Abstract: The network comprises at least one network layer in which a plurality of electronic oscillators, interconnected via programmable coupling elements storing respective network weights, generate oscillatory signals at time delays dependent on the input signal to propagate the input signal from an input to an output of that layer. The network is adapted to provide a network output signal dependent substantially linearly on phase of oscillatory signals in the last layer of the network. The method includes calculating a network error dependent on the output signal and a desired output for the training sample, and calculating updates for respective network weights by backpropagation of the error such that weight-updates for a network layer are dependent on a vector of time delays at the input to that layer and the calculated error at the output of that layer.

Abstract: The present disclosure relates, in part, to anti-influenza antibodies (and antigen binding fragments thereof) and combinations thereof for preventing and treating influenza infection. Presently disclosed combinations provide surprising synergistic effects and can potently prevent, inhibit, or neutralize an influenza infection, such as an influenza A virus (IAV) infection an influenza B virus (IBV) infection, or both.

Abstract: The instant disclosure provides antibodies and antigen-binding fragments thereof that can bind to an influenza virus neuraminidase (NA) and can neutralize an influenza virus infection. Also provided are polynucleotides that encode an antibody, vectors that comprise such polynucleotides, host cells that can express the antibodies, related compositions, and methods of using the herein disclosed compositions to, for example, treat or prevent an influenza infection.

Abstract: The network comprises at least one network layer in which a plurality of electronic oscillators, interconnected via programmable coupling elements storing respective network weights, generate oscillatory signals at time delays dependent on the input signal to propagate the input signal from an input to an output of that layer. The network is adapted to provide a network output signal dependent substantially linearly on phase of oscillatory signals in the last layer of the network. The method includes calculating a network error dependent on the output signal and a desired output for the training sample, and calculating updates for respective network weights by backpropagation of the error such that weight-updates for a network layer are dependent on a vector of time delays at the input to that layer and the calculated error at the output of that layer.

Abstract: An electronic circuit for enabling an efficient use of an oscillating neural network for feature recognition may be provided. The electronic circuit comprises a network of coupled voltage-controlled oscillators, wherein each of the voltage-controlled oscillators is adapted for receiving an edge input signal which is phase-shifted by a fraction of a period length of the voltage-controlled oscillators according to a signal strength of an analog input signal allotted to a respective one of the voltage-controlled oscillators, and an active output circuit. The active output circuit includes input terminals connected to selected ones of the voltage-controlled oscillators, an adder portion for adding input signals present at the input terminals, and a non-linear amplifier, an which input line is of the non-linear amplifier being connected to an output line of the adder portion, thereby an efficient use of an oscillating neural network.