Burst induced synaptic plasticity in Apysia sensorimotor neurons (Phares et al 2003)

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The Aplysia sensorimotor synapse is a key site of plasticity for several simple forms of learning. Intracellular stimulation of sensory neurons to fire a burst of action potentials at 10 Hz for 1 sec led to significant homosynaptic depression of postsynaptic responses. During the burst, the steady-state depressed phase of the postsynaptic response, which was only 20% of the initial EPSP of the burst, still contributed to firing the motor neuron. To explore the functional contribution of transient homosynaptic depression to the response of the motor neuron, computer simulations of the sensorimotor synapse with and without depression were compared. Depression allowed the motor neuron to produce graded responses over a wide range of presynaptic input strength. Thus, synaptic depression increased the dynamic range of the sensorimotor synapse and can, in principle, have a profound effect on information processing. Please see paper for results and details.
1 . Phares GA, Antzoulatos EG, Baxter DA, Byrne JH (2003) Burst-induced synaptic depression and its modulation contribute to information transfer at Aplysia sensorimotor synapses: empirical and computational analyses. J Neurosci 23:8392-401 [PubMed]
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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 motor neuron;
Channel(s): I Na,t; I N; I A; I K; I Calcium; I A, slow;
Gap Junctions:
Receptor(s): AMPA;
Simulation Environment: SNNAP;
Model Concept(s): Synaptic Plasticity; Action Potentials; Facilitation; Depression; Invertebrate; Sensory processing;
Implementer(s): Phares, Gregg A [gregg.a.phares at uth.tmc.edu];
Search NeuronDB for information about:  AMPA; I Na,t; I N; I A; I K; I Calcium; I A, slow;
cs.R *
LP_2_MN_cs.R *
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LP_2_MN_s.cs *
LP_2_MN_s.fAt *
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SN_2_LP.cs *
SN_2_LP.fAt *
SN_2_LP.Xt *
		>>    module's name: R		>>

R:		> 	 distributions	                                >

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>	1		        >	R=0.0		         (1)	>
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	0	>percent%<	>  R = Gaussian(g, percent% x g/3)	>
				>  It's in (g-g*percent%, g+g*percent%)	>
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	20	>step size<	>  In how many steps is R renewed	>