EPSP IPSP




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Software Description

A model to demonstrate the effect of Excitatory Postsynaptic Potentials (EPSP) and Inhibitory Postsynaptic Potentials (IPSP) on a neuron. The model is based on synaptic conductance equations from (Kohn and Worgotter 1998) and a fast resonate-and-fire neuron spiking equation from (Izhikevich 2001).

Izhikevich, EM (2001). Resonate-and-fire neurons. Neural Networks 14: 883-894.

Kohn, J and Worgotter, F (1998). Employing the Z-transform to Optimize the calculation of the synaptic conductance of NMDA and other synaptic channels in network simulations. Neural Computation 10: 1639-1651.

Hodgkin, A. L., & Huxley, A. F. (1952). Quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology, 117, 500544.

Coded By

Bret Fortenberry

Reference

Ames, H. , Gorchetchnikov, A. , Jasmin Leveille , Versace, M., KInNeSS: A modular framework for computational neuroscience, Neuroinformatics,2008 Winter; 6(4):291-309. (2008).

Abstract

A model to show the properties of EPSPs and IPSPs in the postsynaptic cell. The model allows a user to adjust the number of EPSP and IPSP inputs, the rise and fall times and the weighted effect on the postsynaptic cell. The model displays the current of the EPSP and the IPSP and the spiking output of the postsynaptic cell.

An excitatory postsynaptic potentials (EPSP) is a temporary depolarization of postsynaptic membrane caused by the flow of positively charged ions into the postsynaptic cell as a result of opening of ligand-sensitive channels. An EPSP is received when an excitatory presynaptic cell, connected to the dendrite, fires an action potential. The EPSP signal is propagated down the dendrite and is summed with other inputs at the axon hilllock. The EPSP increases the neurons membrane potential. When the membrane potential reaches threshold the cell will produce an action potential and send the information down the axon to communicate with postsynaptic cells. The strength of the EPSP depends on the distance from the soma. The signal degrades across the dendrite such that the more proximal connections have more of an influence.

An inhibitory postsynaptic potentials (IPSP) is a temporary hyperpolarization of postsynaptic membrane caused by the flow of negatively charged ions into the postsynaptic cell. An IPSP is received when an inhibitory presynaptic cell, connected to the dendrite, fires an action potential. The IPSP signal is propagated down the dendrite and is summed with other inputs at the axon hilllock. The IPSP decreases the neurons membrane potential and makes more unlikely for an action potential to occur. A postsynaptic cell typically has less inhibitory connections but the connections are closer to the soma. The proximity of the inhibitory connections produces a stronger signal such that fewer IPSPs are needed to cancel out the effect of EPSPs.

The membrane potential and spiking rate are dependent on a cells biophysical mechanism and the interaction of the cells internal and external voltage. Hodgkin and Huxley (1952) have introduced a standard model to describe the dynamics of cell's membrane potential. That model, described in terms of differential equations, tends to be computationally slow. Over the years, several other simplified spiking models have been designed. Although the later models are faster, they are less accurate than the Hodgkin and Huxley model. In this demonstration the Izhikevich resonate-and-fire model is used. This spiking model is used because it is faster than quadratic firing models and more biologically accurate than integrate and fire models.

Code Description

A demonstration of the effects of multiple EPSPs and IPSPs on a single neuron. To run the code open download the file epsp_ipsp.zip from the download section at the bottom. Unzip the file and open Matlab. Run epsp_ipsp_gui from the root epsp_ipsp directory.

Operating System

Linux/Unix, Machintosh, Windows

Programming Language(s)

Matlab

License

Demo software

Contributors

Bret Fortenberry, Jasmin Leveille, Massimiliano Versace, Kadin Tseng, Doug Sondak, Jesse Palma, Gail Carpenter

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