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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 simulated with experimental data. A: PETH and raster plot for experimentally recorded neuron shown in Fig. 11B, aligned to “squeeze” the event. B: PETH and raster for data simulated to have a single-trial rate profile the same as the PETH in A. 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 100 simulations as in B. The information measured from A is shown as a dotted line.
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f13: Comparison of simulated with experimental data. A: PETH and raster plot for experimentally recorded neuron shown in Fig. 11B, aligned to “squeeze” the event. B: PETH and raster for data simulated to have a single-trial rate profile the same as the PETH in A. 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 100 simulations as in B. The information measured from A is shown as a dotted line.

Mentions: The procedure followed in Fig. 12 could be applied to experimental data: the information obtained from a simulation based on the experimentally derived PETH can be compared with the information measured directly from the experimental spike train. Any mismatch will determine whether trial-by-trial response variability occurs and, if so, quantify how great this is. This is demonstrated in Fig. 13 for the example cell shown in Fig. 11B. Figure 13A is the raster and PETH plot of the cell aligned to squeeze, whereas Fig. 13B is a similar display for the neuron simulated to have the same PETH. Figure 13, C and D shows the shift likelihood distributions for the real (Fig. 13C) and simulated spike trains (Fig. 13D). The shift likelihood distributions are much narrower and better aligned for the simulated neuron, giving it an Ir value of 3.97 bits, compared with 2.87 for the real cell. Figure 13E presents a histogram of the Ir values from 100 simulations, in which the mean Ir was 3.93 bits. We can therefore conclude that although the experimentally recorded cell did code the occurrence of the squeeze event well, trial-to-trial variability in its response meant that it coded for only 73% of the information that could have been coded if its response had been consistent.


Quantifying neural coding of event timing.

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

Comparison of simulated with experimental data. A: PETH and raster plot for experimentally recorded neuron shown in Fig. 11B, aligned to “squeeze” the event. B: PETH and raster for data simulated to have a single-trial rate profile the same as the PETH in A. 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 100 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

f13: Comparison of simulated with experimental data. A: PETH and raster plot for experimentally recorded neuron shown in Fig. 11B, aligned to “squeeze” the event. B: PETH and raster for data simulated to have a single-trial rate profile the same as the PETH in A. 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 100 simulations as in B. The information measured from A is shown as a dotted line.
Mentions: The procedure followed in Fig. 12 could be applied to experimental data: the information obtained from a simulation based on the experimentally derived PETH can be compared with the information measured directly from the experimental spike train. Any mismatch will determine whether trial-by-trial response variability occurs and, if so, quantify how great this is. This is demonstrated in Fig. 13 for the example cell shown in Fig. 11B. Figure 13A is the raster and PETH plot of the cell aligned to squeeze, whereas Fig. 13B is a similar display for the neuron simulated to have the same PETH. Figure 13, C and D shows the shift likelihood distributions for the real (Fig. 13C) and simulated spike trains (Fig. 13D). The shift likelihood distributions are much narrower and better aligned for the simulated neuron, giving it an Ir value of 3.97 bits, compared with 2.87 for the real cell. Figure 13E presents a histogram of the Ir values from 100 simulations, in which the mean Ir was 3.93 bits. We can therefore conclude that although the experimentally recorded cell did code the occurrence of the squeeze event well, trial-to-trial variability in its response meant that it coded for only 73% of the information that could have been coded if its response had been consistent.

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