinal cord imitationcontrol network of robot joints, including an intermediate neuron network and a motor neuron network, and utilizing a robot joint flexor muscle and extensor muscle preferential contraction sequence control method to directly design upper-layer central nervous control input with a limited sequence for a robot alternate swing joint flexor muscle and extensor muscle intermediate neuron control network so as to realize stepping priority control; or indirectly controlling the phase relation between the outputs of the leg middle neuron networks by designing the input priority sequence of the upper-layer central control signals of the flexor muscle and the extensor muscle middle neurons of the guide network by utilizing the guide effect between the middle neuron networks so as to control the stepping priority sequence of the robot. The method is convenient to control, and the movement flexibility of the foot type bionic robot and various exoskeleton robots can be effectively improved.

The invention provides application of Neuregulin-1 (NRG1) in preparation of a product to enhance Transient Receptor Potential Cation Channel Subfamily C Member 6 (TRPC6) channel activity. The invention discovers that TRPC6 channel protein is co-labeled with ErbB4 receptor of NRG1 in hippocampus through immunofluorescence experiment; and moreover, it is further proven by Western blot that the NRG1can promote nitrosylation of the TRPC6 channel protein. Then, it is proven by whole cell patch clamp experiment that both nitrosylation inhibitors and TRPC6 channel blockers can block the NRG1-ErbB4 pathway as well as promote release of gamma aminobutyric acid (GABA); and moreover, TRPC6 channel is knocked-out on intermediate neurons expressing the ErbB4 receptor so as to block effect of the NRG1on GABA release, thereby leading to neuronal rhythmic oscillations and schizophrenia-like behavioral abnormalities. Research results of the invention show that the NRG1 can activate the TRPC6 channelby promoting nitrosylation of the TRPC6 channel protein, thereby increasing the release of the GABA.

The invention relates to a GABAergic neuron conditional knockout gene PGC-1 alpha mouse model and a construction method thereof and belongs to the field of animal models and application thereof. The construction method includes the steps that a PGC-1 alpha <flox / flox> mouse is obtained in the first place; then, a PGC-1 alpha <flox / +> is mated with a C57BL / 6J, and the PGC-1 alpha <flox / +> is obtained, then the PGC-1 alpha <flox / +> and the C57BL / 6J are mated with a Dlx5 / 6<Cre-IRES-EGFP> mouse, and the full knockout D1x5 / 6< Cre-IRES-EGFP >, PGC-1 alpha <flox / flox>, semi knockout D1x5 / 6< Cre-IRES-EGFP > and a PGC-1 alpha <flox> mouse are obtained; as a result, a gene knockout mouse on GABAergic internuncial neuron conditional knockout PGC-1 alpha is prepared, and a credible animal model is provided for mechanism research on neurodevelopment and neurodegenerative diseases.

The invention discloses a prediction method and device based on a multi-mode extreme learning machine, equipment and a medium, and the method comprises the steps: obtaining training data and neuron weight parameters under probability distribution, and constructing a tag vector corresponding to the training data; constructing a plurality of intermediate neurons under each probability distribution according to the weight parameters of the neurons; according to the plurality of interneurons, constructing composite features of the training data under each probability distribution; second-order sample features corresponding to the composite features are calculated, and a kernel matrix corresponding to the second-order sample features is constructed; and obtaining a to-be-predicted sample, and predicting the to-be-predicted sample according to an extreme learning machine model jointly constructed by the kernel matrix and the label vector to obtain a prediction result. The method can be applied to solving data-driven modeling problems, such as image classification, sequence prediction, geophysics, credit evaluation and the like. The technical problem of unstable precision caused by random mapping of an extreme learning machine model in the prior art is solved.

The invention relates to an artificial neural network hardware implementation device based on probability calculation. The artificial neural network hardware implementation device based on probability calculation comprises input, intermediate and output modules. The input module is formed by I input neurons, and the input neurons receive first data and output a first random data sequence; the intermediate module is formed by J intermediate neurons, and the intermediate neurons receive the first random data / parameter sequence and output a second random sequence; and the output module is formed by K output neurons, and the output neurons receive the second random data / parameter sequence and output the second data, wherein I, J and K are integers greater than or equal to 1. The output end of the input neurons is connected with the input end of the intermediate neurons, the output end of the intermediate neurons is connected with the input end of the output neurons, and a complete or partial connection mode is adopted. The first and second random data sequences and the first and second random parameter sequences are expressed by the probability values of 0 or 1 appearing in the data sequences within a period of time. According to the neural network device, hardware logic and wiring resources can be greatly reduced, and circuit cost and power consumption can be reduced so that implementation of a super-large-scale neural network through small and medium-sized circuits is enabled to be possible.