Statistical modeling of neural data

The currently inscrutable computations performed by the brain in constructing perception are ultimately derived from operations that can be performed by single neurons. Despite their overall diversity, many neurons share the same basic properties, and perform the same operations, albeit with different inputs. Sensory neuron “computation” thus might be largely comprised of a canonical set of nonlinear operations, acting on the unique inputs of each neuron. Characterization of such computation is possible by knowing its inputs, and having an appropriate statistical framework to capture the each neuron’s underlying computational components. We have been developing statistical modeling frameworks to use neural data recorded in complex experimental contexts to constrain nonlinear models of their processing. Such tools are widely applicable to both intracellular and extracellular recordings, single neurons and populations, and additional experimentally recorded variables such as task-specificity, eye-movements, and local field potential recordings.

Nonlinear Input Model (NIM)

  • Code and tutorials
  • McFarland JM, Cui Y, Butts DA (2013) Inferring nonlinear neuronal computation based on physiologically plausible inputs. PLoS Computational Biology 9: e1003143. [PDF]
  • Lochmann T, Blanche TJ, Butts DA (2013) Pooling of local features as a basis for direction selectivity in the primary visual cortex. PLoS One 8: e58666. [PDF]
  • Schinkel-Bielefeld N, David SV, Shamma SA, Butts DA (2012) Inferring the role of inhibition in auditory processing of complex natural stimuli. Journal of Neurophysiology 107: 3296. [PDF]
  • Butts DA, Weng C, Jin JZ, Alonso JM, Paninski L (2011) Temporal precision in the visual pathway through the interplay of excitation and stimulus-driven suppression. Journal of Neuroscience 31: 11313-27. [PDF]

Eye-tracking using V1 neural activity

  • Demo code
  • McFarland JM, Bondy AG, Cumming BG, Butts DA (2014) High-resolution eye tracking using V1 neuron activity. Nature Communications 5: 4605. [Online]

Using "network activity" to improve model predictions

  • Cui Y, Liu L, McFarland JM, Pack CC, Butts DA (2016) Inferring cortical variability from local field potentials. Journal of Neuroscience 36: 4121-4135. [Journal website; also see SFN 2014 poster]