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| Home > Publications database > Rate Latency Patterns Across Neuronal Populations Specific to Behavior |
| Abstract | FZJ-2018-03913 |
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2018
Please use a persistent id in citations: http://hdl.handle.net/2128/20103
Abstract: Motor cortices neurons change their firing rates in relation to movement of body parts. The timingsof the peak firing rates differ across simultaneously observed neurons. Several studies related to othercortical areas (e.g. [1]) have shown that the order of the peak latencies encode information processedin the local network. Here we perform latency ordering analysis of neuronal recordings from macaquemotor cortex during a reach-to-grasp task [2]. Specifically we aim to characterize if neurons exhibit thesame rate latency order across trials, and if this order is specific to the behavioral context. Two macaquemonkeys were trained to perform a reach-to-grasp task [2], where the monkeys were required to reach toan object, hold it with a specific grip type (side grip (SG) or precision grip (PG)), and pull it at a specificforce level (high or low). Two cues for the grip type and the force level were provided intervened by a1 sec delay: the first cue presents the grip type and the second the force level. The second cue wasalso the go signal for the monkey to initiate the reaching to the object. Trials with different combinationsof grip type and force level were performed in a pseudo-random order. During the task performance,spiking activities of single neurons were recorded from the motor cortex (M1/PMD) using a 10x10 Utahelectrode array, which enabled to record spike trains of more than 100 single neurons simultaneously. Inorder to obtain the timing of the movement related spiking activity of single neurons, we estimated theinstantaneous firing rate in each individual trial using a kernel convolution method [3] and performed astatistical test [4] to detect significant changes in the firing rate. We register the time latency from the onsetof the arm movement to the time of the peak of the firing during a significant rate change. Thus, for eachtrial we obtain a set of peak latencies for the simultaneously recorded single neurons. We first confirmif neurons exhibit the same latency order across the trials of the identical task condition. We quantifythe similarity between the latency order of each pair of trials by the Spearman’s rank correlation, and testwhether the distribution of the correlation coefficients among the trial pairs are significantly different from thedistribution obtained from randomly shuffled latency combinations. For both monkeys we analyzed and forboth behavioral trial types (SG and PG) the empirical data exhibit significantly stronger correlations than thesurrogates, indicating that the latency order is more consistent across trials (of an identical condition) thanexpected from the random latency combination. Further we examined if the latency order is specific to thebehavioral context. We test whether or not the empirical distributions of the correlation coefficients withintrials of identical conditions significantly differ from the distribution obtained from pairs of trials randomlyselected from different conditions. For one monkey we find that the latency order correlation is significantlystronger within identical conditions than between different conditions, while for the other monkey there isno significant difference.A possible reason for this difference may rest on the fact that the task performance, in terms the move-ment reaction time and success rate, of the latter monkey was much faster and better than the former,which may imply that the movement was already well prepared during the delay period. We are currentlyexamining how the inter-trial variability of the reaction time is reflected in the neuronal activity on a singletrial basis, which would shed light on the neuronal mechanism determining the task performance and itsinter-subject differencesAcknowledgements. Supported by EU Grant 785907(HBP) and EU Grant 720270(HBP)References:[1]: Riehle, A., Wirtssohn, S., Grün, S., and Brochier, T. (2013). Frontiers in Neural Circuits , 7:48.[2]: Luczak, A., McNaughton, B. L., and Harris, K. D. (2015). Nature Reviews Neuroscience, 16(12):745.[3]: Nawrot, M. P., Aertsen, A., and Rotter, S. (1999). Journal of Neuroscience Methods , 94(1):8192.[4]: Baker, S. N. and Gerstein, G. L. (2001). Neural Computation, 13(6):1351137
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