Collections of neurons organize their activity to perform a variety of computational, cognitive, and behavioral tasks. The 'neuro'-mechanics that underlie neural computation is the primary interest of our group. We use a combination of statistical mechanics, nonlinear system theory, and information theory to study this broad field.
Of particular interest is the genesis and impact of neural variability on sensory processing and computation. In collaboration with experimentalists working in visual, somatosensory, auditory, olfactory, and electrosensory systems we study general principles of sensory transduction in populations of neurons. The circuit architecture, nonlinearities inherent in spike transfer, and plasticity of synaptic response allow variability and synchrony to be shaped by stimuli as well as neural state. Understanding the specific mechanisms that mediate this shaping, and the consequences for population coding, are a central challenges for sensory neuroscience.
Litwin-Kumar, A., and Doiron, B. Slow Dynamics and High Variability in Balanced Cortical Networks with Clustered connections. Nature Neuroscience 15: 1498-1505, 2012.
Rosenbaum, R., Rubin, J., and Doiron, B. Synaptic filtering with short term depression. PLoS Computational Biology. 8(6): e1002557. doi:10.1371/journal.pcbi.1002557, 2012.
Polk, A., Litwin-Kumar, A., and Doiron, B. Correlated Neural Variability in Persistent State Networks. Proceedings of the National Academy of Sciences (USA), 109:6295-6300, 2012.
de la Rocha, J.*, Doiron, B.*, Shea-Brown, E. Josic, K. and Reyes, A. Correlation between neural spike trains increase with firing rate. Nature. 448: 802-806, 2007.
Doiron, B., Chacron, M.J., Maler, L., Longtin, A., and Bastain, J. Inhibitory feedback required for network oscillatory responses to communication but not prey stimuli. Nature 421:539-543, 2003.