Short Course 2017

Course Format and Course Modules

This 8th G-Node Short 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.

Module I : Spectral analysis of neuronal responses

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;2(11):947-57.
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.
Grewe J., Kruscha A., Lindner B., Benda J. (2017) Synchronous spikes are necessary but not sufficient for a synchrony code. PNAS; 114: E1977-E1985

Module II : Analysis of spike responses of simultaneously recorded populations

Jannis K. Hildebrandt (Carl von Ossietzky Universität Oldenburg),

The developement of extracellular electode arrays with growing number of sites enable simultaneous recordings of ever larger neural populations. Data obtained with such probes requires analysis that goes beyond the processing of one unit at a time, but needs to take into account how responses in different units relate to each other and the population activity.

In this module, we will analyze data obtained from electrode arrays placed in primary auditory cortex of awake mice, typically yielding 30-40 simultanously recorded single units. We will start with looking at pairwise correlations (noise and signal correlations). We will then go on to analyse how single units responses relate to population activity under different stimulus conditions and acrosse cortical states. Finally, we will try to decode parameters of the auditory stimulus from population responses and validate results.

Literature (required):

Averbeck, B. B., Latham, P. E., & Pouget, A. (2006). Neural correlations, population coding and computation. Nature Reviews Neuroscience, 7(5), 358–366. http://doi.org/10.1038/nrn1888
Okun, M., Steinmetz, N. A., Cossell, L., Iacaruso, M. F., Ko, H., Bartho, P., et al. (2015). Diverse coupling of neurons to populations in sensory cortex. Nature, 521(7553), 511–515. http://doi.org/10.1038/nature14273

Literature (background):

Cafaro, J., & Rieke, F. (2010). Noise correlations improve response fidelity and stimulus encoding. Nature, 468(7326), 964. http://doi.org/doi:10.1038/nature09570
Cohen, M. R., & Kohn, A. (2011). Measuring and interpreting neuronal correlations. Nature Neuroscience, 14(7), 811–819. http://doi.org/10.1038/nn.2842
Kohn, A., Coen-Cagli, R., Kanitscheider, I., & Pouget, A. (2016). Correlations and Neuronal Population Information. Annual Review of Neuroscience, 39(1), 237–256. http://doi.org/10.1146/annurev-neuro-070815-013851

Module III: Analysis of extracellular activity across layers during a perceptual task

Hendrikje Nienborg (Werner Reichardt Centre for Integrative Neuroscience Tübingen)

A central goal in neuroscience is to relate the activity of neurons to a subject’s behavior. In this module we will use approaches based on signal detection theory and apply them to population recordings across cortical layers from macaque visual cortex while the animals are performing a visual task. Additionally, we will introduce an analysis technique that allows for the identification of cortical layers in these recordings.

Literature (required):

David Heeger's handout on signal detection theory: http://www.cns.nyu.edu/~david/handouts/sdt/sdt.html http://www.cns.nyu.edu/%7edavid/handouts/sdt-advanced.pdf
Britten KH, Shadlen MN, Newsome WT, Movshon JA. (1992) The analysis of visual motion: a comparison of neuronal and psychophysical performance. J Neurosci; 12(12):4745-65.

Literature (background):

Britten KH, Newsome WT, Shadlen MN, Celebrini S, Movshon JA. (1996) A relationship between behavioral choice and the visual responses of neurons in macaque MT. Vis Neurosci.; 13(1):87-100.
Pitkow X, Liu S, Angelaki DE, DeAngelis GC, Pouget A. (2015) How Can Single Sensory Neurons Predict Behavior? Neuron; 87(2):411-23. doi: 10.1016/j.neuron.2015.06.033.
Vaknin G, DiScenna PG, Teyler TJ. (1988) A method for calculating current source density (CSD) analysis without resorting to recording sites outside the sampling volume. J Neurosci Methods; 24(2):131-5.

Module IV: What does my population know about the stimulus - decoding analysis and information theory

Fabian Sinz (Baylor College of Medicine, Houston)

Information theory is 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 a simple example of a general 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.