Abstract
Reconstructing stimuli from the spike trains of neurons is an important approach for understanding the neural code. One of the difficulties associated with this task is that signals which are varying continuously in time are encoded into sequences of discrete events or spikes. An important problem is to determine how much information about the continuously varying stimulus can be extracted from the time-points at which spikes were observed, especially if these time-points are subject to some sort of randomness. For the special case of spike trains generated by leaky integrate and fire neurons, noise can be introduced by allowing variations in the threshold every time a spike is released. A simple decoding algorithm previously derived for the noiseless case can be extended to the stochastic case, but turns out to be biased. Here, we review a solution to this problem, by presenting a simple yet efficient algorithm which greatly reduces the bias, and therefore leads to better decoding performance in the stochastic case.
Highlights
One of the fundamental problems in systems neuroscience is to understand how neural populations encode sensory stimuli into spatio-temporal patterns of action potentials
In a recent study (Gerwinn et al, 2009), we investigated decoding schemes for spike trains generated by a known encoding model: The leaky integrate and fire neuron (Burkitt, 2006)
The difficulty in computing the likelihood of a Decoding stimuli from sequences of spike-patterns spike train for the leaky integrate and fire neuron generated by populations of neurons is an imporwww.frontiersin.org tant approach for understanding the neural code
Summary
One of the fundamental problems in systems neuroscience is to understand how neural populations encode sensory stimuli into spatio-temporal patterns of action potentials. One tries to predict the spiking activity of individual neurons or populations in response to a stimulus. We can study the inverse mapping: Given observed spike trains, we try to infer the stimulus which is most likely to have produced this particular neural response (see Figure 1). In this case, we are putting ourselves in the position of a “sensory homunculus” (Rieke et al, 1997) which tries to reconstruct the sensory stimuli given only information about the activity of single neurons or a neural population.
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