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Últimas publicaciones y patentes sobre computación neuromórfica

Publicaciones y patentes sobre computación neuromórfica

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La computación neuromórfica es un campo interdisciplinar que diseña sistemas de hardware y software inspirados en la estructura y el funcionamiento de las redes neuronales biológicas, en particular el cerebro humano. Emplea arquitecturas no von Neumann, en las que los procesadores y la memoria están estrechamente integrados, lo que permite la computación paralela masiva, el procesamiento de información basado en eventos y el funcionamiento de bajo consumo. Utilizando elementos como neuronas artificiales y sinapsis, los sistemas neuromórficos imitan los mecanismos cerebrales de aprendizaje, adaptación y procesamiento sensorial.

Esta es nuestra última selección de publicaciones y patentes mundiales en inglés sobre Computación Neuromórfica, entre muchas revistas científicas en línea, clasificadas y centradas en computación neuromórfica, red neuronal con pico, neurona con fuga de integración y disparo, memristor, neuromórfico, plasticidad sináptica, retraso axonal, neurona de silicio, VLSI analógico, aprendizaje Hebbiano y neuronal con pico.

Current-mode operation of analog content addressable memories

Patent published on the 2026-04-16 in US under Ref US20260105959 by HEWLETT PACKARD ENTPR DEV LP [US] (Buonanno Luca [us], Pedretti Giacomo [us], Zhao Lei [us], Ignowski James [us])

Abstract: [0000] In certain examples, an analog content addressable memory (ACAM) component includes a plurality of transistors and a memristor. A gate terminal of a first transistor of the plurality of transistors is coupled to a data line for applying an input current, another terminal of the first transistor is coupled to the memristor and a gate terminal of a second transistor, another terminal of the second transistor is coupled to a match line, and the ACAM component is configured to provide a match[...]


Our summary: An analog content addressable memory (ACAM) includes multiple transistors and a memristor. The first transistor applies an input current to the memristor and a second transistor. The ACAM provides a match result based on the input current and the memristor s programmed value.

Analog content addressable memory, Current-mode operation, Transistors, Memristor

Patent

Memristive computing schemes in the back-end-of-the-line

Patent published on the 2026-03-05 in WO under Ref WO2026050108 by NATIONAL UNIV OF SINGAPORE [SG] (Astier Hippolyte P A G [sg], Mangattuchali Muhammed Juvaid [sg], Das Chandan [sg], Sudijono John [sg], Gradecak-garaj Silvija [sg])

Abstract: Provided are ultrathin films for use as modulable-resistance channels (memristors, resistive switches, synaptic nodes/synaptic emulators, hysteretic resistors, ReRAM or RRAM, transistors, memtransistors) in the back-end-of-line (BEOL) applications, thus, combining two logic nodes in the BEOL and the front-end-of-line (FEOL). Transition metal dichalcogenides (TMDC) films, other 2D material films, metal oxide films, metal carboxide films, metal nitride oxide films, and a nitride films are provided[...]


Our summary: Memristive computing schemes utilize ultrathin films as modulable-resistance channels in back-end-of-line applications. Various 2D material films and metal oxide films are employed for these components. The study also explores computing schemes leveraging these modulable-resistance elements.

Memristive computing, ultrathin films, back-end-of-line, modulable-resistance

Patent

Memristive computing schemes in the back-end-of-the-line

Patent published on the 2026-03-05 in WO under Ref WO2026050109 by NATIONAL UNIV OF SINGAPORE [SG] (Astier Hippolyte P A G [sg], Mangattuchali Muhammed Juvaid [sg], Das Chandan [sg], Sudijono John [sg], Gradecak-garaj Silvija [sg])

Abstract: Provided are ultrathin films for use as modulable-resistance channels (memristors, resistive switches, synaptic nodes/synaptic emulators, hysteretic resistors, ReRAM or RRAM, transistors, memtransistors) in the back-end-of-line (BEOL) applications, thus, combining two logic nodes in the BEOL and the front-end-of-line (FEOL). Transition metal dichalcogenides (TMDC) films, other 2D material films, metal oxide films, metal carboxide films, metal nitride oxide films, and a nitride films are provided[...]


Our summary: Memristive computing schemes utilize ultrathin films as modulable-resistance channels in back-end-of-line applications. Various 2D materials and metal films are employed for these components. The study also explores computing schemes leveraging these modulable-resistance elements.

Memristive computing, ultrathin films, back-end-of-line, modulable-resistance

Patent

Integrated circuit to implement spiking neural network

Patent published on the 2026-02-24 in US under Ref US12561552 by PUIG TOMAS [US] (Puig TomÁs [us])

Abstract: An integrated circuit and associated methods are disclosed for implementing a spiking neural network (SNN) through a Temporal Causal Entanglement Graph (TCEG). In one embodiment, the TCEG includes a plurality of nodes, each of which is associated with node attributes, and a plurality of edges, each of which is associated with edge attributes. Each node in the TCEG is connected to at least one other node by one or more edges, where each edge denotes a causal entanglement between a first node and [...]


