Timothy O'Leary: Closed loop, data driven neurophysiology: from single neurons to brain-machine interfaces.

University of Cambridge

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.

 

Guests are welcome!

 

Organized by

Henning Sprekeler / Margret Franke