What the lab works on (& why)
Broadly speaking, we like to work on interesting
neuroscientific questions that involve thinking about neural
circuits, neural coding, and behavior in normal animals as well
as animals with aberrant brain states. Although broad by design,
this description serves as a unifying principle for everything
that we do. At the moment, the lab focuses on stimulus
selection for attention, and collaboratively, on the
neural coding of affective states.
Selection is a singular and categorical event. One among
numerous alternatives is chosen, while all the others are
discarded (equivalently, each alternative can either be either
chosen or not). Any neural hypothesis about selection must,
therefore, involve competition among the representations of
multiple alternatives, and must involve mechanisms that yield
the categorical outcome of one alternative being chosen at the
expense of all others. These mechanisms must operate in real
time, changing with the changing stimuli in the environment.
They must be amenable to plasticity, as the needs of an animal
change over time. A big research thrust in the lab is to
understand how the brain, at the level of neural circuits,
accomplishes selection. Because selection is integral to many
complex cognitive functions such as attention, decision making,
and perception, the study of the neural mechanisms of selection
in different contexts has fundamental implications to the
understanding of circuit substrates of psychiatric disorders
such as ADHD, autism, and schizophrenia, and has the potential
to inform the development of therapeutic strategies.
A parallel research thrust in the lab is to investigate
fundamental issues in the design of neural circuits.
Considering the evolutionary richness in the animal world,
investigating how different species implement neural solutions
to aspects of behavior that are common across species and
critical for survival, such as attention, reward learning,
spatial navigation, etc is an extremely valuable endeavor. Such
study has the potential to yield deep insights into basic brain
function, and into the human condition. To complement current
knowledge in the neural bases of these behaviors, the bulk of
which comes from studies in mammals, we study these questions in
birds.
Finally (and on a very different note), a metaphor that is
commonly used for thinking about the brain is that it is a
biological computer. However, there are fundamental differences
between how a brain is designed and constructed, and how a
computer is designed and put together, thereby limiting the
extent to which this metaphor works. Importantly, brains can
perform "easily" certain computational tasks that are considered
to be hard for traditional computers. Could the differences in
how brains and computers are built account for these differences
in performance? If so, can we draw insights from studying brains
and use them to build more efficient, versatile and powerful
computing machines? We like to speculate/think about these questions
at the interface of AI and BI, and look forward to the day
when we will start investigating some of them.