Research focus

Fundamentally, my lab is interested in three main questions:

  1. How is memory encoded as changes to complex networks of synapses and neurons?

  2. How do these synaptic/neuronal representations change throughout behavioral learning?

  3. How are these processes disrupted by neurological disease?

We employ a wide variety of approaches from systems neuroscience, molecular biology, and computational data science to explore how changes to synaptic plasticity and neural activity encode behavior:

  • CRISPR/Cas9 gene editing

  • In vivo two-photon imaging

  • Silicon probe electrophysiology (Neuropixels)

  • Optogenetic and pharmacogenetic perturbations

  • Freely moving and head-fixed behaviors


Goal: Track dynamic expression of synaptic proteins in behaving mice

Molecular techniques:

  • Use CRISPR/Cas9 to label endogenous synaptic proteome

  • Image of millions of individual synapses in behaving mice

  • Synaptome imaging in mouse models of Alzheimer's and Autism Spectrum Disorder


Goal: Explore roles of specific cell types in learning and memory

Cellular techniques:

  • Activity-based labeling of discrete cell types

  • In vivo 2p calcium imaging

  • Patch-clamp (in vivo & in vitro)

  • Optogenetic and pharmacogenetic perturbation


Goal: Record brain-wide neural activity using Neuropixels probes

Circuit techniques:

  • Simultaneous whole-brain electrical recordings

  • Track activity across cortex, hippocampus, and other structures throughout entire process of learning and memory


Goal: Observe & perturb learning and memory in behaving mice

Behavioral techniques:

  • Record brain-wide synaptic and neuronal activity during behavior

  • Freely moving & head-fixed VR

  • Behavior in disease models:

    • AD: Working memory tasks

    • ASD: social interaction

Current projects in the lab


  • Labeling and tracking millions of individual synapses in behaving mice

  • Exploring the role of synaptic engrams in learning and memory

  • Using whole-brain electrophysiology to uncover the spatiotemporal dynamics of memory encoding, storage, and retrieval


  • Training machine-learning algorithms to super-resolve synapses and fine neuronal processes in vivo

  • Improving tissue registration across imaging modalities

Disease models and clinical collaborations

  • Longitudinal in vivo imaging of synaptic pathologies of Alzheimer’s disease

  • Imaging synapse dynamics in vivo to explore the etiology of SynGAP-associated neuropsychiatric disease

  • Enhancing human clincial imaging with cross-modal restoration