Neuroscience 2000 Abstract
| Presentation Number: | 736.15 |
|---|---|
| Abstract Title: | Population density methods for large-scale modeling of neuronal networks with realistic synaptic kinetics. |
| Authors: |
Nykamp, D. Q.*2
; Haskell, E. C.2
; Tranchina, D.1,2
1Biology, New York University, New York, NY 2Mathematics, New York University, New York, NY |
| Primary Theme and Topics |
F. Sensory Systems - 79. Visual cortex: striate |
| Session: |
736. Visual cortex: striate--neural coding and synchrony Poster |
| Presentation Time: | Wednesday, November 8, 2000 3:00 PM-4:00 PM |
| Location: | Hall G-J |
| Keywords: | Mathematical Modeling, Neuronal Networks, Population Methods |
The population density method is a promising time-saving alternative to direct Monte-Carlo simulations of neural network activity, in which one tracks the state of thousands of individual neurons and synapses. This was found to be roughly a hundred times faster than direct simulation for various test networks of integrate-and-fire model neurons with instantaneous excitatory and inhibitory post-synaptic conductances (Nykamp and Tranchina, 2000). In this method, neurons are grouped into large populations of similar neurons. For each population, one calculates the evolution of a probability density function (PDF) which describes the distribution of neurons over state space. The population firing rate is then given by the total flux of probability across the threshold voltage for firing an action potential. Extending the method beyond instantaneous synapses is important for obtaining realistic results because synaptic kinetics play an important role in network dynamics. Embellishments incorporating more realistic synaptic kinetics for the underlying neuron model increase the dimension of the PDF, which was one-dimensional in the instantaneous synapse case. This increase in dimensionality causes a substantial increase in computation time to find the exact PDF, decreasing the computational speed advantage of the population density method over direct Monte-Carlo simulation. We report here on a one-dimensional model of the PDF for neurons with realistic synaptic kinetics that is computationally efficient. The model is shown to be accurate by comparison with Monte-Carlo simulations. A mean field approximation does not capture network kinetics nearly as well.
Sample Citation:
[Authors]. [Abstract Title]. Program No. XXX.XX. 2000 Neuroscience Meeting Planner. New Orleans, LA: Society for Neuroscience, 2000. Online.
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