Emergence of a Stable Cortical Map for Neuroprosthetic Control

By Karunesh Ganguly, Jose M. Carmena. 2009

581 citations recorded by the source corpus.

In this article, the authors show that the neural representation for control of a neuroprosthetic device undergoes a process of consolidation, after which it is stable, readily recalled, and resistant to interference.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Natural continual learning: success is a journey, not (just) a destination (2021, background): “In the RNNs, we found that the single-unit representations of a given digit changed during learning of the task involving that digit but stabilized after learning, consistent with work in several distinct biological circuits [6, 11, 16, 20, 34].”
A reward-modulated Hebbian learning rule can explain experimentally observed network reorganization in a brain control task (2010, methodology): “Monkeys using BCIs to control cursors or robotic arms improve with practice (Taylor et al., 2002; Carmena et al., 2003; Musallam et al., 2004; Schwartz, 2007; Ganguly and Carmena, 2009), indicating that learning-related changes are funneling through the set of neurons being recorded.”
ELECTROENCEPHALOGRAPHIC (EEG) CONTROL OF THREE-DIMENSIONAL MOVEMENT (2010, background): “…high-resolution signals are essential, and thus they have used intracortical electrodes to record neuronal action potentials or local ﬁeld potentials (Serruya et al 2002, Taylor et al 2002, Carmena et al 2003, Ganguly and Carmena 2009, Andersen et al 2004, Hochberg et al 2006, Velliste et al 2008).”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, methodology): “More recently, Jose Carmena and his colleagues (728, 729) reported an improved temporal resolution for an adaptive decoder that modeled spikes as a point process.”
Automated long-term recording and analysis of neural activity in behaving animals (2016, methodology): “…from such experiments have produced conflicting results, with some studies reporting stability (Chestek et al., 2007; Fraser and Schwartz, 2012; Ganguly and Carmena, 2009; Greenberg and Wilson, 2004), while others finding significant fluctuations even within a single day (Carmena et al., 2005;…”
Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface (2015, background): “…be explained by the fact that PD shifts have an upper bound (180°), and perturbations in decoded PDs can partially be accommodated physiologically through re-aiming and neural plasticity ( 15 , 28 ), whereas baseline shifts are unbounded and thus might not always be possible to accommodate.”
Sleep-Dependent Reactivation of Ensembles in Motor Cortex Promotes Skill Consolidation (2015, background): “While neuroprosthetic learning is beginning to be explored in more detail [57–59], it is unclear whether this learning represents declarative, procedural, or some more abstract form of learning that is fundamentally different than either of the above.”
Dynamic Reorganization of Neuronal Activity Patterns in Parietal Cortex. (2017, unknown): “Many studies have noted stability in motor cortex activity, but some have identified subtle shifts in tuning over days (Chestek et al., 2007; Ganguly and Carmena, 2009; Huber et al., 2012; PadoaSchioppa et al., 2004; Peters et al., 2014; Rokni et al., 2007; Stevenson et al., 2011).”
Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis (2017, background): “, 2012), while slower processes like learning and changes in motivational state can vary slowly over days or weeks (Kleim et al., 1998; Ganguly and Carmena, 2009; Peters et al., 2014).”