Abstract: A thermodynamic RAM technology stack, two or more memristors or pairs of memristors comprising AHaH (Anti-Hebbian and Hebbian) computing components, and one or more AHaH nodes composed of such memristor pairs to that forms at least a portion of the thermodynamic RAM technology stack. The levels of the thermodynamic-RAM technology stack include the memristor, a Knowm synapse, an AHaH Node, a kT-RAM, kT-RAM instruction set, a sparse spike encoding, a kT-RAM emulator, and a SENSE Server.

Abstract: A thermodynamic RAM technology stack, two or more memristors or pairs of memristors comprising AHaH (Anti-Hebbian and Hebbian) computing components, and one or more AHaH nodes composed of such memristor pairs that form at least a portion of the thermodynamic RAM technology stack. The levels of the thermodynamic-RAM technology stack include the memristor, a Knowm synapse, an AHaH node, a kT-RAM, kT-RAM instruction set, a sparse spike encoding, a kT-RAM emulator, and a SENSE Server.

Abstract: A universal machine learning building block, comprising in some embodiments a differential pair of output electrodes, wherein each electrode comprises a plurality of input lines coupled to it via collections of meta-stable switches. In other embodiments, a methodology can be implemented in the context of hardware and/or software for deriving linear neurons implementing an AHaH plasticity rule and generating an AHaH node(s) that can include one or more such linear neurons, wherein the AHaH node(s) functions according to an AHaH rule. Some embodiments can also include an AHaH classifier and/or AHaH cluster that include one or more such AHaH nodes.

Abstract: A thermodynamic random access memory includes one or more AHaH (Anti-Hebbian and Hebbian) node wherein read out of data is accomplished via a common summing electrode through memristive components and wherein multiple input cells are simultaneously active. A ktRAM architecture comprising a memory wherein each input synapse or “bit” of the memory interacts on or with a common electrode through a common “dendritic” electrode, and wherein each input can be individually driven. Each input constitutes a memory cell driving a common electrode.

Abstract: A thermodynamic RAM technology stack, two or more memristors or pairs of memristors comprising AHaH (Anti-Hebbian and Hebbian) computing components, and one or more AHaH nodes composed of such memristor pairs that form at least a portion of the thermodynamic RAM technology stack. The levels of the thermodynamic-RAM technology stack include the memristor, a Knowm synapse, an AHaH node, a kT-RAM, kT-RAM instruction set, a sparse spike encoding, a kT-RAM emulator, and a SENSE Server.

Abstract: A ktRAM architecture comprising a memory wherein each input synapse or “bit” of the memory interacts on or with a common electrode through a common “dendritic” electrode, and wherein each input can be individually driven. Each input constitutes a memory cell driving a common electrode. One or more AHaH nodes can be provided wherein read out of data is accomplished via a common summing electrode through memristive components and wherein multiple input cells are simultaneously active.

Abstract: Methods and systems for thermodynamic computing based on the attractor dynamics of volatile dissipative electronics attempting to maximize circuit power consumption. A general model of memristive devices based on collections of metastable switches, adaptive synaptic weights can be formed from a differential pair of memristors and modified according to anti-hebbian and hebbian plasticity. The arrays of synaptic weights can be employed to build a neural node circuit with attractor states that are shown to be logic functions forming a computationally complete set. By configuring the attractor states of the computational building block in different ways, high-level machine learning functions can be demonstrated for real-world applications.

Abstract: An immunoassay apparatus on a chip is disclosed, which can quantitatively measure the concentration of hormones (particularly corticosterone and progesterone) in a biological sample. Such an apparatus can be designed to be used in the field, saving time and money for those taking the measurements. The measurements are made within a micro-fluidic channel configured on a substrate of a chip, which is loaded using simple capillary forces. Competitive immunoassay can be performed, with the competing agents being the hormone (e.g., antigen) and hormone-coated latex beads (e.g., both pre-mixed in a methanol solution).