A fundamental question in neuroscience is how volitional actions emerge from neural circuits in the brain. Answers to this and other 'big' questions will begin to unfurl in the 21st century thanks to improved data acquisition techniques which permit the sampling of hundreds to thousands of cells simultaneously. A looming problem, however, is that datasets will soon grow so large that a new generation of analytical tools will also be required. The central aim of this project, to understand how parietal cortical ensemble activity maps onto the freely-behaving body, will make use of recent advances in markerless 3D motion tracking and large-scale in vivo calcium imaging in freely behaving mice. In order to analyze datasets of this scale, we will develop novel machine learning algorithms to correlate neuronal activity patterns with key movement features such as changes in joint angles. The major goals of the project include the synchronization of neural and behavioral recordings from mice foraging in an open arena. Once sufficient data is collected, the next goal will be to implement 'deep belief' machine learning algorithms which will be trained to recognize patterns between the neural and behavioral datasets. To this end, we have the key advantage of collaborating with a field-leading expert in deep belief algorithms who focuses on modeling biological movement. Finally, we will determine if we can apply our new tools to accurately foretell an animal's autonomously chosen trajectory when it is presented with a navigational goal. By imaging neural activity during a variety of behaviors, we aim to make a quantum leap forward in understanding the cortical representation of a wide spectrum of whole-body movements which comprise an animal's waking behavior. The computational tools developed in the process could be applied to study any number of behaviors in wild-type or disease-model mice, and could even be applied in developing clinical neural prostheses.
Project leader: Jonathan Whitlock
Institution: Kavliinstitutt for nevrovitenskap