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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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Schematic of analysis method concept. A: raster plot (100 trials) and perievent time histogram (PETH) of a simulated spike train. B: PETH for 3 different shifts relative to spike train of first trial. C: shift likelihood distribution for the spike train shown in B. D: superimposed shift likelihood distributions for all trials, with the averaged shift likelihood distribution (gray line).
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f1: Schematic of analysis method concept. A: raster plot (100 trials) and perievent time histogram (PETH) of a simulated spike train. B: PETH for 3 different shifts relative to spike train of first trial. C: shift likelihood distribution for the spike train shown in B. D: superimposed shift likelihood distributions for all trials, with the averaged shift likelihood distribution (gray line).

Mentions: Direct estimation of this probability is difficult. Much easier is the problem of estimating P(SP/δτ). To do this, we first shift the PETH by δτ (Fig. 1B) and then assume that the observed single-trial spike train resulted from this underlying rate modulation. We use a model for the spike-generating process to estimate the likelihood of the observed spike train, given this rate (details of the model will be addressed in subsequent sections). This process is repeated for all possible values of δτ, producing the likelihood plot P(SP/δτ) of Fig. 1C.


Quantifying neural coding of event timing.

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

Schematic of analysis method concept. A: raster plot (100 trials) and perievent time histogram (PETH) of a simulated spike train. B: PETH for 3 different shifts relative to spike train of first trial. C: shift likelihood distribution for the spike train shown in B. D: superimposed shift likelihood distributions for all trials, with the averaged shift likelihood distribution (gray line).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f1: Schematic of analysis method concept. A: raster plot (100 trials) and perievent time histogram (PETH) of a simulated spike train. B: PETH for 3 different shifts relative to spike train of first trial. C: shift likelihood distribution for the spike train shown in B. D: superimposed shift likelihood distributions for all trials, with the averaged shift likelihood distribution (gray line).
Mentions: Direct estimation of this probability is difficult. Much easier is the problem of estimating P(SP/δτ). To do this, we first shift the PETH by δτ (Fig. 1B) and then assume that the observed single-trial spike train resulted from this underlying rate modulation. We use a model for the spike-generating process to estimate the likelihood of the observed spike train, given this rate (details of the model will be addressed in subsequent sections). This process is repeated for all possible values of δτ, producing the likelihood plot P(SP/δτ) of Fig. 1C.

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