Event:

Due to the closed loop relationship between neural activity and
behaviour, it is difficult to establish whether a signal in the brain
drives a behavioural outcome or merely reports it, and it is even harder
to manipulate brain activity in a systematic way. I will present our
recent attempts to study nervous systems in a closed loop setting at two
very different levels, and the challenges we encountered:

(1) Using ideas from system identification, we are constructing
data-driven predictive models of neural activity. We focus on the
dynamics of small central pattern generating circuits that control
movement. The internal dynamics of these circuits is extremely rich, and
this presents an obstacle to building predictive, data driven models
with "the right level" of detail. I will give intuition behind out
approach, the challenges, and our efforts use them to control the
activity of living neural circuits in real time.

(2) I will describe brain-machine interfaces (BMI) we developed to
understand how abstract representations of the environment are used by
rodents to navigate. I will show how the neural code can be surprisingly
simple in some brain areas, enabling robust decoding with no learning
required by the animal. I will also show how adaptive properties in
other neural circuits can make them extremely sensitive to coupling via
brain-machine interfaces, to the extent that the code itself may
reconfigure completely during BMI use.