Motor learning with unstable neural representations.

By Uri Rokni, Andrew G. Richardson, Emilio Bizzi, H. Sebastian Seung. 2007

259 citations recorded by the source corpus.

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In Cognitive Neuroscience

Citations in the corpus

Natural continual learning: success is a journey, not (just) a destination (2021, background): “…stability of neural representations with some indicating stable representations (Chestek et al., 2007; Dhawale et al., 2017; Katlowitz et al., 2018; Jensen et al., 2021) and others drifting representations (Liberti et al., 2016; Driscoll et al., 2017; Schoonover et al., 2021; Rokni et al., 2007).”
Single-Neuron Stability during Repeated Reaching in Macaque Premotor Cortex (2007, methodology): “Quantifying the size of PD shifts with the SD of the shift distribution, Rokni et al. (2007) found a change of 19° in the best isolated units.”
Emergence of a Stable Cortical Map for Neuroprosthetic Control (2009, background): “Possible reasons for the apparent variability include the process of learning a novel motor relationship [40–42], postural changes, and subtle changes in the pattern of muscle activation [20,23].”
A reward-modulated Hebbian learning rule can explain experimentally observed network reorganization in a brain control task (2010, result): “Theresultsofthismodelingpaperalsosupportthehypotheses introduced by Rokni et al. (2007).”
Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects (2009, background): “It remains possible that intracortical recording might not produce speed and accuracy of performance substantially greater than what can be achieved with EEG, a non-invasive method not requiring surgery or any of its attendant risks [31].”
Long-term stability of cortical population dynamics underlying consistent behavior (2020, unknown): “…time, prior studies disagree on the stability of movement information in single neurons over short periods of time (from one to four days), with some claiming inherent instability of tuning properties 29 and others claiming stability well within the error introduced by measurement noise 11,17,30 .”
Dynamic Reorganization of Neuronal Activity Patterns in Parietal Cortex. (2017, unknown): “Incorporation of Representations for Newly Learned Associations into Existing Population Activity Patterns Computational models suggest that an advantage of dynamic neuronal representations could be the flexibility of incorporating new information into the population (Ajemian et al., 2013; Rokni et ”
ELECTROENCEPHALOGRAPHIC (EEG) CONTROL OF THREE-DIMENSIONAL MOVEMENT (2010, background): “At the same time, they face difﬁculties in ensuring stable long-term function (Otto et al 2008), and the information available from neurons may have inherent limitations (Rokni et al 2007).”
Causes and consequences of representational drift (2019, background): “The null space is a subspace of population activity that is orthogonal toa low-dimensional task representation[25,26].”
Automated long-term recording and analysis of neural activity in behaving animals (2016, background): “…which the relationship between single unit activity and various movement parameters (i.e. a cell’s ‘motor tuning’) is stable over days and weeks has been the subject of recent debate (Carmena et al., 2005; Chestek et al., 2007; Ganguly and Carmena, 2009; Rokni et al., 2007; Stevenson et al., 2011).”