Abstract
Decision is a self-generated phenomenon, which is hard to track with standard time averaging methods, such as peri-event time histograms (PETHs), used in behaving animals. Reasons include variability in duration of events within a task and uneven reaction time of animals. We have developed a temporal normalization method where PETHs were juxtaposed all along task events and compared between neurons. We applied this method to neurons recorded in striatum and GPi of behaving monkeys involved in a choice task. We observed a significantly higher homogeneity of neuron activity profile distributions in GPi than in striatum. Focusing on the period of the task during which the decision was taken, we showed that approximately one quarter of all recorded neurons exhibited tuning functions. These so-called coding neurons had average firing rates that varied as a function of the value of both presented cues, a combination here referred to as context, and/or value of the chosen cue. The tuning functions were used to build a simple maximum likelihood estimation model, which revealed that (i) GPi neurons are more efficient at encoding both choice and context than striatal neurons and (ii) context prediction rates were higher than those for choice. Furthermore, the mutual information between choice or context values and decision period average firing rate was higher in GPi than in striatum. Considered together, these results suggest a convergence process of the global information flow between striatum and GPi, preferentially involving context encoding, which could be used by the network to perform decision-making.
Highlights
In a visually guided motor task, decision-making is a distributed neural process that involves the basal ganglia (BG) interacting with the frontal and prefrontal cortical areas as well as with the dopaminergic system (Opris and Bruce, 2005; Schultz, 2006; Daw, 2007; Samejima and Doya, 2007; Kable and Glimcher, 2009)
We focused our analysis on a possible correlation between the neuronal activity in striatum and GPi and the animal behavior during the crucial period between the appearance of the cue and the go signal, the decision period (DP)
normalized inter-event time histogram (NIETH) extraction and normalization To have an overall view of the neuronal dynamics associated with the choice task and to compare both striatal and pallidal activity profiles, we investigated the temporal outline of NIETHs across all the steps of the task
Summary
In a visually guided motor task, decision-making is a distributed neural process that involves the basal ganglia (BG) interacting with the frontal and prefrontal cortical areas as well as with the dopaminergic system (Opris and Bruce, 2005; Schultz, 2006; Daw, 2007; Samejima and Doya, 2007; Kable and Glimcher, 2009). In a recent electrophysiological study in behaving monkeys, using a multiple choice task, we showed that the encoding of the movement direction by the neurons of the striatum (the main input of the BG) and the internal globus pallidus (GPi, the main output of the BG) is modulated by the incentive value of the action (Pasquereau et al, 2007) This could provide a mechanism by which motor program selection could be learned under dopamine control (Samejima and Doya, 2007). Despite the intrinsic limitation that PETH computation does not by itself provide a framework for statistical inference (Czanner et al, 2008), it remains a widely used tool that provides meaningful insights and whose efficiency has been improved (Endres and Oram, 2010) To solve this conundrum, we developed a simple method to normalize time durations in each trial and to build a normalized inter-event time histogram (NIETH) for individual neurons. We addressed the questions of how and where information flows were processed in the BG system
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