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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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Related in: MedlinePlus

Implementation of analysis on experimental spike trains. For the entire figure, the left column represents data aligned to go-cue, middle column to reach, and right column to squeeze events. B and C: relate to discharge of one neuron; D and E to the discharge of a different cell that was simultaneously recorded. A: distribution of 3 different behavioral events relative to each other. B and D: PETH (top) and raster plot (bottom) of the activity of a single neuron recorded from supplementary motor area (SMA). C and E: single trial shift likelihood distributions (black) and averaged shift likelihood distributions (gray).
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f11: Implementation of analysis on experimental spike trains. For the entire figure, the left column represents data aligned to go-cue, middle column to reach, and right column to squeeze events. B and C: relate to discharge of one neuron; D and E to the discharge of a different cell that was simultaneously recorded. A: distribution of 3 different behavioral events relative to each other. B and D: PETH (top) and raster plot (bottom) of the activity of a single neuron recorded from supplementary motor area (SMA). C and E: single trial shift likelihood distributions (black) and averaged shift likelihood distributions (gray).

Mentions: Figure 11 shows the results of the method applied to two real neurons recorded from the supplementary motor area (SMA) of a monkey performing a precision grip task with the hand contralateral to the recording site. Full details of the task are given in previous publications (Soteropoulos and Baker 2006, 2007). Briefly, the animal held both hands on home pad switches. A “go cue” signaled that a movement should be made. The animal then lifted one hand from the home pad (the “reach” event) and gripped the levers of a precision grip manipulandum between finger and thumb (the “squeeze” event, indicating first lever movement). Following a hold period, the levers were released to obtain a reward.


Quantifying neural coding of event timing.

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

Implementation of analysis on experimental spike trains. For the entire figure, the left column represents data aligned to go-cue, middle column to reach, and right column to squeeze events. B and C: relate to discharge of one neuron; D and E to the discharge of a different cell that was simultaneously recorded. A: distribution of 3 different behavioral events relative to each other. B and D: PETH (top) and raster plot (bottom) of the activity of a single neuron recorded from supplementary motor area (SMA). C and E: single trial shift likelihood distributions (black) and averaged shift likelihood distributions (gray).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f11: Implementation of analysis on experimental spike trains. For the entire figure, the left column represents data aligned to go-cue, middle column to reach, and right column to squeeze events. B and C: relate to discharge of one neuron; D and E to the discharge of a different cell that was simultaneously recorded. A: distribution of 3 different behavioral events relative to each other. B and D: PETH (top) and raster plot (bottom) of the activity of a single neuron recorded from supplementary motor area (SMA). C and E: single trial shift likelihood distributions (black) and averaged shift likelihood distributions (gray).
Mentions: Figure 11 shows the results of the method applied to two real neurons recorded from the supplementary motor area (SMA) of a monkey performing a precision grip task with the hand contralateral to the recording site. Full details of the task are given in previous publications (Soteropoulos and Baker 2006, 2007). Briefly, the animal held both hands on home pad switches. A “go cue” signaled that a movement should be made. The animal then lifted one hand from the home pad (the “reach” event) and gripped the levers of a precision grip manipulandum between finger and thumb (the “squeeze” event, indicating first lever movement). Following a hold period, the levers were released to obtain a reward.

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
Related in: MedlinePlus