Our summary: The integrated circuit implements a spiking neural network using a Temporal Causal Entanglement Graph. Each node in the graph has attributes and connects to other nodes through edges that represent causal relationships. The system incorporates normalizing-flow neurons and transfer entropy to enable dynamic learning and real-time causal inference.

Integrated Circuit, Spiking Neural Network, Temporal Causal Entanglement Graph, Dynamic Learning

Patent

Memory-induced long-range order drag

Published on 2026-02-11 by Yuan-Hang Zhang, Chesson Sipling and Massimiliano Di Ventra @IOP SCIENCE

Abstract: Recent research has shown that memory, in the form of slow degrees of freedom, can induce a phase of long-range order (LRO) in locally-coupled fast degrees of freedom, producing power-law distributions of avalanches. In fact, such memory-induced LRO (MILRO) arises in a wide range of physical systems. Here, we show that MILRO can be transferred to coupled systems that have no memory of their own. As an example, we consider a stack of layers of spins with local feedforward couplings: only the firs[...]


Our summary: Memory-induced long-range order (MILRO) can propagate through coupled systems lacking their own memory. MILRO enables downstream layers to maintain intra-layer long-range order despite being memory-free. This mechanism has implications for neuromorphic systems and information flow in the brain cortex.

Memory-induced long-range order, power-law distributions, coupled systems, collective activity

Publication

Event-Driven Computer Vision with Spiking Transformers for Energy-Efficient Edge Perception in Sustainable Water Conservancy and Urban Water Utilities

Published on 2026-02-03 by Jing Liu, Hong Liu, Yangdong Li @MDPI

Abstract: Digital transformation in water conservancy and urban water utilities demands perception systems that are accurate, fast, and energy-efficient and maintainable over long service lifecycles at the edge. We present HydroSNN, a neuromorphic computer-vision framework that couples an event-driven sensing pipeline with a spiking-transformer backbone to support monitoring of canals, reservoirs, treatment plants, and buried pipeline networks. By reducing always-on compute and unnecessary data movement, [...]


Our summary: HydroSNN is a neuromorphic computer-vision framework designed for energy-efficient monitoring of water infrastructure. It utilizes an event-driven sensing pipeline and a spiking-transformer backbone to enhance accuracy and reduce operational energy use. The framework introduces novel components for improved performance and sustainability in edge perception systems.

Event-Driven, Spiking Transformers, Energy-Efficient, Water Utilities

Publication

Systems and methods for current/voltage controlled neuromorphic computing in artificially created nanoscopic magnetic honeycomb lattice

Patent published on the 2026-01-29 in WO under Ref WO2026024923 by THE CURATORS OF THE UNIV OF MISSOURI [US] (Singh Deepak K [us], Nair Satish S [us])

Abstract: A computing element is provided. The computing element includes a) an artificial lattice comprising a multiplicity of connecting elements separated by pores; b) a plurality of input channels attached to the artificial lattice each configured to provide at least one of an electrical current and voltage to the artificial lattice; c) a plurality of readout channels attached to the artificial lattice, wherein each readout channel of the plurality of readout channels is connected to a common ground; [...]


Our summary: This document describes a computing element utilizing a magnetic honeycomb lattice. It features input channels for electrical current and voltage, and readout channels connected to a common ground. Sensing elements are integrated to read outputs from the lattice configuration.

neuromorphic computing, magnetic lattice, electrical current, sensing elements

Patent

Memristor Synapse&mdash;A Device-Level Critical Review

Published on 2026-01-28 by Sridhar Chandrasekaran, Yao-Feng Chang, Firman Mangasa Simanjuntak @MDPI

Abstract: The memristor has long been known as a nonvolatile memory technology alternative and has recently been explored for neuromorphic computing, owing to its capability to mimic the synaptic plasticity of the human brain. The architecture of a memristor synapse device allows ultra-high-density integration by internetworking with crossbar arrays, which benefits large-scale training and learning using advanced machine-learning algorithms. In this review, we present a statistical analysis of neuromorphi[...]


Our summary: This review analyzes memristor synapses for neuromorphic computing, emphasizing their ability to mimic synaptic plasticity. It discusses device-level characteristics and applications in hardware neural networks and highlights future directions in healthcare. The study also covers statistical trends in memristive systems from 2018 to 2025.

Memristor, Neuromorphic Computing, Synaptic Plasticity, Optoelectronic Synapses

Publication

Temas tratados: Computación neuromórfica, red neuronal con picos, neurona con fugas de integración y disparo, memristor, plasticidad sináptica, retardo axonal, neurona de silicio, VLSI analógico, aprendizaje Hebbian, arquitecturas no von Neumann, procesamiento de la información basado en eventos, computación paralela masiva, ISO/IEC 30170, IEEE 802154, IEC 61966-2-1, ISO/IEC 24765 e ISO 26262.

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