Research

My neuroscience research is focused on understanding neural computations and information flow in brain networks.

Neuron activity

What are neural computations?

The fundamental unit of information in the cortex is a pattern of activity across many neurons. These activity patterns propagate through neural circuits, and are changed by the circuits. Those changes are neural computations. My long-term goal is to understand the computations in brains and understand how these computations create perception, decision, and action.

How do we approach these goals?

We develop cutting-edge experimental tools like two-photon holographic stimulation to analyze networks and population activity. Causal interventions are critical to understanding highly interconnected networks.

We collaborate with theoretical groups and use large-scale network simulations to enable close experiment-theory interactions that drive the science forward.

We are inspired by the architecture and computations of machine learning and artificial intelligence (ML, AI) systems. AI systems also perform computations at a network level. Is this process similar to the brain? Similarly to how “interpretable AI” approaches seek to understand AI computations with causal perturbations, how can we use our two-photon optogenetic approaches to best understand brain function?

Current Research Themes

Cortical network process illustration

How do cortical networks process dynamic input?

We have found that the cortical recurrent network performs sequence filtering

How does learning change networks and computation?

We are examining plasticity in recurrent brain networks using cellular-resolution stimulation.

Beyond the cortex, how do other neural architectures in other brain areas enable the function of those areas?

We are using endoscopic imaging and stimulation to study this.

How do multiple brain areas interact to represent information and control information?

We are examining brain area interactions using mesoscopic two-photon imaging, behavioral assays, and two-photon perturbations.

Research Highlight: Inhibitory-Stabilized Networks

We have made progress understanding network operation in the cortex.
Cortical networks are strongly coupled: 

Excitatory and inhibitory diagram

What this means in the brain:

  • The cortex operates in a balanced state where neurons receive large amounts of input.

  • Networks of realistic (conductance-based) require several scaling rules — of synaptic speed and also synapse number — to create balanced-state network operation. [Sanzeni, Histed & Brunel, 2022].

  • Balanced state/inhibition-stabilized operation of cortical operation renders excitatory cells sensitive to both increments and decrements of input. 

  • Suppression can result from withdrawal of input from other excitatory cells with little or no change in inhibitory firing.

Tools & Scientific Approaches

Some of the approaches we use to gain insight into recurrent networks and neural computation include:

Foundational Research

My current research builds on discoveries from my prior work in:

Two-photon stimulation changes patterns of neural activity in the brain.