Elsevier

Neurocomputing

Volumes 32–33, June 2000, Pages 1013-1020
Neurocomputing

A Bayesian decision approach to evaluate local and contextual information in spike trains

https://doi.org/10.1016/S0925-2312(00)00273-3Get rights and content

Abstract

In this study, we applied Bayesian decision theory to evaluate the information contained in neural spike trains. We used the spike statistics from 90% of the labelled trials to classify each of the remaining unlabelled trials. Classification rate were computed at different post-stimulus time within time windows of different durations. This allowed us to visualize and evaluate the information content of the spike trains in a scale-space representation. We found that discrimination of patterns within the receptive fields of the neurons can be accomplished at an early stage of the response within a relatively small time window (5–30 ms), while the discrimination of global contextual information can be accomplished at a later time.

Introduction

The average firing rate of a neuron as measured in electrophysiological studies has been considered the most reliable measure in explaining the function and representation of the neuron. However, in ‘real-time’ behavior, the animal typically has to make decisions using the information encoded within a few spikes [7]. How could this be accomplished? Two possible solutions have been proposed: (1) the system could be taking the population average of the responses of a group of neurons within a short-time window [2], [8], and (2) the exact timing structure of the spikes might carry additional and more precise information [1], [3], [4], [6], [9], [10].

Here, we examined the continuum between these two extremes by evaluating the information at different time after stimulus onset within windows of different durations. We measured information by asking the question, how well could we discriminate the input stimuli based on the spike counts of a incoming spike train within a specific window, provided the statistics of the responses of the cell to all the conditions are available? Specifically, we wanted to develop a representation that allow us to evaluate the effectiveness of spike count as a neural code systematically.

Section snippets

Experiments

The data analyzed in this study was collected from single and multiple units in the primary visual cortex of awake behaving monkeys while they were performing a fixation task, i.e. staring at a red spot on the screen while the test stimulus was presented on the screen 350ms each trial. Eighty-seven cells from two monkeys were studied (see [5] for details). Ten sessions of multielectrode recording were conducted to evaluate the information encoded in the simultaneous activities of multiple

Data analysis

In this analysis, the approach we have taken is as follows. For each time window of interest, the statistics of the spike counts within the window for 90% of the trials were compiled, and a classification rule (decision boundary)was determined based on the spike count distributions corresponding to the two conditions being compared. Even though we typically collected 30–80 trials per condition to obtain a reasonable distribution, it is often useful to fit the spike-count distribution with

Scale-space representation

The classification rates table can be displaced as a gray-scale picture, with highest rate the brightest and lowest rate the darkest. We called this the scale-space representation signature of the information content of the spike train. We have similarly used signal detection theory (receiver-operating characteristics curves) and information theory (mutual information) to compute the cell's scale-space representation signature and found that they are roughly similar qualitatively. In this

Conclusion

In this study, we analyzed the information of spike trains by looking at the cell's classification rate across a scale space. This gives a more precise measure of the observations that we have made earlier [5]. The basic new findings in this study are: (1) Information of different nature is richest at different specific post-stimulus time, suggesting that multiple perceptual representations might be computed or communicated at different time. (2) A 5–30 ms time window usually contains most of the

Acknowledgements

Lee is supported by a grant from the McDonnell-Pew Foundation, and LIS Grant 9720350 from NSF. Yan is supported by American Zhu Kezhen Education Foundation. The research facility is supported in part by EY 08098 core grant for Vision Research to the Eye and Ear Institute of Pittsburgh.

Elise Cassidente is an undergraduate student in Mathematics and Computer Science at Carnegie Mellon University.

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Cited by (0)

Elise Cassidente is an undergraduate student in Mathematics and Computer Science at Carnegie Mellon University.

Tai Sing Lee is an Assistant Professor in Computer Science and the Center for the Neural Basis of Cognition at Carnegie Mellon University.

(Lee's photo can be found in Yu and Lee's paper in the same issue).

Xiaogang Yan obtained BS in Computer Science from Nanjing University in 1982, M.S. and Ph.D. in Biomedical Engineering from Zhejiang University in PRC in 1984 and 1990 respectively. He is currently an Associate Professor in Biomedical Engineering at Zhejiang University and is a Research Associate at the Center for the Neural Basis of Cognition at Carnegie Mellon, USA. He is interested in Neural Information Processing and Intelligent System Development.

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