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Quantifying neural coding of event timing.

Soteropoulos DS, Baker SN - J. Neurophysiol. (2008)

Bottom Line: The perievent time histogram (PETH) examines how, on average, neural firing modulates before and after the alignment event.This is used to generate a probability distribution of the event occurrence, using Bayes' rule.By an information theoretic approach, this method yields a single value (in bits) that quantifies the reduction in uncertainty regarding the time of an external event following observation of the spike train.

View Article: PubMed Central - PubMed

Affiliation: Institute of Neuroscience, Medical School, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK.

ABSTRACT
Single-neuron firing is often analyzed relative to an external event, such as successful task performance or the delivery of a stimulus. The perievent time histogram (PETH) examines how, on average, neural firing modulates before and after the alignment event. However, the PETH contains no information about the single-trial reliability of the neural response, which is important from the perspective of a target neuron. In this study, we propose the concept of using the neural activity to predict the timing of the occurrence of an event, as opposed to using the event to predict the neural response. We first estimate the likelihood of an observed spike train, under the assumption that it was generated by an inhomogeneous gamma process with rate profile similar to the PETH shifted by a small time. This is used to generate a probability distribution of the event occurrence, using Bayes' rule. By an information theoretic approach, this method yields a single value (in bits) that quantifies the reduction in uncertainty regarding the time of an external event following observation of the spike train. We show that the approach is sensitive to the amplitude of a response, to the level of baseline firing, and to the consistency of a response between trials, all of which are factors that will influence a neuron's ability to code for the time of the event. The technique can provide a useful means not only of determining which of several behavioral events a cell encodes best, but also of permitting objective comparison of different cell populations.

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Comparison of responses with and without latency variation. A: PETH and raster plot for trials with baseline rate of 40 Hz and a response rate of 80 Hz (duration 200 ms, gamma order 4). Response latency varied randomly from trial to trial (normal distribution, SD 100 ms). B: PETH and raster for data simulated to have a rate profile the same as the PETH in A, with no latency variation from trial to trial. C and D: shift likelihood distributions for single trials (black) and mean shift likelihood distribution (gray), corresponding to the data illustrated in A and B. E: distribution of information estimated from 50 simulations as in B. The information measured from A is shown as a dotted line.
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f12: Comparison of responses with and without latency variation. A: PETH and raster plot for trials with baseline rate of 40 Hz and a response rate of 80 Hz (duration 200 ms, gamma order 4). Response latency varied randomly from trial to trial (normal distribution, SD 100 ms). B: PETH and raster for data simulated to have a rate profile the same as the PETH in A, with no latency variation from trial to trial. C and D: shift likelihood distributions for single trials (black) and mean shift likelihood distribution (gray), corresponding to the data illustrated in A and B. E: distribution of information estimated from 50 simulations as in B. The information measured from A is shown as a dotted line.

Mentions: Figure 10C is a cluster plot of the actual response latency versus the maximum likelihood shift. There is substantial and significant (P < 0.01, shuffle test) correlation between the two. However, the variation in estimated shift was greater than that in actual response latency (SD 70 vs. 50 ms) and the slope of the regression line was smaller than unity (0.75). This is due to variations in spike timing, which means that spikes do not occur exactly at the moment of a rate change. Even with no trial-to-trial latency variation, maximum likelihood shifts show some dispersion (e.g., Figs. 4H and 12D).


Quantifying neural coding of event timing.

Soteropoulos DS, Baker SN - J. Neurophysiol. (2008)

Comparison of responses with and without latency variation. A: PETH and raster plot for trials with baseline rate of 40 Hz and a response rate of 80 Hz (duration 200 ms, gamma order 4). Response latency varied randomly from trial to trial (normal distribution, SD 100 ms). B: PETH and raster for data simulated to have a rate profile the same as the PETH in A, with no latency variation from trial to trial. C and D: shift likelihood distributions for single trials (black) and mean shift likelihood distribution (gray), corresponding to the data illustrated in A and B. E: distribution of information estimated from 50 simulations as in B. The information measured from A is shown as a dotted line.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2637006&req=5

f12: Comparison of responses with and without latency variation. A: PETH and raster plot for trials with baseline rate of 40 Hz and a response rate of 80 Hz (duration 200 ms, gamma order 4). Response latency varied randomly from trial to trial (normal distribution, SD 100 ms). B: PETH and raster for data simulated to have a rate profile the same as the PETH in A, with no latency variation from trial to trial. C and D: shift likelihood distributions for single trials (black) and mean shift likelihood distribution (gray), corresponding to the data illustrated in A and B. E: distribution of information estimated from 50 simulations as in B. The information measured from A is shown as a dotted line.
Mentions: Figure 10C is a cluster plot of the actual response latency versus the maximum likelihood shift. There is substantial and significant (P < 0.01, shuffle test) correlation between the two. However, the variation in estimated shift was greater than that in actual response latency (SD 70 vs. 50 ms) and the slope of the regression line was smaller than unity (0.75). This is due to variations in spike timing, which means that spikes do not occur exactly at the moment of a rate change. Even with no trial-to-trial latency variation, maximum likelihood shifts show some dispersion (e.g., Figs. 4H and 12D).

Bottom Line: The perievent time histogram (PETH) examines how, on average, neural firing modulates before and after the alignment event.This is used to generate a probability distribution of the event occurrence, using Bayes' rule.By an information theoretic approach, this method yields a single value (in bits) that quantifies the reduction in uncertainty regarding the time of an external event following observation of the spike train.

View Article: PubMed Central - PubMed

Affiliation: Institute of Neuroscience, Medical School, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK.

ABSTRACT
Single-neuron firing is often analyzed relative to an external event, such as successful task performance or the delivery of a stimulus. The perievent time histogram (PETH) examines how, on average, neural firing modulates before and after the alignment event. However, the PETH contains no information about the single-trial reliability of the neural response, which is important from the perspective of a target neuron. In this study, we propose the concept of using the neural activity to predict the timing of the occurrence of an event, as opposed to using the event to predict the neural response. We first estimate the likelihood of an observed spike train, under the assumption that it was generated by an inhomogeneous gamma process with rate profile similar to the PETH shifted by a small time. This is used to generate a probability distribution of the event occurrence, using Bayes' rule. By an information theoretic approach, this method yields a single value (in bits) that quantifies the reduction in uncertainty regarding the time of an external event following observation of the spike train. We show that the approach is sensitive to the amplitude of a response, to the level of baseline firing, and to the consistency of a response between trials, all of which are factors that will influence a neuron's ability to code for the time of the event. The technique can provide a useful means not only of determining which of several behavioral events a cell encodes best, but also of permitting objective comparison of different cell populations.

Show MeSH