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Related Concept Videos

Neural Circuits01:25

Neural Circuits

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.
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Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
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The Role of Ion Channels in Neuronal Computation

A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential.
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Neuron Structure01:30

Neuron Structure

Neurons are the main type of cell in the nervous system that generate and transmit electrochemical signals. They primarily communicate with each other using neurotransmitters at specific junctions called synapses. Neurons come in many shapes that often relate to their function, but most share three main structures: an axon and dendrites that extend out from a cell body.
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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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Neuromorphic silicon neuron circuits.

Giacomo Indiveri1, Bernabé Linares-Barranco, Tara Julia Hamilton

  • 1Institute of Neuroinformatics, University of Zurich and ETH Zurich Zurich, Switzerland.

Frontiers in Neuroscience
|July 13, 2011
PubMed
Summary
This summary is machine-generated.

This study details hardware implementations of spiking neurons for diverse applications like neural system modeling and brain-machine interfaces. It reviews common circuit designs and presents experimental results from fabricated VLSI chips.

Keywords:
adaptive exponentialanalog VLSIcircuitconductance basedintegrate and firelog-domainspikingsubthreshold

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Area of Science:

  • Neuroscience
  • Electrical Engineering
  • Computer Science

Background:

  • Hardware implementations of spiking neurons are crucial for advanced applications.
  • Circuit solutions for silicon neurons must align with specific application needs.

Purpose of the Study:

  • To describe common building blocks and techniques for silicon neuron circuits.
  • To provide an overview of various neuromorphic silicon neurons and their computational models.
  • To compare design methodologies and present experimental validation.

Main Methods:

  • Reviewing common circuit implementation techniques for spiking neurons.
  • Categorizing neuromorphic silicon neurons based on computational models (e.g., Hodgkin-Huxley, adaptive integrate-and-fire).
  • Comparing design approaches and validating with experimental results from fabricated VLSI chips.

Main Results:

  • Demonstrated a range of silicon neuron designs implementing diverse computational models.
  • Presented experimental validation of these designs through measurements from fabricated VLSI chips.
  • Highlighted the adaptability of circuit solutions to specific application requirements.

Conclusions:

  • Hardware implementations of spiking neurons offer versatile solutions for neural system modeling, real-time systems, and brain-machine interfaces.
  • A variety of computational models can be effectively implemented in silicon neurons.
  • Experimental results confirm the functionality and applicability of different neuromorphic silicon neuron designs.