Data Analysis Course 2014

This 6th G-Node Course on Neural Data Analysis comprises four course modules, one module for each day. Each module treats a specific topic that will be presented by one faculty member. Each module includes an introductory lecture plus a total of six hours of practical course work. All students are required to prepare in advance on the basis of the introductory reading lists below.

Alex Loebel (Ludwig-Maximilians-Universität München)

Synaptic responses to a series of pre-synaptic action-potentials are complex and non-linear. This phenomenon, termed short-term synaptic plasticity, is observed at all stages of the nervous system, from synaptic connections at the spinal cord to neocortical connections. Some connection types exhibit short-term depression, in which the responses diminish with the stimulus series; while other types exhibit short-term facilitation, in which the responses facilitates. In this module we will learn how to model such responses, and how to extract the models' parameters from experimental data. Using the Jackknife statistical method we will evaluate the accuracy of our fits. Time permitting, we will also discuss the stochastic version of these models, with which the quantal parameters of synapses can be estimated.

Literature:

• Tsodyks M, Markram H (1997). The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. PNAS, 94:719-23. [pdf].
• Markram H, Wang Y, Tsodyks M (1998). Differential signaling via the same axon of neocortical pyramidal neurons. PNAS, 95:5323-8. [pdf]
• Loebel et al. (2009). Multiquantal release underlies the distribution of synaptic efficacies in the neocortex. Front Comput Neurosci, 3:27. [pdf]

Script
Matlab data files for the different exercises
Matlab script file, for exercise (1i)
Matlab script file, for exercise (2ii)
Matlab script file, for exercise (2iii)
lecture

Thomas Wachtler (Ludwig-Maximilians-Universität München),
Martin Nawrot (Freie Universität Berlin)

This course module introduces the students to the analysis of directional tuning in single unit recordings from the motor cortex of the behaving monkey. Goal of the module is to understand basic concepts of neuronal tuning curves and rate coding, and to practice the analysis of single-unit spike train data. Students will analyze single unit activity recorded from the monkey primary motor cortex during the preparation and execution of reaching movements.

Literature:

• Georgopoulos AP, Kalaska JF, Caminiti R, Massey JT (1982) On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex. J Neurosci. 1982 Nov;2(11):1527-37. [pdf]
  
• Nawrot M, Aertsen A, Rotter S (1999) Single-trial estimation of neuronal firing rates: from single-neuron spike trains to population activity. J Neurosci Methods. 1999 Dec 15;94(1):81-92. [pdf]
  
• Rickert J, Riehle A, Aertsen A, Rotter S, Nawrot MP (2009) Dynamic encoding of movement direction in motor cortical neurons. J Neurosci. 2009 Nov 4;29(44):13870-82. [pdf]

Lecture
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Fabian Sinz (Eberhard-Karls-Universität Tübingen)

Originally developed in the 1940ies for applications in communication, information theory has become a standard tool in computational neuroscience where it is, for instance, used to quantify the available information about a stimulus in responses of neurons, or to extract those features in the stimulus that carry most information about the neural response. In the course, we will look at simple scheme how to estimate a lower bound on the information between stimulus and the corresponding neural response which uses the tight link between information and the notion of predictablity. We will apply this method to recordings from macaque V1 neurons from the paper by Berens et al. listed in the references below.

Literature:

• Quian Quiroga, R., & Panzeri, S. (2009). Extracting information from neuronal populations: information theory and decoding approaches. Nat Rev Neurosci, 10(3), 173–185. doi:10.1038/nrn2578 [pdf]
• Berens, P., Ecker, A. S., Cotton, R. J., Ma, W. J., Bethge, M., & Tolias, A. S. (2012). A fast and simple population code for orientation in primate V1. Journal of Neuroscience, 32(31), 10618–26. doi:10.1523/JNEUROSCI.1335-12.2012 [pdf]
• Bishop, C. M. (2007). Pattern Recognition and Machine Learning (Information Science and Statistics). Springer. Chapter 4. In particular logistic regression [pdf]

Script
Data, toolbox, and exercises
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Jan Grewe (Eberhard-Karls-Universität Tübingen)

In this module spectral methods like power-spectrum estimation, reverse reconstruction and the coherence will be introduced. We will apply these methods to data recorded in electroreceptors of weakly electric fish to reconstruct the stimulus from the responses and to estimate the relation between the stimulus and the neuronal response.

Literature:

• Borst A, Theunissen FE (1999) Information theory and neural coding. Nat Neurosci. 1999 Nov;2(11):947-57. [pdf]
• Middleton JW, Longtin A, Benda J, Maler L (2006) The cellular basis for parallel neural transmission of a high-frequency stimulus and its low-frequency envelope. Proc Natl Acad Sci U S A. 2006 Sep 26;103(39):14596-601.[pdf]
• McDonnell MD, Ward LM (2011) The benefits of noise in neural systems: bridging theory and experiment. Nat Rev Neurosci. 2011 Jun 20;12(7):415-26. [pdf]

Script
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solutions
lecture
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