Time estimation and the readiness to go: Temporal and decisional processes involved in movement preparation Alexa Riehle*1, Bjørg Elisabeth Kilavik*2, Adrián Ponce Alvarez*3, Nicolas Brunel§4 * Institut de Neurosciences Cognitives de la Méditerranée, UMR 6193, CNRS-Univ. Aix-Marseille II, 31 ch. Joseph Aiguier, 13402 Marseille cedex 20, France. § Laboratoire de Neurophysique et Physiologie, UMR 8119, CNRS-Univ. René Descartes, 45 rue des Saints Pères, 75270 Paris cedex 06, France. 1
[email protected] 2
[email protected] 3
[email protected] 4
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Résumé Pour qu’un acte moteur soit exécuté correctement, son décours temporel doit être précisément organisé. Cependant, la façon comme le temps est représenté au cerveau est assez inconnue. S’agit-t-il d’un processus invariant, indépendant du contexte comportemental, comme la préparation motrice ? Pour étudier les processus préparatoires, des tâches impliquant un délai entre deux signaux ont été utilisées. On a pu montrer que même dans une condition dans laquelle la durée du délai a été maintenue constante, le temps de réaction (RT) varie d’essai à essai. De plus, l’activité de neurones corticaux est prédictive, essai par essai, du RT. Cette variabilité du RT est généralement attribuée au niveau de disponibilité motrice. Quels processus l’influencent-ils ? Est-ce l’estimation temporelle qui serait à l’origine ? Nous allons entraîner des singes à une tâche qui vise à distinguer des processus d’estimation de temps de ceux de la préparation motrice. Nous allons enregistrer l’activité neuronale du cortex moteur en utilisant un dispositif multiélectrode. En parallèle, la modélisation va nous aider à élucider des mécanismes de la dynamique temporelle de la décharge neuronale et de sa variabilité, et à tester l’hypothèse selon laquelle une partie significative de la variabilité neuronale est due aux processus d’estimation temporelle.
Key words Time estimation, movement preparation, neuronal variability, decisional processes, delay activity, anticipation.
Outline of project The anticipation of predictable events is of crucial importance for organizing most efficiently motor performance. In order to study anticipatory processes in cognitive neuronscience, tasks in which a delay is
imposed between an instruction signal and a go signal are frequently used. Persistent neuronal activity recorded in the monkey during such delays, so-called delay activity, has mostly been interpreted as linked to memory processes [1,2], attention [3], motor preparatory processes (see for a review [4]) or decision processes [5], partly as a function of the cortical area under study [4]. Only a few studies have questioned the link between delay activity and timing processes [6-9]. In this context, the influence of timing parameters on motor cortical activity has been rarely studied (but see [10-12]). Mean reaction time is strongly reduced when temporal information is provided [13]. However, great trial-by-trial variability in movement initiation is often observed even in conditions in which the same prior information was provided in each trial [14,15]. The analysis of neuronal activity at the end of an instructed delay revealed a statistically significant trial-by-trial correlation between firing rate and reaction time in a high proportion of motor cortical neurons: the higher the firing rate, the shorter reaction time [14,16] (see also [17,18] for the frontal eye field). This correlated variability is usually attributed to the changes in the readiness to act. Which processes influence this variability in being ready? It might be explained with trial-by-trial fluctuations in the level of attention [3] or a more general arousal effect. For instance, the variability in cortical network activity is described as "ongoing activity" [19] which might be a correlate of the behavioral state, such as attention or arousal. In other words, the large variability in cortical activity is explained as a combination of induced activity, for instance by a stimulus and/or a behavioral response, and such ongoing activity [19]. Here we ask the question to which extent is it, however, influenced by the variable trial-by-trial estimation of the delay duration [20]? Do time estimation processes involved in motor behavior use different networks than movement preparatory processes, both necessarily leading to the readiness to act via a decisional process? Up to now, no clear distinction could be made between time estimation processes and movement preparation, if there is any.
We found in a delayed pointing task, in which two durations were presented at random, that the temporal profile of the discharge of many motor cortical neurons seemed indeed to be linked to the temporal judgment of the monkey [21]. Specifically, we estimated the firing rate in each single trial using a convolution of spike occurrences [22] for determining in each trial the moment of peak discharges in relation to each of the three trial events (i.e. the instruction signal, the expected go signal in the middle of long trials and the go signal). We found that the across trial variability in temporal occurrences of the discharge peaks increased with time from the instruction signal to movement onset. We then hypothesized that if one considers the animal's relative (subjective) time as the time that elapses between the instruction signal and movement onset, then, by suppressing this (subjective) time variability, the trial-by-trial variability in neuronal discharge should decrease. We thus defined a new time scale in each trial such that, after rescaling, the time between the instruction signal and movement onset was identical for all trials. Each spike was then displaced accordingly. As a result, in the new time scale, in which reaction time variability was suppressed, the trial-bytrial variability in the peak locations did indeed no longer increase during the time course of the trial ([21], Fig. 1). This suggests a direct link between the temporal property of spiking activity and the trial-by-trial time estimation. The activity of neurons in motor cortex reflected a continuous signature of the "elasticity" of time ("time is a rubberband") between the instruction signal and movement initiation, which varied from trial to trial as a function the animal's relative (subjective) time. Some of the questions raised by experimental data on delay period activity have been investigated using modeling studies. In particular, it has been shown that irregular activity in cortex can be generated by the intrinsic dynamics of a sparsely connected network of excitatory and inhibitory neurons with strong inhibition [23-25]. Other modeling studies have investigated temporal dynamics in delay periods. In particular, decision-related ramping-up of neuronal activity can be obtained in an attractor network including sufficiently slow synaptic kinetics [26]. Furthermore, during the delay period, fast transitions between attractors occur after Hebbian learning of stimulus pair associations, leading to anticipatory activity [27]. The mechanisms by which a single neuron or a network can represent the duration of a delay have also been investigated by recent modeling studies [28,29]. Benefiting from this recently established cooperation between our teams in Marseille and Paris we will use a combined experimental and theoretical approach to understand time estimation processes involved in motor performance. Monkeys are trained in a specifically designed task in which time estimation processes will be clearly separated in time from movement preparation and neuronal activity will be recorded in motor cortex using a multi-electrode device. In parallel, modeling studies will help us (i) to investigate the mechanisms of the temporal dynamics of neuronal discharge and its variability, and (ii) to further test the hypothesis that a
significant part of the variability is due to temporal estimation processes.
Acknowledgements The project is funded by a grant from “Agence Nationale de la Recherche”: ANR-05-NEUR-045-01.
Figure 1. Activity of a motor cortical neuron in a long delay trial, before (A) and after (B) time transformation. Data are presented both as raster display and PSTH. Trials were arranged off-line according to increasing reaction time, from top to bottom. In A, neuronal spike data are presented in the original time scale (time in ms). The data are aligned to signal occurrence. PS: instruction (preparatory) signal; ES: expected go signal; RS: go (response) signal. The first row of red diamonds corresponds to movement onset, the second one (blue) to movement end. In B, the same data are presented after the trial-by-trial transformation of the time scale, the time scale being normalized between PS (t0) and movement onset (red diamonds in A). Here, the blue diamonds correspond to movement end. (from [21])
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