Overall research goals

  • Identify circuit-based mechanisms that regulate synaptic transmission
  • Examine neuronal activity during sensory experiences
  • Isolate neuronal circuits that coordinate activity to produce learning behaviors

Main technical directions

  • Slice electrophysiology – somatic and dendritic intracellular recordings
  • In vitro and in vivo two photon imaging
  • Freely moving and head-fixed virtual reality behavior
 
 
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The balance of excitation and inhibition is critical for normal brain function, and when disrupted it can cause severe neural disorders such as epilepsy, schizophrenia and depression. However during processes such as learning, shifts in the weight of excitation and inhibition are important to discriminate and store only the relevant information based on contexts.
 

A precise model of how excitatory and inhibitory neurons cooperate to tune information flow is crucial to our understanding of the brain.


Questions of interest

 
 
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Exchange of information occurs across many levels in the brain – between molecules and receptors of a single neuron, between synapses connecting two neurons, and between neural circuits linking distant brain areas. Activity dependent changes in the signaling strength at any of these junctures results in plasticity, a substrate for information storage in the central nervous system.

How does hard wired circuit architecture contribute to modulation of information flow and long-term plasticity?

 

 
 
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Neurons wire up to form simple and complex circuits. Neural circuitry provides the architectural framework for information flow in the brain. Local circuits mediate information flow within a brain area, while long-range projection circuits coordinate activity between two distinct brain regions.

Could activity in one brain area influence the flow of information in another area?

 

 
 
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Learning requires association of information from different co-active brain areas during sensory experiences. At the level of the cell, this could occur through integration and association of inputs that coincide in time or space and influence the neuron’s output. Knowing how the connectivity and activity patterns of the different neurons forming a circuit could predict functions like associational plasticity, which typically requires paired activation of multiple inputs, is highly advantageous to study how the circuit contributes to cued learning.

What is the link between associational plasticity and learning behaviors?

 

 
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A single hippocampal CA1 pyramidal neuron receives several different inputs (glutamatergic, GABAergic) from various sources (hippocampus, entorhinal cortex). Glutamatergic inputs drive excitation in a circuit; GABAergic inputs typically inhibit the propagation of excitation.

How do local and long-range circuits interact to change the dynamics of excitation and inhibition for acquiring important information?