Neurosim Modules
Neurosim contains seven modules:
(links show screenshots of simulations with default parameters)
Hodgkin-Huxley
HODGKIN-HUXLEY is a direct implementation of the original Hodgkin-Huxley model of a nerve impulse. Stimuli can be applied in either current clamp or voltage clamp mode, each with square or ramp waveform and user-defined amplitude and timing. A wide range of phenomena can be simulated, including refractory period, threshold accommodation, voltage clamp tail currents, single channel patch clamp conductances and many others. Various drugs can be applied, and the temperature and ionic concentrations can be varied.
Goldman
GOLDMAN simulates the Goldman-Hodgkin-Katz constant field equation. This allows students to explore the relationship between ionic concentration and equilibrium potential, and relative ionic permeability and the membrane potential. It explicitly calculates the Nernst and Goldman equations for a range of ionic parameters.
Membrane Patch
MEMBRANE PATCH simulates the kinetic properties of single ion channels. Three simple models are supplied: a two-state open/shut channel; a 3-state agonist-activated channel (shut/unbound, shut/bound, open/bound); and a 3-state antagonist blocked channel (shut, open, blocked). The program can also model a channel with up to 5 states with user-defined transition rate constants. Open-time and shut-time histograms can be displayed, with multi-exponential curves superimposed. A maximum-likelihood routine can optimize curve fit parameters.
Passive Conduction
PASSIVE CONDUCTION simulates the non-spiking conduction properties (cable properties) of an “ideal” axon or dendrite (infinite length and uniform diameter). The user specifies the membrane characteristics (membrane resistance, axoplasmic resistance and membrane capacitance) and diameter, and the simulation shows how the voltage varies over time and distance in response to a current pulse. The membrane potential can be displayed either as a graph of potential against time at specified recording locations relative to the stimulus, or the complete time-varying potential profile along the axon on either side of the stimulus location.
Network
NETWORK allows the user to construct arbitrary circuits of neurons interconnected by synapses. Neurons can implement an integrate-and-fire or Hodgkin-Huxley formalism, or a mixture of the two. Compartmental models can be generated by linking neurons (compartments) with electrical synapses. Many different types of synapses can be defined, including chemical synapses with different reversal potentials, synaptic strengths and facilitation properties, and electrical synapses with different rectification properties. Chemical synapses can be voltage dependent, or have Hebbian properties. A very wide range of circuit phenomena can be demonstrated, including endogenous and network oscillators, lateral inhibition in sensory systems, and many others.
Advanced HH
ADVANCED HH is a do-it-yourself single-compartment neuron model in which both voltage-dependent and synaptic conductances can be incorporated. It is intended for investigating more complex cellular systems than that of the standard HH model. Up to nine voltage-dependent channel types can be included, each with user-defined maximum conductance and equilibrium potential, and with activation and inactivation kinetics defined using a built-in equation editor. Intracellular calcium concentration can be modelled, and any channel can be made calcium dependent. Neurons designed in this model can be imported into the Network model and used within circuits.
Wilson Cowan
WILSON-COWAN is a firing-rate model that simulates a large but spatially-localized population of neurons containing an interacting mix of excitatory and inhibitory neurons. Individual neurons are not modelled, but the overall activity is determined by setting general properties such as threshold, threshold variability, and connection strengths. Such a population can generate a variety of outputs, including bi-stable, multi-stable and oscillatory. Multiple W-C populations can be coupled together to produce complex patterns of output.
