Techniques for static and dynamic input multiplexing for high-density neural signal recording are disclosed herein. A multiplexer can receive a first set of neural signals via inputs. A subset of the first set of neural signals above a threshold can be determined. A group of the inputs corresponding to the subset of the first set of neural signals can be determined. Operation of the multiplexer can be modified to block inputs not in the identified group of the inputs. A second set of neural signals can be received into the multiplexer via the group of the inputs. The second set of neural signals can be transmitted to a plurality of channels of an amplifier while blocking inputs not in the identified group of the inputs. The second set of neural signals can be amplified using the amplifier. The amplified second set of neural signals can be transmitted for further processing.

A video encoder determines scaled transform coefficients, wherein determining the scaled transform coefficients comprises scaling transform coefficients of a block of the video data according to a given quantization step. The video encoder determines scalar quantized coefficients, wherein determining the scalar quantized coefficients comprises applying scalar quantization to the scaled transform coefficients of the block. Additionally, the video encoder applies a neural network that determines a respective set of probabilities for each respective transform coefficient of the block. The respective set of probabilities for the respective transform coefficient includes a respective probability value for each possible adjustment value in a plurality of possible adjustment values. Inputs to the neural network include the scaled transform coefficients and the scalar quantized coefficients. The video encoder determines, based on the set of probabilities for a particular transform coefficient of the block, a quantization level for the particular transform coefficient.

An apparatus includes an optical transmitter and/or an optical receiver configured to use one or more artificial neural networks (ANNs) for geometric constellation shaping, the determination of constellation symbols to be transmitted, and/or the determination of the transmitted bit-word(s) or codewords. Each ANN has a plurality of bit-level processing portions connected to a symbol-level processing portion in a manner that enables bitwise processing of constellation-point labels.

An apparatus includes an optical transmitter and/or an optical receiver configured to use one or more artificial neural networks (ANNs) for geometric constellation shaping, the determination of constellation symbols to be transmitted, and/or the determination of the transmitted bit-word(s) or codewords. Each ANN has a plurality of bit-level processing portions connected to a symbol-level processing portion in a manner that enables bitwise processing of constellation-point labels.

A video encoder determines scaled transform coefficients, wherein determining the scaled transform coefficients comprises scaling transform coefficients of a block of the video data according to a given quantization step. The video encoder determines scalar quantized coefficients, wherein determining the scalar quantized coefficients comprises applying scalar quantization to the scaled transform coefficients of the block. Additionally, the video encoder applies a neural network that determines a respective set of probabilities for each respective transform coefficient of the block. The respective set of probabilities for the respective transform coefficient includes a respective probability value for each possible adjustment value in a plurality of possible adjustment values. Inputs to the neural network include the scaled transform coefficients and the scalar quantized coefficients. The video encoder determines, based on the set of probabilities for a particular transform coefficient of the block, a quantization level for the particular transform coefficient.

Techniques for static and dynamic input multiplexing for high-density neural signal recording are disclosed herein. A multiplexer can receive a first set of neural signals via inputs. A subset of the first set of neural signals above a threshold can be determined. A group of the inputs corresponding to the subset of the first set of neural signals can be determined. Operation of the multiplexer can be modified to block inputs not in the identified group of the inputs. A second set of neural signals can be received into the multiplexer via the group of the inputs. The second set of neural signals can be transmitted to a plurality of channels of an amplifier while blocking inputs not in the identified group of the inputs. The second set of neural signals can be amplified using the amplifier. The amplified second set of neural signals can be transmitted for further processing.

Some embodiments enable the interpretation and transformation of unstructured human input to enable end-to-end service visibility across a transport network. Some embodiments include assembling and sending a prompt to a large language model (LLM) to interpret and transform examples of unstructured human input describing a circuit to network element (NE) information, where the NE information can be used to identify neighbor devices between different vendor products providing a service. The LLM can output data points that enable identifying neighboring devices on an end-to-end service path that includes multi-vendor and/or multigenerational devices. The data points used in conjunction with inventory data enables neighboring connections to be determined so that islands of different multi-vendor and/or multigenerational devices can be connected to neighbor devices along the end-to-end service path. The neighboring connections can be added to an inventory database enabling visibility end-to-end service path.