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Related Concept Videos

System of Memory01:23

System of Memory

Memory is categorized into three major systems: sensory memory, short-term memory (STM), and long-term memory (LTM). These systems differ in their capacity and the duration for which they can hold information. Sensory memory captures raw sensory input from the environment, holding it for just a few seconds or less. For example, on hearing a brief, loud sound, like a car horn honking, the sound seems to linger in the mind for a moment even after it stops. This is an instance of sensory memory...
Storage01:23

Storage

A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze each...
Understanding Memory01:19

Understanding Memory

Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or playing an...
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
MOS Capacitor01:25

MOS Capacitor

A Metal-Oxide-Semiconductor (MOS) capacitor is a fundamental structure used extensively in semiconductor device technology, particularly in the fabrication of integrated circuits and MOSFETs (metal-oxide-semiconductor field-effect transistors). The MOS capacitor consists of three layers: a metal gate, a dielectric oxide, and a semiconductor substrate.
The metal gate is typically made from highly conductive materials such as aluminum or polysilicon. Beneath the metal gate lies a thin layer of...

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Updated: Jun 6, 2026

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
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Published on: March 9, 2019

Reconfigurable Selector-Only Memory (SOM) for Scalable Neuromorphic Computing.

Jin-Yu Wen1,2, Chuan-Qi Yi1, Ya-Ru Zhang1

  • 1School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|June 4, 2026
PubMed
Summary
This summary is machine-generated.

Selector-only memory (SOM) offers a scalable solution for neuromorphic devices, overcoming limitations in artificial intelligence continual learning. This novel device enables efficient neuron-synapse reconfigurability and improved performance in AI tasks.

Keywords:
(selector‐only memorygrowing‐when‐required (GWR) continual learning)leaky integrate‐and‐fire (LIF) neuronovonic threshold voltagereconfigurable neuromorphic computing

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Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits
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Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

Published on: April 15, 2015

Area of Science:

  • Materials Science
  • Computer Science
  • Electrical Engineering

Background:

  • Scalable reconfigurable neuromorphic devices are essential for advancing artificial intelligence (AI), particularly for continual learning.
  • Existing devices face scalability challenges due to limited operating margins and manufacturing immaturity.
  • Neuromorphic hardware aims to mimic the human brain's efficiency and learning capabilities.

Purpose of the Study:

  • To propose and demonstrate a novel, scalable device concept for reconfigurable neuromorphic hardware.
  • To address the limitations of current neuromorphic devices in terms of scalability and process maturity.
  • To enable efficient neuron-synapse reconfigurability within a single device.

Main Methods:

  • Introduction of Selector-Only Memory (SOM) with decoupled volatile threshold switching and nonvolatile threshold window.
  • Demonstration of an Indium-doped Germanium Selenide (In-doped GeSe) SOM device.
  • Implementation of a capacitor-free leaky integrate-and-fire neuron leveraging intrinsic parasitic capacitance.
  • Development of a one-shot subthreshold-conductance readout method for synaptic plasticity.
  • Utilizing a unified reverse-subthreshold pulse scheme for programming conductance states.

Main Results:

  • The In-doped GeSe SOM enables neuron-synapse reconfigurability in a single cell.
  • Successful validation of neuron functionalities including all-or-none firing and integrate-and-fire dynamics.
  • Achieved 16 distinguishable synaptic conductance states with long-term stability (>10^4 s).
  • SOM-based simulations on a Growing-When-Required MNIST task showed a 2.67x accuracy increase and 58% size reduction after rollback.

Conclusions:

  • Selector-Only Memory (SOM) presents a promising device concept for scalable, reconfigurable neuromorphic hardware.
  • The proposed device overcomes key scalability constraints of existing neuromorphic technologies.
  • SOM technology facilitates efficient implementation of both neuronal and synaptic functions, advancing AI capabilities.