Computational Neuroscience - book notes

    Chap 1: Intro
  • Some phenomena
  • Computation
  • Emergent phenomena
  • Why care?
  • AI,ML,Neuroscience
  • How to read + resources
    Chap 2: Neurons
  • Basic biology
  • Dynamics
  • Activation outputs
  • Math
  • Individual neurons
  • Detector review
  • Appendix
    Chap 3: Networks
  • Neocortex biology
  • Categorization | Distributed representations
  • Bidirectional dynamics
  • Inhibition | Regulation
  • Appendix
    Chap 4: Learning
  • Synaptic Plasticity
  • XCAL model
  • Self-organized learning
  • Error-driven learning
  • Leabra (framework)
  • Appendix
    Chap 5: Areas of the Brain
  • Anatomy navigation
  • Major areas
  • Perception | attention
  • Motor control - motor cortext - basal ganglia - cerebellum
  • Memory - temporal cortex and hippocampus
  • Language
  • Executive functions
    Chap 8: Memory
  • Episodic
  • Hippocampus - patterns
  • Complementary learning systems
  • Familiarity - recognition
  • Priming
  • Appendix
    Chap 9: Language
  • Biology
  • Reading | Dyslexia
  • Spelling to sound maps
  • Latent semantics
  • Sentence gestalt
  • Language modeling - next steps
    Chap 10-Executive Function
  • PFC/BG biology
  • Phasic DA | temporal credit assignment
  • PBWM computation model
  • Stroop model
  • A-not-B
  • SIR model
  • N-Back task
  • Hierarchical organization
  • Affective influences
  • More functions
  • Alternates
  • Summary