New Directions in Motor Control Workshop

Emory Conference Center Hotel | May 18-19, 2017

Tutorials are designed to familiarize experimentalists with cutting-edge concepts and tools that they can use to analyze their behavioral and physiological data. 

Please note that tutorials are held concurrently in time so it is possible to attend only one.


Ilya Nemenman
Information theoretic analysis of neural spike trains

Although nearly all current models of motor encoding assume a spike rate code, recent studies in a number of species have demonstrated that precise (millisecond-scale) spike timing patterns can be far better at predicting — and controlling — behavior. This tutorial will present both established and novel analytical techniques for assessing how precise spike timing patterns encode motor output.

The session is aimed at experimental neurophysiologists interested in analyzing single neuron and multi-neuronal data with the goal of understanding which spike statistics (rate, timing, specific multi-spike patterns) contribute significantly to the neural code. Participants will be provided with both sample data and MATLAB code to familiarize them with applications of these techniques.

Related papers (not necessary to read before the tutorial):
  1. Srivastava K, Holmes CM, Vellema M, Pack A, Elemans C, Nemenman I, and Sober SJ (2017). Motor control by precisely timed spike patterns. PNAS.
  2. Tang C, Srivastava K, Chehayeb D, Nemenman I, and Sober SJ (2014). Precise temporal encoding in a vocal motor system. PLoS Biology.
  3. Nemenman I, Lewen GD, Bialek W, and de Ruyter van Steveninck RR (2008). Neural coding of natural stimuli: information at sub-millisecond resolution. PLoS Computational Biology.


David Hofmann
Application of generalized linear models to spike trains

Generalized Linear Models (GLMs) are a simple yet powerful stochastic modeling tool. Among many other applications, they have been used in neuroscience to model receptive fields and effective connectivity between neurons. In this tutorial we will review the formal framework, compare GLMs to other neuronal models, discuss a link to information theory, and review some of the limitations of GLMs. We will learn to use the GLM implementation provided in the Python toolbox "statsmodels" and reproduce previously published results. Participants are also invited to bring their own data for analysis.

 Related papers (not necessary to read before the tutorial):
  1. Truccolo, Wilson, Uri T Eden, Matthew R Fellows, John P Donoghue, and Emery N Brown. “A Point Process Framework for Relating Neural Spiking Activity to Spiking History, Neural Ensemble, and Extrinsic Covariate Effects.” Journal of Neurophysiology 93, no. 2 (February 2004): 1074–1089. doi:10.1152/jn.00697.2004.
  2. Pillow, Jonathan W, Jonathon Shlens, Liam Paninski, Alexander Sher, Alan M Litke, E J Chichilnisky, and Eero P Simoncelli. “Spatio-Temporal Correlations and Visual Signalling in a Complete Neuronal Population.” Nature 454, no. 7207 (August 2008): 995–9. doi:10.1038/nature07140.
  3. Williamson, Ross S., Maneesh Sahani, and Jonathan W. Pillow. “The Equivalence of Information-Theoretic and Likelihood-Based Methods for Neural Dimensionality Reduction.” PLOS Computational Biology 11, no. 4 (April 1, 2015): e1004141. doi:10.1371/journal.pcbi.1004141.
  4. Cessac, B, and R Cofré. “Spike Train Statistics and Gibbs Distributions.” Journal of Physiology, Paris 107, no. 5 (November 2013): 360–8. doi:10.1016/j.jphysparis.2013.03.001.


Audrey Sederberg
Beyond Spikes: Insights from local field potential recordings in electrode array data

The goal of this tutorial is to provide an overview of methods for analyzing local field potentials (LFPs) acquired from multi-electrode arrays. These will include the following: inverse current source density methods for source localization, classification methods for identifying neural signatures of brain state, and building models based on the LFP to predict trial-by-trial variability of spiking responses. We will work through examples in Matlab using sample data from recordings in rodent somatosensory cortex. The intended audience of this workshop is experimentalists who want to expand their computational toolbox to include advanced strategies for interpretation of the LFP.

Related papers (not necessary to read before the tutorial):
  1. Cui, Y., Liu, L. D., McFarland, J. M., Pack, C. C., & Butts, D. A. (2016). Inferring Cortical Variability from Local
  2. Field Potentials. Journal of Neuroscience, 36(14), 4121–4135.
  3. Herreras, O., Makarova, J., & Makarov, V. A. (2015). New uses of LFPs: Pathway-specific threads obtained through spatial discrimination. Neuroscience, 310, 486–503.
  4. Pettersen, K. H., Devor, A., Ulbert, I., Dale, A. M., & Einevoll, G. T. (2006). Current-source density estimation based on inversion of electrostatic forward solution: Effects of finite extent of neuronal activity and conductivity discontinuities. Journal of Neuroscience Methods, 154 (1–2), 116–133.


Gordon Berman
Quantitative behavioral analysis using dimensionality reduction

Amid the development of increasingly sophisticated and detailed tools for measuring and manipulating neural circuitry, our ability to characterize animal behavior, the ostensible output of these circuits, has lagged considerably. In recent years, however, a number of researchers have devised novel tools that aim to bring behavioral analyses to a level of quantitative description that allow for greater links between an animal’s actions and the physiology that underlies them.

In this tutorial, we will explore one such method in detail: behavioral space mapping, which takes movies of freely-behaving animals and represents their full repertoire of movements as peaks and valleys in a 2D plane without using any human annotations. Using provided code, we will apply these analyses to two sample data sets one invertebrate and one vertebrate.

Participants are encouraged to bring examples of their own animal behavior data, as the last part of the tutorial will involve a group discussion about how to capture data that is amenable to these analyses and how to apply the methods once that data is collected.

Related papers (not necessary to read before the tutorial):
  1. Berman GJ, Bialek W, and Shaevitz JW (2016). Predictability and hierarchy in Drosophila behavior. PNAS
  2. Berman GJ, Choi DM, Bialek W, and Shaevitz JW (2014). Mapping the stereotyped behaviour of freely moving fruit flies. J R Soc Interface.