Computational and Theoretical Neuroscience
The experimental techniques in neuroscience have advanced dramatically both in the number of simultaneously recorded cells and the ability to do so in behaving animals in rich environments. New data constantly reveal unseen diversity and complexity of neurons’ response patterns and their dynamics over time. Turning such big data into knowledge about the brain requires two types of mathematical efforts: 1) advanced data analysis methods to uncover structures and to infer biologically important features, and 2) new models and theories to understand the mechanisms and principles of how neural circuits allow the animal to process information and generate behavior. My group applies machine learning, dynamical system, and complex network to questions spanning the spectrum between the two types, with a focus on the ongoing challenge to relate the connectivity patterns between neurons and the dynamics and function of the circuit.
- Ocker*, Y. Hu*, M. Buice, B. Doiron, K. Josic, R. Rosenbaum and E. Shea-Brown. From the statistics of connectivity to the statistics of spike times in neuronal networks. Curr. Opin. Neurobio. doi:http://dx.doi.org/10.1016/j.conb.2017.07.011. 2017.
- Hu, J. Trousdale, K. Josic and E. Shea-Brown. Local paths to global coherence: cutting networks down to size. Phys. Rev. E 89(3): 032802. doi: 10.1103/PhysRevE.89.032802. 2014.
- Hu, J. Zylberberg, and E. Shea-Brown. The sign rule and beyond: Boundary effects, flexibility, and optimal noise correlations in neural population codes. PLoS Comput. Biol. 10(2): e1003469. doi: 10.1371/journal.pcbi.1003469, 2014.
- Hu, J. Trousdale, K. Josic, and E. Shea-Brown. Motif Statistics and Spike Correlations in Neuronal Networks. J. Stat. Mech. P03012. 2013.
- Trousdale, Y. Hu, E. Shea-Brown, and K. Josic. Impact of network structure and cellular response on spike time correlations. PLoS Comp. Biol. 8(3): e1002408. 2012