Neural Modeling

After attending our local SeattleAI meeting and discussing neural network frameworks I decided to put together my notes on this subject.

What would one need to know to do neural modeling?

This paper (Introduction to Computational Neural Modeling for Computer Scientists and Mathematicians) provides a list of principles that govern well-practiced neural modeling:

  • Knowledge of neuroscience principles
  • Knowledge of computational time and space complexities
  • Awareness of computational resources
  • Well-researched neurological parameters
  • Well-planned and implemented model design
  • Documentation discipline (both parameter sources and coding)
  • Knowledge of statistical analysis techniques
  • Patience


See Neural Modeling: Neuron


  • See Neural Modeling: Synaptic Plasticity
  • See Neural Modeling: Synaptic Connectivity
  • Post synaptic potential (PSP) models
  • Neurotransmitters (only four most significant types out of hundreds discovered):
    • AMPA -- fast excitatory
    • NMDA -- slow excitatory
    • GABAA -- fast inhibitory
    • GABAB -- slow inhibitory
  • Transmission delays
  • Plasticity: presynaptic/postsynaptic
  • Operations: AND, OR, XOR, NOT, SUM, MUL, MAX


  • Learning algorithms:
    • Synaptic weight changes:
      • Hebb's rule: if S sends a pulse at time t and R fires at time t+1, then that synapse becomes more effective at firing R in the future
      • Milner's modification: if S sends a pulse at time t and R does not fire at time t+1, then that synapse becomes less effective in firing R in the future
    • Connection changes (more/less connections with a particular neuron)
    • Neuron changes (birth/death)
  • Learning related problems:
    • Under-/over-learning; under-/over-fitting
    • Catastrophic interference
  • Sensitization/habituation
  • Types of learning: autonomous, continuous, on-line, supervised/unsupervised

Initial configuration



Overall models

Modeling tools and simulators

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