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Related Concept Videos

Neuroplasticity01:01

Neuroplasticity

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Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
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Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
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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...
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Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability...
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Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
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Neurotransmitters are integral to the brain's communication system, enabling neurons to transmit signals across synapses. This chemical exchange underpins various cognitive functions, including memory processes. The role of neurotransmitters in memory is multifaceted, influencing the encoding, consolidation, and retrieval of memories through their action on different neural circuits.
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Flash Memory for Synaptic Plasticity in Neuromorphic Computing: A Review.

Jisung Im1, Sangyeon Pak2, Sung-Yun Woo1

  • 1School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.

Biomimetics (Basel, Switzerland)
|February 25, 2025
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Summary
This summary is machine-generated.

Neuromorphic electronics using flash memory offer energy-efficient in-memory computing. This review explores NOR, AND, and NAND flash advancements for optimized neural network applications.

Keywords:
AND flash memoryNAND flash memoryNOR flash memoryflash memoryin-memory computingneuromorphicsynaptic device

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Area of Science:

  • Neuromorphic Engineering
  • Computer Science
  • Materials Science

Background:

  • The exponential growth of data necessitates energy-efficient solutions for storage and processing.
  • Neuromorphic electronics, mimicking biological neural systems, offer a path beyond the von Neumann bottleneck.
  • In-memory computing with synaptic devices promises reduced energy consumption for computational tasks.

Purpose of the Study:

  • To review recent advancements in neuromorphic computing utilizing NOR, AND, and NAND flash memory.
  • To provide a comprehensive overview of flash memory-based neuromorphic computing.
  • To highlight the potential of flash memory in optimizing energy efficiency for neural networks.

Main Methods:

  • Analysis of multi-bit non-volatility and biologically inspired features in synaptic devices.
  • Exploration of Ohm's law for energy reduction in multiplication and accumulation operations.
  • Review of array architectures, operational methods, and electrical properties of NOR, AND, and NAND flash memory.

Main Results:

  • Flash memory, particularly NOR, AND, and NAND types, demonstrates significant potential for large-scale data storage in neuromorphic systems.
  • Flash memory-based synaptic devices can effectively reduce energy consumption in essential computing operations.
  • Integration of flash memory enables efficient implementation of various neural network designs.

Conclusions:

  • Flash memory is a competitive technology for energy-efficient neuromorphic computing and in-memory processing.
  • Understanding flash memory characteristics is crucial for optimizing its application in diverse neural network architectures.
  • This review provides insights for advancing flash memory-based neuromorphic computing across various applications.