A network model of tail withdrawal in Aplysia (White et al 1993)

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Accession:34606
The contributions of monosynaptic and polysynaptic circuitry to the tail-withdrawal reflex in the marine mollusk Aplysia californica were assessed by the use of physiologically based neural network models. Effects of monosynaptic circuitry were examined by the use of a two-layer network model with four sensory neurons in the input layer and one motor neuron in the output layer. Results of these simulations indicated that the monosynaptic circuit could not account fully for long-duration responses of tail motor neurons elicited by tail stimulation. A three-layer network model was constructed by interposing a layer of two excitatory interneurons between the input and output layers of the two-layer network model. The three-layer model could account for long-duration responses in motor neurons. Sensory neurons are a known site of plasticity in Aplysia. Synaptic plasticity at more than one locus modified dramatically the input-output relationship of the three-layer network model. This feature gave the model redundancy in its plastic properties and points to the possibility of distributed memory in the circuitry mediating withdrawal reflexes in Aplysia. Please see paper for more results and details.
Reference:
1 . White JA, Ziv I, Cleary LJ, Baxter DA, Byrne JH (1993) The role of interneurons in controlling the tail-withdrawal reflex in Aplysia: a network model. J Neurophysiol 70:1777-86 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Aplysia;
Cell Type(s): Aplysia sensory neuron; Aplysia interneuron; Aplysia motor neuron;
Channel(s): I Na,t; I L high threshold; I N; I A; I K; I Calcium; I A, slow;
Gap Junctions:
Receptor(s): AMPA;
Gene(s):
Transmitter(s):
Simulation Environment: SNNAP;
Model Concept(s): Activity Patterns; Bursting; Synaptic Plasticity; Facilitation; Invertebrate;
Implementer(s): Baxter, Douglas;
Search NeuronDB for information about:  AMPA; I Na,t; I L high threshold; I N; I A; I K; I Calcium; I A, slow;
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White
README.txt
cs.R *
LP.neu
LP_2_MN_f.cs
LP_2_MN_f.fAt
LP_2_MN_f.Xt *
LP_2_MN_s.cs
LP_2_MN_s.fAt
LP_2_MN_s.Xt *
LP_Kv.A *
LP_Kv.B
LP_Kv.vdg
LP_leak.vdg
LP_Na.A
LP_Na.B
LP_Na.vdg
MN.neu
MN_Ca.A
MN_Ca.B
MN_Ca.vdg
MN_Kv.A
MN_Kv.B
MN_Kv.vdg
MN_leak.vdg
MN_Na.A
MN_Na.B
MN_Na.vdg
SN.neu
SN_10_Hz.trt
SN_2_LP.cs
SN_2_LP.fAt
SN_2_LP.Xt *
SN_2_MN.cs
SN_2_MN.fAt
SN_2_MN.Xt *
SN_Ca.A
SN_Ca.B
SN_Ca.vdg
SN_Ka.A
SN_Ka.B
SN_Ka.vdg
SN_Ks.A
SN_Ks.vdg
SN_Kv.A *
SN_Kv.B
SN_Kv.vdg
SN_leak.vdg
SN_Na.A
SN_Na.B
SN_Na.vdg
vdg.R *
White_control.jpg
White_control.ntw
White_control.ous
white_control.smu
                            
README_White.txt

This simulations reproduces the model published
in:

White, J.A., Ziv, I., Baxter, Cleary, L.J., Baxter, D.A.
and Byrne, J.H.  (1993)  The role of interneurons in 
controlling the tail-withdrawal reflex in Aplysia: A 
network model.  J. Neurophysiol. 70 1777-1786..  

The results of a simulation are illustrated in 
White_control.jpg.

Example use:

Start SNNAP (double click on the SNNAP.jar file)
click on "Run Simulation"
Then in the new window "File"->"Load Simulation"
browse to load white_control.smu and click "Start"
See http://snnap.uth.tmc.edu/ to download SNNAP



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