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

5. Models of point neuronal dynamic

Chapter 5

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, Countess of Lovelace, the daughter of poet , was a visionary English mathematician, considered by many as “the first computer programmer” for writing an algorithm for a mechanical computer (”analytical engine”) designed by in the mid- 1800s. Ada was quoted back in 1815, saying:

"I have my hopes, and very distinct ones too, of one day, getting cerebral phenomena such that I can put them into mathematical equations. In short, a law or laws for the mutual actions of the molecules of brain... I hope to bequeath to the generations a calculus of the nervous system."

Learn more about the story of Ada Lovelace:

About 100 years later,provided one of the most widely utilized differential models for neural activity: the passive membrane model, later extended to the Leaky Integrated-and-Fire (LIF) model. Another important development is the Izhikevich neuron model, proposed by in 2003, offering a quadratic LIF with a recovery variable. A bio-plausible model was suggested by and in 1952: The HH model, for which the authors were awarded the Nobel Prize in Physiology in 1963. The HH model provides a detailed differential description of neuronal dynamics and it is the principal mathematical description used for biologically plausible neuronal simulations. This chapter will discuss some of the mathematical formulations of these models to the level appropriate for this book’s scope. Due to the importance of the LIF model, it will be more profoundly discussed.

Louis Lapicque
Eugene Izhikevich
Alan Hodgkin
Andrew Huxley
Ada Augusta
Lord Byron
Charles Babbage
Ada Lovelace (source: wikipedia)