Reactivation of emergent task-related ensembles during slow-wave sleep after neuroprosthetic learning
By T. Gulati, Dhakshin Ramanathan, Chelsea C. Wong, Karunesh Ganguly. 2014
125 citations recorded by the source corpus.
Brain-machine interfaces can allow neural control over assistive devices. They also provide an important platform for studying neural plasticity. Recent studies have suggested that optimal engagement of learning is essential for robust neuroprosthetic control. However, little is known about the neural processes that may consolidate a neuroprosthetic skill. On the basis of the growing body of evidence linking slow-wave activity (SWA) during sleep to consolidation, we examined whether there is 'offline' processing after neuroprosthetic learning. Using a rodent model, we found that, after successful learning, task-related units specifically experienced increased locking and coherency to SWA during sleep. Moreover, spike-spike coherence among these units was substantially enhanced. These changes were not present with poor skill acquisition or after control awake periods, demonstrating the specificity of our observations to learning. Notably, the time spent in SWA predicted the performance gains. Thus, SWA appears to be involved in offline processing after neuroprosthetic learning.
Subfields: Cognitive Neuroscience
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In Cognitive Neuroscience
- Dhakshin Ramanathan
- Karunesh Ganguly
- T. Gulati
- Steffen B. E. Wolff
- Valentin A. Normand
- Ashesh K. Dhawale
- Evi Kopelowitz
- Rajesh Poddar
Citations in the corpus
- Replay in Deep Learning: Current Approaches and Missing Biological Elements (2021, neutral): “Spiking activity from motor cortex during NREM sleep has firing patterns that are similar to those of recent learning (Ramanathan, Gulati, & Ganguly, 2015; Gulati, Guo, Ramanathan, Bodepudi, & Ganguly, 2017; Gulati, Ramanathan, Wong, & Ganguly, 2014).”
- Sleep-Dependent Reactivation of Ensembles in Motor Cortex Promotes Skill Consolidation (2015, methodology): “While largely consistent with a body of work demonstrating the involvement of fast spindles in motor learning [10,11,51], this was quite distinct from what had been observed across the two previous studies demonstrating cortical reactivation in rodents [29,50].”
- Automated long-term recording and analysis of neural activity in behaving animals (2016, background): “…et al., 2014), but fail to address how neural activity is modulated outside of task context and across behavioral states (Arieli et al., 1996; Evarts, 1964; Gomez-Marin et al., 2014; Gulati et al., 2014; Lee and Dan, 2012; Rinberg et al., 2006; Santhanam et al., 2007; Wilson and McNaughton, 1994).”