A device includes first and second wires, resistors, and a processor. Input signals are transmitted from the first wires through the resistors to the second wires. The processor receives a sum value of the input signals from one of the second wires, and shifts the sum value by a nonlinear activation function to generate a shifted sum value. The processor calculates a backpropagation value based on the shifted sum value and a target value related to a corresponding input signal of the input signals, and generates a pulse number based on the corresponding input signal of the input signal and the backpropagation value. The processor applies a voltage pulse to one of the resistors related to the corresponding input signal based on the pulse number, in order to modify a value of the corresponding input signal in the input signals to be the same as the target value.

A universal odor code system encodes an olfactory stimulus into olfactory receptor space as quantitative measures of responses by olfactory receptors, producing an odor code profile. A mapping function maps an odor code profile into a formula of elements that approximates or recreates an odor code profile of a target olfactory stimulus.

A crested barrier memory and selector device may include a first electrode, a first self-rectifying, tunneling layer having a first dielectric constant, and an active, barrier layer that has a second dielectric constant and another self-rectifying, tunneling layer having a third dielectric constant. The first self-rectifying layer may be between the first electrode and the active layer. The second dielectric constant may be at least 1.5 times larger than the first dielectric constant. The device may also include a second electrode, where the active, barrier layer is between the first self-rectifying, tunneling layer and the second electrode.

Mechanisms including: receiving a first set of observed spike counts (FSoOSCs) for the spiking cells; determining a set of probabilities (SoPs) by: retrieving the SoPs from stored information (SI); or calculating the SopS based on the SI, wherein the SI regards possible biological states (BSs) of a subject, wherein each of the possible BSs belongs to at least one of a plurality of time sequences (PoTSs) of BSs, wherein each of the PoTSs of BSs corresponds to a possible action of the subject, and wherein each probability in the set of probabilities indicates a likelihood of observing a possible spike count for one of the plurality of spiking cells; identifying using a hardware processor a first identified BS of the subject from the possible BSs based on the FSoOSCs and the set of probabilities; and determining an action to be performed based on the first identified BS.

Mechanisms including: receiving a first set of observed spike counts (FSoOSCs) for the spiking cells; determining a set of probabilities (SoPs) by: retrieving the SoPs from stored information (SI); or calculating the SopS based on the SI, wherein the SI regards possible biological states (BSs) of a subject, wherein each of the possible BSs belongs to at least one of a plurality of time sequences (PoTSs) of BSs, wherein each of the PoTSs of BSs corresponds to a possible action of the subject, and wherein each probability in the set of probabilities indicates a likelihood of observing a possible spike count for one of the plurality of spiking cells; identifying using a hardware processor a first identified BS of the subject from the possible BSs based on the FSoOSCs and the set of probabilities; and determining an action to be performed based on the first identified BS.

An method of operating an implantable device includes sensing a neural signal generated in a tissue of a body, recognizing input information to process a cryptocurrency-based financial transaction by analyzing the sensed neural signal, and processing the cryptocurrency-based financial transaction based on the recognized input information.

A Biological Neural Network (BNN) core unit comprising a neural cell culture, an input stimulation unit, an output readout unit may be controlled through its various life cycles to provide data processing functionality. An automation system comprising an environmental and chemical controller unit adapted to operate with the BNN stimulation and readout data interfaces facilitates the monitoring and adaptation of the BNN core unit parameters. Pre-processing and post-processing of the BNN interface signals may further facilitate the training and reinforcement learning by the BNN. Multiple BNN core units may also be assembled together as a stack. The proposed system provides a BNN Operating System as a core component for a wetware server to receive, process and transmit data for different client applications without exposing the BNN core unit components to the client user while requiring significantly less energy than conventional silicon-based hardware and software information processing for high-level cognitive computing tasks.

A crested barrier memory and selector device may include a first electrode, a first self-rectifying, tunneling layer having a first dielectric constant, and an active, barrier layer that has a second dielectric constant and another self-rectifying, tunneling layer having a third dielectric constant. The first self-rectifying layer may be between the first electrode and the active layer. The second dielectric constant may be at least 1.5 times larger than the first dielectric constant. The device may also include a second electrode, where the active, barrier layer is between the first self-rectifying, tunneling layer and the second electrode.

A superconducting neuromorphic pipelined processor core can be used to build neural networks in hardware by providing the functionality of somas, axons, dendrites and synaptic connections. Each instance of the superconducting neuromorphic pipelined processor core can implement a programmable and scalable model of one or more biological neurons in superconducting hardware that is more efficient and biologically suggestive than existing designs. This core can be used to build a wide variety of large-scale neural networks in hardware. The biologically suggestive operation of the neuron core provides additional capabilities to the network that are difficult to implement in software-based neural networks and would be impractical using room-temperature semiconductor electronics. The superconductive electronics that make up the core enable it to perform more operations per second per watt than is possible in comparable state-of-the-art semiconductor-based designs.

A reinforcement learning brain-machine interface (RL-BMI) can have a policy that governs how detected signals, emanating from a motor cortex of a subject's brain, are translated into action. The policy can be improved by detecting a motor signal having a characteristic and emanating from the motor cortex. The system can provide, to a device and based on (i) the motor signal and (ii) an instruction policy, a command signal resulting in a first action by a device. Additionally, an evaluation signal, emanating from the motor cortex in response to the first action, can also be detected. With the foregoing information, the system can adjust the policy based on the evaluation signal such that a subsequent motor signal, from the subject's brain, having the characteristic results in a second action, by the device, different from the first action, as needed.