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Chunking of single movements into integrated sequences has been described during motor learning, and we have recently demonstrated that this process involves a dopamine-dependant mechanism in animal (Levesque et al. in Exp Brain Res 182:499-508, 2007; Tremblay et al. in Behav Brain Res 198:231-239, 2009). However, there is no such evidence in human. The aim of the present study was to assess this question in Parkinson's disease (PD), a neurological condition known for its dopamine depletion in the striatum. Eleven PD patients were tested under their usual levodopa medication (ON state), and following a 12-h levodopa withdrawal (OFF state). Patients were compared with 12 healthy participants on a motor learning sequencing task, requiring pressing fourteen buttons in the correct order, which was determined by visual stimuli presented on a computer screen. Learning was assessed from three blocks of 20 trials administered successively. Chunks of movements were intrinsically created by each participant during this learning period. Then, the sequence was shuffled according to the participant's own chunks, generating two new sequences, with either preserved or broken chunks. Those new motor sequences had to be performed separately in a fourth and fifth blocks of 20 trials. Results showed that execution time improved in every group during the learning period (from blocks 1 to 3). However, while motor chunking occurred in healthy controls and ON-PD patients, it did not in OFF-PD patients. In the shuffling conditions, a significant difference was seen between the preserved and the broken chunks conditions for both healthy participants and ON-PD patients, but not for OFF-PD patients. These results suggest that movement chunking during motor sequence learning is a dopamine-dependent process in human.

The coordination of activity amongst populations of neurons in the brain is critical to cognition and behavior. One form of coordinated activity that has been widely studied in recent years is the so-called neuronal avalanche, whereby ongoing bursts of activity follow a power-law distribution. Avalanches that follow a power law are not unique to neuroscience, but arise in a broad range of natural systems, including earthquakes, magnetic fields, biological extinctions, fluid dynamics, and superconductors. Here, we show that common techniques that estimate this distribution fail to take into account important characteristics of the data and may lead to a sizable misestimation of the slope of power laws. We develop an alternative series of maximum likelihood estimators for discrete, continuous, bounded, and censored data. Using numerical simulations, we show that these estimators lead to accurate evaluations of power-law distributions, improving on common approaches. Next, we apply these estimators to recordings of in vitro rat neocortical activity. We show that different estimators lead to marked discrepancies in the evaluation of power-law distributions. These results call into question a broad range of findings that may misestimate the slope of power laws by failing to take into account key aspects of the observed data.

No abstract

In this paper, we introduce a network combining k-Winners-Take-All and Self-Organizing Map principles within a Feature Extracting Bidirectional Associative Memory. When compared with its "strictly winner-take-all" version, the modified model shows increased performance for clustering, by producing a better weight distribution and a lower dispersion level (higher density) for each given category. Moreover, because the model is recurrent, it is able to develop prototype representations strictly from exemplar encounters. Finally, just like any recurrent associative memory, the model keeps its reconstructive memory and noise filtering properties.