What happens inside the neurons that control our motivational states?
When we say we are so hungry we could eat a horse, we do not mean an actual horse. Instead, we are expressing that we 1) prioritize feeding over other behaviors; 2) are willing to accept a higher cost of food; 3) will take more joy in eating; 4) eat more vigorously; and 5) will eat a larger meal. These effects are controlled by different sub-systems of the hunger circuit. While some are snap decisions, others require tracking behavioral variables over longer timescales: How much have I eaten? Is the food richer than I expected? How have my other needs changed? When should I stop?
Our central hypothesis is that these slow behavioral variables are tracked and predicted using intracellular signaling molecules that evolve over similar timescales. Inside the neurons of motivational circuits, the rise and fall of these intracellular signals (e.g., cAMP) are controlled by inputs of neuromodulators, peptides, and hormones (e.g., GPCRs). In turn, these intracellular signaling molecules regulate the spiking activity of the neurons and ultimately behavior.
Our goal is to combine the fields of systems neuroscience and cell signaling to understand how motivational dynamics are computed biochemically in intact neural circuits, in real time, and during behavior.
We study the mammalian hypothalamus.
At the base of a vertebrate brain, the hypothalamus is a hub for innate behaviors. If a miniature version of you could take a stroll from the anterior to the posterior end of the hypothalamus, you would find somewhat demarcated nuclei that regulate body temperature, thirst, mating, parenting, sleeping, circadian time, feeding, energy expenditure, and aggression behaviors. We know these nuclei control innate behaviors and basic needs, and the associated neurocircuits use both fast and slow modes of transmission (e.g., glutamate and neuropeptides). We use such innate neurocircuits as models to investigate biochemical computations that occur at the interface between fast and slow transmissions, ultimately shaping behaviors over seconds, minutes, hours, and days.
Turn problems of biology into problems of light.
We apply the tools of systems neuroscience in our research. We use light-activated sensors and enzymes to measure and manipulate cellular signals deep inside the brain of a behaving mouse. We perform these experiments using in vivo 2p GRIN-lens imaging, in vivo 2p-FLIM imaging, fiber photometry, 2p-FLIM brain-slice imaging, and optogenetics. We collaborate with sensor and optogenetic bioengineers to push technological boundaries, while customizing in-house behavioral, hardware, and analysis software to suit the needs of individual experiments.
Some of our open-source repositories include 1) Nanosec photometry system, 2) picoDAQ data acquisition hardware, 3) a FLIM analysis package, 4) a photometry analysis package, 5) a mouse feeding control system, 6) a brain-slice LED-controller package.
Our first-principle questions.
Below are some of the fundamental questions regarding the first principles of biochemical computation.
How are computational elements (e.g., timers, filters, integrators) made using biochemical signals and used for behaviors?
How may the spatiotemporal characters of the same computational element be fine-tuned in different circuits (e.g., changing the lengths of biochemical timers)?
In what ways can biochemical computation be plastic and molded by experience?
How are different intracellular signals (e.g., calcium and cAMP) interfaced with each other in the context of behavioral computation?
How can the same signaling pathway be used for different purposes in the same cell?
What are the general principles of interactions between fast and slow signals?
How do we use biochemical knowledge to develop new treatments for motivational disorders?
Our work contributes to a new school of motivational biology: we study the difficult problems of tracking and predicting needs, and find algorithmic solutions using principles of cell signaling in space and in time.
“The worthwhile problems are the ones you can really solve or help solve, the ones you can really contribute something to. … No problem is too small or too trivial if we can really do something about it.”
Richard Feynman, Perfectly Reasonable Deviations from the Beaten Track