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Critical Region, Critical Values and Significance Level01:16

Critical Region, Critical Values and Significance Level

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The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in  probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the...
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The Synapse02:47

The Synapse

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Neurons communicate with one another by passing on their electrical signals to other neurons. A synapse is the location where two neurons meet to exchange signals. At the synapse, the neuron that sends the signal is called the presynaptic cell, while the neuron that receives the message is called the postsynaptic cell. Note that most neurons can be both presynaptic and postsynaptic, as they both transmit and receive information.
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Review and Preview01:10

Review and Preview

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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
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Review and Preview01:13

Review and Preview

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Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
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Electrical Synapses01:28

Electrical Synapses

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Electrical synapses found in all nervous systems play important and unique roles. In these synapses, the presynaptic and postsynaptic membranes are very close together (3.5 nm) and are actually physically connected by channel proteins forming gap junctions.
Gap junctions allow the current to pass directly from one cell to the next. In contrast, in the chemical synapse, the neurotransmitters carry the information through the synaptic cleft from one neuron to the next. They consist of two...
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Chemical Synapses01:26

Chemical Synapses

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Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
Because chemical synapses depend on the release of neurotransmitter molecules from synaptic vesicles to pass on their signal, there is an approximately one millisecond delay between when the axon potential reaches the presynaptic terminal and when the neurotransmitter leads to opening of postsynaptic ion channels. Additionally, this signaling is...
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Related Experiment Video

Updated: Feb 13, 2026

A Method for Growing Bio-memristors from Slime Mold
07:46

A Method for Growing Bio-memristors from Slime Mold

Published on: November 2, 2017

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Memristor Synapse-A Device-Level Critical Review.

Sridhar Chandrasekaran1, Yao-Feng Chang2, Firman Mangasa Simanjuntak3,4

  • 1Micro and Nano Devices Laboratory, School of Electronics Engineering, Vellore Institute of Technology, Chennai 600127, India.

Nanomaterials (Basel, Switzerland)
|February 12, 2026
PubMed
Summary
This summary is machine-generated.

Memristor devices mimic brain plasticity for neuromorphic computing. Optoelectronic synapses using 2D materials offer enhanced control and diverse healthcare applications, advancing artificial intelligence.

Keywords:
LTPSRDPSTDPSTPmemristor synapse

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Last Updated: Feb 13, 2026

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Engineering

Background:

  • Memristors are explored for nonvolatile memory and neuromorphic computing due to their brain-like synaptic plasticity.
  • Crossbar arrays enable ultra-high-density integration of memristor synapse devices for large-scale machine learning.
  • Biomimetic properties like short-term plasticity (STP) and long-term plasticity (LTP) are crucial for hardware neural networks.

Purpose of the Study:

  • To statistically analyze neuromorphic computing device publications from 2018 to 2025, focusing on memristive systems.
  • To provide a device-level perspective on biomimetic properties (STP, LTP, STDP, SRDP) in hardware neural networks.
  • To highlight optoelectronic synapses based on 2D materials for mimicking brain plasticity via optical stimuli.

Main Methods:

  • Statistical analysis of scientific publications on neuromorphic computing devices.
  • Device-level examination of biomimetic properties in memristive systems.
  • Review of optoelectronic synapse technologies utilizing 2D materials and optical stimulation.

Main Results:

  • Memristive systems show significant research growth in neuromorphic computing.
  • Optoelectronic synapses based on 2D materials demonstrate effective mimicry of brain plasticity through optical control.
  • Practical applications include MNIST recognition, pattern recognition, and potential healthcare solutions.

Conclusions:

  • Memristor synapses, particularly optoelectronic ones, are promising for advanced AI and neuromorphic systems.
  • Optical stimulation offers novel control mechanisms for memristor-based artificial synapses.
  • Future directions point to significant healthcare applications, including artificial cognition and physiological monitoring.