Temporal Limitations of the Standard Leaky Integrate and Fire Model

Abstract

The leaky integrate and fire model of neural spiking has been used extensively to simulate saccadic responses in a variety of tasks from visual search to simple reaction times. Although it has been tested for its neural spiking accuracy and its spatial prediction of fixations in visual salience, it has not been well tested for its temporal accuracy. Saccade generation invariably results in a positively skewed distribution of saccadic reaction times over large numbers of samples, yet we show that the LIF algorithm tends to produce a distribution shifted to shorter fixations (in comparison with human data) in its classic implementation. Further, parameter optimization using a genetic algorithm and Nelder–Mead method does improve the fit of the resulting distribution, but is still unable to match temporal distributions of human responses in a simple visual search task. Further analysis revealed, that the LIF algorithm produces discrete reaction times instead of distributions. Aggregated over many pictures they may be treated as a distribution although the form of this distribution depends on the input images used to create it.