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
Powered by GitBook
On this page
  • The neuron doctrine
  • Abstractions and modeling

Was this helpful?

  1. I. Introduction
  2. 1. Introducing the perspective of the scientist

Take away lessons

PreviousBrain modelingNext2. Introducing the perspective of the computer architect

Last updated 3 years ago

Was this helpful?

The neuron doctrine

The neuron doctrine: The neuron is the essential constituent of the nervous system and the fundamental unit of perception.

A Neuron is a nerve cell, comprised of a soma (site of signals integration), dendrites (signal input pathways), and axon (signal output pathway). Neurons typically communicate with other neurons with spikes.

The neuron doctrine: The neuron is the essential constituent of the nervous system and the fundamental unit of perception.

Neural coding: Neurons encode information with spikes. Encoding can be based on binary coding (responding when something happens), rate coding (information as a function of spiking rate), time-to-spike (information as a function of the exact time at which a neuron fired), and with population coding (information as a dynamical pattern of firing).

Abstractions and modeling

The brain can be investigated from different abstraction levels ranging from the molecular level, neurons, and synapses to networks, maps, systems, and beings.

Networks and emergent behavior: A function can emerge from the joint activation patterns in neural networks. Emergent behavior can- not be observed by studying single entities.

Bottom-up and top-down modeling: In bottom-up brain modeling, large-scale networks are defined using its elementary building blocks. In top-down brain modeling, low-level details are constrained to higher-level observations.