Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task

By M. Laubach, Johan Wessberg, Miguel A. L. Nicolelis. 2000

286 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

Cortical Synaptogenesis and Motor Map Reorganization Occur during Late, But Not Early, Phase of Motor Skill Learning (2004, background): “…increases in immediate early gene expression occur during the early stages of motor learning (Kleim et al., 1996), and changes in neuronal ensemble activity within the motor cortex are both observable during the initial training phase and directly predict performance (Laubach et al., 2000).”
Variance and invariance of neuronal long-term representations (2017, unknown): “Similar to previous electrophysiological studies [63,72,73], neuronal activity became more invariantly task-‐ related with training [27,37,48], with deep layer 5a (L5a) neurons showing more improvement than supragranular L2/”
Theta Rhythms Coordinate Hippocampal–Prefrontal Interactions in a Spatial Memory Task (2005, background): “processing [12], attention [13], and motor learning [14].”
Imaging brain dynamics using independent component analysis (2001, unknown): “Finally, ICA has proven effective at analyzing single-unit activity from the cerebral cortex [65], [ 66 ] and in separating neurons in optical recordings from invertebrate ganglia [67].”
Spatio-spectral filters for improving the classification of single trial EEG (2005, methodology): “We record brain activity by means of multielectrode electroencephalogram (EEG) which is noninvasive as opposed to invasive work by, i.e., [ 10 ]‐[13].”
Spatiotemporal Dynamics of Functional Clusters of Neurons in the Mouse Motor Cortex during a Voluntary Movement (2013, background): “Motor learning increases the information about the movement that is encoded by neural ensemble activity in the motor cortex (Laubach et al., 2000; Paz and Vaadia, 2004).”
A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals (2007, methodology): “…Polak 1997) None (Anderson et al 1995a, Panuccio et al 2002) Filtering − rectification − thresholding (Karniel et al 2002, Kositsky et al 2003, Reger et al 2000a, 2000b) Averaging (Laubach et al 2000, Otto et al 2003, Vetter et al 2003) TFR methods (Laubach et al 2000, Musallam et al 2004) None (”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “The main goal of these studies was to investigate physiological properties, including the ability of neural ensembles in the sensorimotor cortex to encode information and express plastic adaptations while freely behaving animals learned new motor tasks (460).”
Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms (2004, unknown): “[2], [3], [4]) and non-invasive BCI technology (e.”