33 results about "Neuronal circuits" patented technology

Disclosed herein are methods for modifying electrical currents in brain circuits through the simultaneous use of focused ultrasound pulse (FUP) and an existing brain-imaging system, such as a functional magnetic resonance imaging (fMRI) system. The methods are used for research, treatment and diagnosis of psychiatric, neurological, and neuroendocrine disorders whose biological mechanisms include brain circuits. The methods include the simultaneous steps of applying FUP to a live neuronal circuit within a brain and monitoring a brain image produced by a brain imaging system during the application of FUP.

A mobile brain-based device BBD includes a mobile base equipped with sensors and effectors (Neurally Organized Mobile Adaptive Device or NOMAD), which is guided by a simulated nervous system that is an analogue of cortical and sub-cortical areas of the brain required for visual processing, decision-making, reward, and motor responses. These simulated cortical and sub-cortical areas are reentrantly connected and each area contains neuronal units representing both the mean activity level and the relative timing of the activity of groups of neurons. The brain-based device BBD learns to discriminate among multiple objects with shared visual features, and associated “target” objects with innately preferred auditory cues. Globally distributed neuronal circuits that correspond to distinct objects in the visual field of NOMAD 10 are activated. These circuits, which are constrained by a reentrant neuroanatomy and modulated by behavior and synaptic plasticity, result in successful discrimination of objects. The brain-based device BBD is moveable, in a rich real-world environment involving continual changes in the size and location of visual stimuli due to self-generated or autonomous, movement, and shows that reentrant connectivity and dynamic synchronization provide an effective mechanism for binding the features of visual objects so as to reorganize object features such as color, shape and motion while distinguishing distinct objects in the environment.

Compositions and methods are provided for modulating the activity of cells using engineered receptors, polynucleotide encoded engineered receptors, and gene therapy vectors comprising polynucleotides encoding engineered receptors. These compositions and methods find particular use in modulating the activity of neurons, for example in the treatment of disease or in the study of neuronal circuits.

An electronic neuronal circuit system to model the interaction between the postsynaptic terminal of a first synapse between two neurons and the postsynaptic terminal of a second synapse between two neurons includes comparators to model the presynaptic neurons of the synapses, plurality of three diodes connected to the comparators to model synapses, an AND gate and latch to model the formation of functional link between the postsynaptic terminals, and timer-controlled latches for controlling the life-span of the inter-postsynaptic functional link, durations of re-activation of inter-postsynaptic functional link and flow of activity through the output postsynaptic dendritic terminals.

The present invention provides methods of therapy of cognitive deficits associated with a central nervous system disorder or condition, methods of enhancing cognitive performance and methods for repeated stimulation of neuronal activity or a pattern of neuronal activity, such as that underlying a specific neuronal circuit(s). The methods comprise combining cognitive training protocols and a general administration of CREB pathway-enhancing agents.

A circuit for simulating the behavior of a biological neural circuit, the circuit comprising: a plurality of nodes, wherein each node comprises: a neuron circuit; a time multiplexed synapse circuit, the time multiplexed synapse circuit The synaptic circuit is coupled to the input of the neuron circuit; the short-term plasticity (STP) circuit of time multiplexing, the short-term plasticity (STP) circuit of this time multiplexing is coupled to the input of the node and is coupled to the synaptic circuit; A multiplexed spike timing-dependent plasticity (STDP) circuit coupled to the input of the node and coupled to the synaptic circuit; an output of the node coupled to the neuron a circuit; and an interconnect structure coupled between the plurality of nodes for providing coupling from an output of any node of the plurality of nodes to any input of any other node of the plurality of nodes.

The present invention proposes a side-inhibition neuron circuit based on a ferroelectric transistor FeFET, which includes a capacitor, a reset tube, a positive feedback tube, two-stage series inverters, and a ferroelectric transistor; wherein, the capacitor is used to simulate biological The cell membrane capacitance of the neuron accumulates the charge brought by the input post-synaptic current; the reset tube is an N-type MOSFET device that provides a reset path for the charge accumulated on the capacitor; the positive feedback tube is a P-type MOSFET device, When the input of the first-stage inverter is close to its logic threshold level, it replenishes the charge for the capacitor; the two-stage series inverter is composed of two sets of complementary CMOS, which amplifies the voltage change at the input terminal, and the pulse is generated at its output terminal. ; The ferroelectric transistor is an N-type FeFET device used to mimic the lateral inhibition function of biological neurons. The invention can significantly reduce the hardware overhead; meanwhile, it highly simulates the basic characteristics and advanced functions of biological neurons.

Provided is a neuromorphic system using a neuron circuit. The neuromorphic system includes: one or two or more neuron circuits configured to output a firing signal according to signals input from a synapse array; a homeostatic circuit for each neuron circuit; and a global self-controller configured to generate and provide control signals for the neuron circuits by using the firing signal output from the neuron circuits. The neuron circuit includes a neuromorphic device and an output circuit that outputs the firing signal of the neuromorphic device. The global self-controller generates and supplies a reset signal to the neuromorphic device of the fired neuron circuit, and the global self-controller generates and supplies a lateral inhibition signal to the neuromorphic device of the non-fired neuron circuit. The homeostatic circuit alleviates inhibition of other neurons by the neurons with a predominant firing function.

The invention discloses a voice storage and classification system based on a memristor, which relates to the field of digital audio information storage and classification in electronic technology, including a sound pickup module, an amplification module, a memristor filter module, and an A / D conversion connected in sequence module, a control module and a memory module based on a memristor cross array, the control module is also connected to a speech classification module, the speech classification module is a neural network based on a memristor, and the hardware circuit of the neuron of the neural network is a memristor bridge neuron circuit.

The present invention provides a RBF neurons circuit and its working method, which includes the first Gilbert multiplier, the second Gilbert multiplication, the square root circuit, the resistance and the Gaussian function to generate the circuit;The current output terminal of the multiplier is connected to the input terminal of the square root circuit respectively; the output end of the square root circuit connects one end of the resistor and the Gaussian function generates the current input terminal of the circuit; the other end of the resistor is grounded.By given the appropriate external bias voltage, a variable and variable two -dimensional Gaussian function can be generated.The present invention can be a special neural network chip, which has the advantages of small volume, portable, embedded, and other advantages. It can achieve high parallel computing, overcoming the software to achieve the large volume, difficulty carrying, not easy to embed, calculated by the RBF neuron circuit moduleSlow flaws.