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Stimulus encoding in temporal firing patterns relative to population-derived stimulus onset times

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Brasselet,  R
Research Group Physiology of Sensory Integration, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Panzeri,  S
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Logothetis,  NK
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Kayser,  C
Research Group Physiology of Sensory Integration, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Brasselet, R., Panzeri, S., Logothetis, N., & Kayser, C. (2011). Stimulus encoding in temporal firing patterns relative to population-derived stimulus onset times. Poster presented at 41st Annual Meeting of the Society for Neuroscience (Neuroscience 2011), Washington, DC, USA.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-B954-D
Abstract
The information carried by sensory neurons is usually examined after aligning individual spikes relative to the physical stimulus onset. However, the brain does not know this precise onset, but rather has to rely on intrinsically defined reference points to decode temporal activity patterns. This raised doubts about the relevance of stimulus information in millisecond precise spike times and promoted speculations about intrinsic references. We here demonstrate that a sub-population of neurons in auditory cortex may provide such a reliable reference system. We recorded the responses of 70 neurons in primary auditory cortex of alert macaque monkeys in response to 12 naturalistic sounds. Within this dataset, we found a population (‘A’) of 17 neurons (24) with the distinct property that these neurons responded to each stimulus on nearly every trial (at least 92 of trials) with short response onset latency (below 20ms). This property was not shared by the other neurons (population ‘B’), which responded only to a subset of stimuli and which often exhibited large variability in their latency. We then assessed the amount of stimulus information carried by ‘B’ neurons when referenced to i) the physical stimulus onset, ii) the onset latency of a simultaneously recorded ‘A’ neuron, and iii) the onset latency (if existent) of a simultaneously recorded ‘B’ neuron. Stimulus information was defined as the cumulative information in binary 5-spike patterns (3ms bins) over the first 100ms stimulus. Using a ‘B’ neuron as reference allowed recovering only approximately 50 of the information available when using the stimulus onset as reference, but using an ‘A’ neuron as reference recovered more than 75 of this information. We then constructed a hypothetical population of N ‘A’ neurons with the same latency trial-to-trial-jitter and cross-correlation as measured in real ‘A’ neurons, and we systematically quantified the information carried by B neurons when referenced to this population. This revealed that observing the activity of a few tens of ‘A’ neurons permits to infer the stimulus onset latency with a temporal precision sufficient to recover nearly 95 of the total stimulus information. These results demonstrate the existence of a specific population of neurons in auditory cortex which possess the required temporal precision and response reliability to serve as an intrinsic temporal reference signaling the onset of a to-be-decoded sound. In addition, they show that the relative timing between neurons in auditory cortex carries considerable stimulus information, not only when considering information in onset latencies, but also in sustained spike patterns.