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Enteric Nervous Network Explorer

This simulator by Prof. Joel Bornstein implements a small network of neurons consisting of a model output neuron receiving synaptic input from 3 different input neurons. The network is designed to Study the interactions of different types of synaptic potentials in a realistic enteric neuron and the effects of varying the voltage-dependent ion channels in the output neuron membrane. The simulator allows a user to specify the type of synaptic potential generated by an input neuron with the options being a fast excitatory synaptic potential (EPSP) mediated by acetylcholine acting on nicotinic acetylcholine receptors (Foong et al., 2015), a depolarizing synapse mediated by GABA acting on GABAA receptors and a depolarizing synapse mediated by GABAC receptors (Koussoulas et al., 2018).

Usage

  1. The strength or weight of each synaptic input can be set by the user.

  2. The timing of the activation of each input neuron can also be set by the user.

  3. The other parameter that can be set by the user is the contribution to the output neuron’s response to depolarization of a voltage-dependent potassium channel in the M-current family, Kv7.2 (or KCNQ2).

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    Bornstein_usage

See Chambers et al (Chambers et al., 2014b) for a review of approaches to modelling the enteric nervous system.

Each neuron in the network is simulated as a compartmental model using the open access Neuron simulation system with individual elements (ion channels, synaptic weights, neurotransmitters, etc) coded in Python. The neuron models are based on a published model of the intrinsic sensory neurons of the enteric nervous system (Chambers et al., 2014a), with the ion channels setting neuronal excitability and firing properties set to simulate firing in mouse myenteric interneurons and motor neurons. These include Nav1.3, Nav1.7, a delayed rectifier K channel equivalent to the Hoidgkin-Huxley channel, an A-current channel, a general background conductance and the Kv7.2 channel.

All parameters (including maximum conductance, voltage threshold and time constants of activation, inactivation and decay) of these channels can be varied independently in the code by modifying the Python scripts that define them, as can their reversal potentials.