Simulation Environment: SNNAP

Note: this list includes both models hosted here at ModelDB and models where have metadata and link to the sourcecode somewhere else. These may also be viewed separately; see the browse by simulator page.

  1. A Model of Multiple Spike Initiation Zones in the Leech C-interneuron (Crisp 2009)
  2. A network model of tail withdrawal in Aplysia (White et al 1993)
  3. Burst induced synaptic plasticity in Apysia sensorimotor neurons (Phares et al 2003)
  4. Bursting activity of neuron R15 in Aplysia (Canavier et al 1991, Butera et al 1995)
  5. Caffeine-induced electrical oscillations in Aplysia neurons (Komendantov, Kononenko 2000)
  6. Computational Model of a Central Pattern Generator (Cataldo et al 2006)
  7. Computational model of the distributed representation of operant reward memory (Costa et al. 2020)
  8. Currents contributing to decision making in neurons B31-B32 of Aplysia (Hurwitz et al. 2008)
  9. Effects of Acetyl-L-carnitine on neural transmission (Lombardo et al 2004)
  10. Enhanced Excitability in Hermissenda: modulation by 5-HT (Cai et al 2003)
  11. Homosynaptic plasticity in the tail withdrawal circuit (TWC) of Aplysia (Baxter and Byrne 2006)
  12. I A in Kenyon cells resemble Shaker currents (Pelz et al 1999)
  13. Kenyon cells in the honeybee (Wustenberg et al 2004)
  14. Leech S Cell: Modulation of Excitability by Serotonin (Burrell and Crisp 2008)
  15. Minimal cell model (Av-Ron et al 1991)
  16. Morris-Lecar model of the barnacle giant muscle fiber (Morris, Lecar 1981)
  17. Multiple modes of a conditional neural oscillator (Epstein, Marder 1990)
  18. S cell network (Moss et al 2005)
  19. Serotonergic modulation of Aplysia sensory neurons (Baxter et al 1999)
  20. Spike propagation and bouton activation in terminal arborizations (Luscher, Shiner 1990)
  21. Squid axon (Hodgkin, Huxley 1952) (SNNAP)
  22. Touch Sensory Cells (T Cells) of the Leech (Cataldo et al. 2004) (Scuri et al. 2007)
http://snnap.uth.tmc.edu

SNNAP -- Simulator for Neural Networks and Action Potentials is a tool for rapid development and simulation of realistic models of single neurons and neural networks. It includes mathematical descriptions of ion currents and intracellular second messengers and ions. In addition, you can simulate current flow in multicompartment models of neurons by using the equations describing electric coupling.
Top authors for SNNAP:
Top concepts studied with SNNAP:
Top neurons studied with SNNAP:
Top currents studied with SNNAP:
Top references cited by these models:
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