Neural reactivations during sleep determine network credit assignment

By T. Gulati, Ling Guo, Dhakshin Ramanathan, Anitha Bodepudi, Karunesh Ganguly. 2017

113 citations recorded by the source corpus.

A fundamental goal of motor learning is to establish the neural patterns that produce a desired behavioral outcome. It remains unclear how and when the nervous system solves this 'credit assignment' problem. Using neuroprosthetic learning, in which we could control the causal relationship between neurons and behavior, we found that sleep-dependent processing was required for credit assignment and the establishment of task-related functional connectivity reflecting the casual neuron–behavior relationship. Notably, we observed a strong link between the microstructure of sleep reactivations and credit assignment, with downscaling of non-causal activity. Decoupling of spiking to slow oscillations using optogenetic methods eliminated rescaling. Thus, our results suggest that coordinated firing during sleep is essential for establishing sparse activation patterns that reflect the causal neuron-behavior relationship.

Subfields: Cognitive Neuroscience

Citations in the corpus

• Replay in Deep Learning: Current Approaches and Missing Biological Elements (2021, neutral): “Further, replay during sleep was essential for solving the credit assignment problem by selectively increasing task-related neuron activity and decreasing task-unrelated neuron activity following sleep (Gulati et al., 2017).”