Neuromorphic Engineering Book
  • Welcome
  • Preliminaries
    • About the author
    • Preface
    • A tale about passion and fear
    • Before we begin
  • I. Introduction
    • 1. Introducing the perspective of the scientist
      • From the neuron doctrine to emergent behavior
      • Brain modeling
      • Take away lessons
    • 2. Introducing the perspective of the computer architect
      • Limits of integrated circuits
      • Emerging computing paradigms
      • Brain-inspired hardware
      • Take away lessons
      • Errata
    • 3. Introducing the perspective of the algorithm designer
      • From artificial to spiking neural networks
      • Neuromorphic software development
      • Take home lessons
  • II. Scientist perspective
    • 4. Biological description of neuronal dynamics
      • Potentials, spikes and power estimation
      • Take away lessons
      • Errata
    • 5. Models of point neuronal dynamic
      • Tutorial - models of point neuronal processes
        • The leaky integrate and fire model
        • The Izhikevich neuron model
        • The Hodgkin-Huxley neuron model
      • Synapse modeling and point neurons
      • Case study: a SNN for perceptual filling-in
      • Take away lessons
    • 6. Models of morphologically detailed neurons
      • Morphologically detailed modeling
      • The cable equation
      • The compartmental model
      • Case study: direction-selective SAC
      • Take away lessons
    • 7. Models of network dynamic and learning
      • Circuit taxonomy, reconstruction, and simulation
      • Case study: SACs' lateral inhibition in direction selectivity
      • Neuromorphic and biological learning
      • Take away lessons
      • Errate
  • III. Architect perspective
    • 8. Neuromorphic Hardware
      • Transistors and micro-power circuitry
      • The silicon neuron
      • Case study: hardware - software co-synthesis
      • Take away lessons
    • 9. Communication and hybrid circuit design
      • Neural architectures
      • Take away lessons
    • 10. In-memory computing with memristors
      • Memristive computing
      • Take away lessons
      • Errata
  • IV. Algorithm designer perspective
    • 11. Introduction to neuromorphic programming
      • Theory and neuromorphic programming
      • Take away lessons
    • 12. The neural engineering framework
      • NEF: Representation
      • NEF: Transformation
      • NEF: Dynamics
      • Case study: motion detection using oscillation interference
      • Take away lessons
      • Errate
    • 13. Learning spiking neural networks
      • Learning with SNN
      • Take away lessons
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  1. II. Scientist perspective
  2. 5. Models of point neuronal dynamic

Take away lessons

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Point neuron: A neuron model with no spatial structure or morphology.

The leaky integrate and fire model: A model representing a neuron with a combination of a ”leaky” resistor and a capacitor. The cell membrane acts as a leaky capacitor which models the membrane capability to separate ions. A resistor is used to model the ion channels. The most abundant neuronal model for large-scale simulations.

The Izhikevich neuron model: A quadratic LIF type model with a recovery variable, able to replicate several characteristics of biological neurons while remaining computationally efficient.

The Hodgkin-Huxley model: The most famous and influential biological plausible neuron model. It models a neuron’s ion channels’ conductances in terms of activation and inactivation variables. The cell membrane is represented by a capacitor, and voltage-gated and leak ion channels. The electrochemical gradients driving the flow of ions are represented by batteries (E). The model takes into account Na+, K+ ion channels, and a leakage channel.

Model comparison. Learn the tradeoff between the number of neuronal features (e.g. spike frequency adaptation and phasic spiking) and the required computational resources (needed to simulate the model for 1ms) for the LIF, HH, and the Izhikevich models.