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Design Example: Capacitance Multiplier Circuit01:20

Design Example: Capacitance Multiplier Circuit

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In integrated circuit technology, a capacitance multiplier is often utilized to produce a larger capacitance value when a small physical capacitance falls short. This is achieved by a circuit that multiplies capacitance values by a factor of up to 1000, such that a 10-pF capacitor can replicate the performance of a 100-nF capacitor.
The circuit illustrated in Figure 1 below incorporates two op-amps, with the first operating as a voltage follower and the second acting as an inverting amplifier.
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Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Overview of Synapses01:25

Overview of Synapses

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A synapse is a specialized structure where two neurons connect, allowing them to pass an electrical or chemical signal to another neuron. It is the point of communication between neurons. The term "synapse" is derived from the Greek word "synapsis," which means "conjunction." The entire process of neural communication revolves around the synapse. When activated, a neuron releases chemicals known as neurotransmitters into the synapse. These neurotransmitters cross the synapse and bind to...
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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: Jul 20, 2025

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
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A Compact and Low-Power SoC Design for Spiking Neural Network Based on Current Multiplier Charge Injector Synapse.

Malik Summair Asghar1,2, Saad Arslan3, Ali A Al-Hamid1

  • 1Department of Electronics, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea.

Sensors (Basel, Switzerland)
|July 29, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a compact analog system-on-chip (SoC) for low-power spiking neural networks (SNNs) in IoT devices. The novel design achieves high accuracy with significantly reduced power consumption compared to digital counterparts.

Keywords:
CMOSInternet of Thingsartificial intelligenceartificial neural networksleaky integrate and fireneuromorphicspiking neural network

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Area of Science:

  • Neuromorphic Engineering
  • Integrated Circuit Design
  • Artificial Intelligence Hardware

Background:

  • Spiking Neural Networks (SNNs) offer energy-efficient computation for AI tasks.
  • Low-power System-on-Chip (SoC) designs are crucial for Internet of Things (IoT) applications.
  • Existing SNN implementations often face challenges in power consumption and chip area.

Purpose of the Study:

  • To present a compact analog SoC implementation of an SNN optimized for low-power IoT applications.
  • To develop novel analog neuron and synaptic circuits for reduced power and area.
  • To demonstrate the performance and efficiency of the proposed SNN SoC against a digital implementation.

Main Methods:

  • Designed analog neuron and synaptic circuits using a current multiplier charge injector (CMCI) for synapses and an asynchronous structure for neurons.
  • Implemented the SNN SoC using a 65 nm CMOS process.
  • Trained the SNN chip on the MNIST dataset and compared its performance (area, power) against a Field-Programmable Gate Array (FPGA) based digital SoC.

Main Results:

  • The proposed synapse circuit (CMCI) reduces power consumption and chip area, offering design scalability.
  • The asynchronous neuron circuit enhances sensitivity to synaptic inputs and energy efficiency.
  • The SNN chip achieved 96.56% classification accuracy on the MNIST dataset.
  • The fabricated chip occupies 0.96 mm² and consumes 530 μW average power, which is 200x lower than its digital counterpart.

Conclusions:

  • The developed analog SNN SoC provides a highly power-efficient and compact solution for AI in IoT.
  • The novel circuit designs for neurons and synapses enable significant reductions in power and area.
  • This work demonstrates a viable pathway for deploying advanced AI capabilities on resource-constrained edge devices.