ModelDB: Homosynaptic plasticity in the tail withdrawal circuit (TWC) of Aplysia (Baxter and Byrne 2006)

Homosynaptic plasticity in the tail withdrawal circuit (TWC) of Aplysia (Baxter and Byrne 2006)

Accession: 83472

The tail-withdrawal circuit of Aplysia provides a useful model system for investigating synaptic dynamics. Sensory neurons within the circuit manifest several forms of synaptic plasticity. Here, we developed a model of the circuit and investigated the ways in which depression (DEP) and potentiation (POT) contributed to information processing. DEP limited the amount of motor neuron activity that could be elicited by the monosynaptic pathway alone. POT within the monosynaptic pathway did not compensate for DEP. There was, however, a synergistic interaction between POT and the polysynaptic pathway. This synergism extended the dynamic range of the network, and the interplay between DEP and POT made the circuit respond preferentially to long-duration, low-frequency inputs.
Reference: Baxter DA, Byrne JH (2007) Short-term plasticity in a computational model of the tail-withdrawal circuit in Aplysia Neurocomputing 70(10-12):1993-1999

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Model Information (Click on a link to find other models with that property)

Model Type:	Network;
Brain Region(s)/Organism:	Aplysia;
Cell Type(s):	Aplysia sensory neuron; Aplysia interneuron; Aplysia motor neuron;
Channel(s):	I N; I K; I Sodium; I Potassium;
Gap Junctions:
Receptor(s):	AMPA;
Gene(s):
Transmitter(s):	Glutamate;
Simulation Environment:	SNNAP;
Model Concept(s):	Synchronization; Synaptic Plasticity; Short-term Synaptic Plasticity; Action Potentials; Facilitation; Post-Tetanic Potentiation; Depression;
Implementer(s):	Baxter, Douglas;

Search NeuronDB for information about: AMPA; I K; I N; I Potassium; I Sodium; Glutamate;

Model files Help downloading and running models

Baxter_Byrne_Neurocomput_06

README.html

This simulation reproduces the model published 
in:

Baxter DA, Byrne JH (2006) Short-term plasticity in a 
computational model of the tail-withdrawal circuit in Aplysia 
Neurocomputing in press

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 and load the file TWC.smu
and click "Start"

This reproduces a figure similar to Fig 2B however the spiking
frequency in the sensory neurons is higher.

See  http://snnap.uth.tmc.edu/  to download SNNAP

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