Circuits that contain the Region : Aplysia

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    Models   Description
1. A network model of tail withdrawal in Aplysia (White et al 1993)
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.
2. Burst induced synaptic plasticity in Apysia sensorimotor neurons (Phares et al 2003)
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.
3. Computational Model of a Central Pattern Generator (Cataldo et al 2006)
The buccal ganglia of Aplysia contain a central pattern generator (CPG) that mediates rhythmic movements of the foregut during feeding. This CPG is a multifunctional circuit and generates at least two types of buccal motor patterns (BMPs), one that mediates ingestion (iBMP) and another that mediates rejection (rBMP). The present study used a computational approach to examine the ways in which an ensemble of identified cells and synaptic connections function as a CPG. Hodgkin-Huxley-type models were developed that mimicked the biophysical properties of these cells and synaptic connections. The results suggest that the currently identified ensemble of cells is inadequate to produce rhythmic neural activity and that several key elements of the CPG remain to be identified.
4. Homosynaptic plasticity in the tail withdrawal circuit (TWC) of Aplysia (Baxter and Byrne 2006)
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.

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