neural engineering & plasticity lab
Understanding how distributed brain networks learn, adapt, and recover—and translating these principles into new treatments.
Cross-area dynamics during learning
Learning a new motor skill requires a distributed circuit involving ‘cognitive’ (e.g. prefrontal cortex, PFC) and motor networks (Motor Cortex, M1 & dorsolateral striatum, DLS). We aim to understand this process from the first exploratory attempt to the emergence of a consolidated skill. Figure on the left is based on results from Kim et al., Nature 2023 showing the transition from fast learning to slow learning; there were changes in coupling between PFC and M1 that marked the onset of stabilization of M1 dynamics. Slow learning is also associated with greater coupling between M1 and DLS (e.g. Lemke et al., Nat Neurosc, 2019; Lemke et al., eLife, 2021). Understanding these two dynamics can improve recovery after injury.
Kim et al., Nature, 2023; Lemke et al, Nature Neuroscience, 2019; Lemke et al. eLife, 2019; Veuthey et al., Nat Comm, 2020
Network basis of memory consolidation and forgetting
Sleep and rest are both known to be important for memory consolidation. We aim to delineate the network dynamics of learning, consolidation and active forgetting. The balance between consolidation and forgetting may be important for generalization and “credit assignment” . Image on right depicts that sleep patterns (slow-oscillations, delta waves, spindles) may regulate consolidation versus forgetting.
Griffin et al., Nature 2025; Kim et al., Nature 2023; Kim et al., Cell, 2019; Gulati et al., Nature Neuroscience, 2017
Modulation of network dynamics to improve recovery after stroke
Our basic cross-species work has found that restoration of low-frequency oscillatory network dynamics promotes functional recovery after injury, providing a foundation for developing targeted neuromodulation therapies. Our perspective (Ganguly et al., Neuron, 2022) provides a more detailed discussion on this.
We are currently conducting a pilot study to evaluate this approach in individuals with stroke. (KULMINATE Study)
Choi et al., Neuron 2025; Ghuman et al. Nature Communications, 2023; Khanna et al, Cell, 2021; Ramanathan, Guo et al., Nature Medicine, 2018
Long-term Stable BCI control
We co-lead the UCSF BRAVO Trial. We aim to translate our growing understanding of representational stability/plasticity to create long-term stable neuroprosthetic control. Our recent study found that accounting for representational drift can allow long-term stable BCI control (Natraj et al, Cell 2025).
Natraj et al., Cell, 2025; Natraj et al., Neuron, 2022​; Silversmith*,Abiri*,Hardy*,Natraj* et al, Nature Biotechnology, 2021; Ganguly et al., Nature Neuroscience, 2011; Ganguly et al., PLoS Biology, 2009
