Dr. Robert Haslinger
Instructor: Harvard Medical School
Associate in Neuroscience:
Massachusetts General Hospital
Dept. Brain and Cognitive Sciences, MIT
|I'm a physicist currently working in the field of computational neuroscience. My fundamental interest is how the collective, emergent dynamics of neurons in the brain gives rise to complex information processing. Although indiviudal neurons are very noisy, organisms can
reliably perceive sensory stimuli and quickly compute appropriate
behavior to ensure their survival. Such
robust computation relies upon the
coordinated, collective activity of many neurons passing information
back and forth. Recent experimental advances have allowed hundreds, and
presumably soon, thousands or more neurons to be simultaneously
recorded, providing a new opportunity to study how large neuronal
networks coordinate their member neurons activity. Yet it is not
currently clear, which aspects of such network activity are
computationally relevant or how to tease out, from noisy, undersampled
data, the dynamic, functional structures relevant for cognitive processing.
Thus the present challenges in examining such complex data are twofold. First the development of new computational tools that can detect structured patterns in large data sets of recorded neural activity. Second, to develop new theories and hypotheses for how recurrent spiking networks might compute so that we know what types of structure we're looking for. Towards the first goal I use techniques from data science (statistics and machine learning) to develop practical analysis methodologies that can extract, from neural activity recorded in behaving animals, the collective dynamics that large networks use to process information. In collaboration with co-PIs who perform animal experiments, I have applied these methods to a wide variety of neural systems to determine how real neuronal networks compute. Towards the second goal I use methods from complex systems and statistical physics to research theoretical models of information processing and computation by complexly structured neural networks. Such work aims to provide a theoretical framework to identify and explain common computational principles underlying apparently diverse cortical areas and functions.
For more detailed information on my research click here.