Zrenner Lab

Experimental Retinal Prothetics Group

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Sudarshan Sekhar

First nameSudarshan
Present position and titlePh.D. student,  M.Sc (Neuroscience)

Business address

Experimental Retinal Prothetics Group
Institute for Ophthalmic Research
University of Tübingen
Elfriede-Aulhorn-Strasse 7
D-72076 Tübingen,

Phone: +49 (0)7071 29-87789
Fax: +49 (0)7071 29-4472
E-mail: sudsa89[at]gmail.com

Academic Education

Year Degree University Field of study
2013-present Ph.D.
Graduate School of Neural and Behavioural Sciences IMPRS (International Max Planck Research School)
University of Tübingen Neuroscience
2011-2013 M.Sc University of Tübingen Neuroscience
2007-2011 Bachelors in Engineering (B.E) Anna University, Tamil Nadu, India Electrical Engineering

PhD project description

I started my PhD project in October of 2013. The main goal of my project is to have a better theoretical understanding of early retinal coding and to then use this knowledge to design the next generation retinal prosthesis.

My thesis work will involve performing my own experiments (in-vitro extracellular recording from mouse retina, where I separately stimulate the retina both visually and electrically) and then fitting the data that I obtain (retinal ganglion cell spike times) with a statistical model such as a GLM (generalised linear model). Information theory will also be used to quantify the amount of bits of information carried by the spikes. This will help evaluate how well the stimuli drive the cells that are being recorded.

My project will involve some statistical machine learning and I will be taking the appropriate course work for this. I am also working under the active guidance of Dr Jakob Macke who is on my thesis committee.

Awards / Scholarships

10/ 2011 - 03/2013Winner of the Max Planck Scholarship for international students

Additional information

Coding Experience

  1. C/C++
  2. Matlab

Language Proficiency

  1. Level B1 in German from the Goethe Institute, Chennai.

Research Interests

  1. Visual Prosthetics
  2. Motor Prosthetics
  3. Neurophysiology
  4. Medical Imaging
  5. Neuroengineering
  6. Statistical Modelling

Workshops Attended

Attended the 6th G-Node Winter Course on Neural Data Analysis in Munich, which ran from the 24th of February till the 28th of February 2014.
In this workshop I covered in depth the following areas of computational neuroscience

  1. Data Analysis of Short-Term Synaptic Plasticity

    1. Simulated the 1997 Tsodyks-Markram model for short term synaptic depression [5]. Simulated neuronal dynamics, oscillations based on coupled differential equations. Using this simulation I studied the effect of parameters such as time constant for recovery, release probability etc on neuronal oscillatory dynamics.
    2. Fit the parameters to the above mentioned model using Matlab’s fminsearch function and determined the accuracy of the fit parameters using the jackknife approach, by calculating the coefficient of variation.

  2. Analysis of Directional Tuning in Single Unit Activity from the Monkey Motor Cortex

    1. Constructed PSTH of the activity of a single neuron in the motor cortex of a monkey under 6 different directions of hand reaching. Based on these PSTH constructed the tuning curve of the neuron against 6 different directions. Calculated the Signal to Noise ratio from these tuning curves.
    2. Calculated the vector of preferred direction of motion for the above neuron based on the PSTH and tuning curves.
    3. Estimated the PSTH of a neuron from single trial spike times by convolving the spike times with various filters such as Gaussian and boxcar.

  3. Information Theory and Decoding

    1. For a given data set, calculated the mutual information by calculating the probabilities of the various outcomes, and then the corresponding entropy distributions.

  4. Spectral Analysis of Spike Responses

    1. Calculated the power spectrum of p-unit responses in electric fish. Analysed how this spectrum varied under different windowing functions.
    2. Calculated the coherence between the stimulus and response for a neuron in the electric fish across multiple trials. Based on this trial averaged coherence calculation, estimated the amount of noise in the response and nonlinear encoding that was taking place in the neuron of the electric fish.



  1. Energetics Based Simulation of 3D Neuronal Networks : Energetics Based Models
  2. Energetics Based Simulation of 3D Neuronal Networks : Neurogenesis Inspired Structure Generation.
    Presented both posters in Kobe, Japan, from the 30th of August to the 1st of September, at the 3rd International Neuroinformatics Conference. The abstracts were published in the journal, Frontiers of Neuroscience.
  3. Optimization of Electrical Stimulation of Retina to Generate Naturalistic Visual Responses
    Presented the poster at NeNa (Neurowissenschaftliche Nachwuchskonferenz - Conference of Junior Neuroscientists) in November of 2012.


  1. Venkateswaran, N., Sekhar, S., ThirupatchurSanjayasarathy, T., Krishnan, S. N., Kabaleeswaran, D. K., Ramanathan, S., Narayanasamy, N., Jagathrakshakan, S. S., and Vignesh, S. R. (2012). Energetics based spike generation of a single neu-ron: simulation results and analysis. Front. Neuroenergetics 4:2. doi: 10.3389/ fnene.2012.00002
    The paper was published in the frontiers in Neuroenergetics and can be accessed online


  1. Pillow JW, Paninski L, Uzzell VJ, Simoncelli EP, Chichilnisky EJ (2005) Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model. J Neurosci 25: 11003–13.
  2. Magri C, Whittingstall K, Singh V, Logothetis NK, Panzeri S (2009). A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings. BMC Neurosci. 2009 Jul 16;10:81.
  3. Berry MJ, Warland DK, Meister M. (1997). The structure and precision of retinal spike trains. Proc Natl Acad Sci U S A. 1997 May 13;94(10):5411-6.
    4. Victor JD (2005). Spike train metrics. Curr Opin Neurobiol. 2005 Oct;15(5):585-92.
  4. Tsodyks M, Markram H. (1997). The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. Proc. Natl. Acad. Sci. USA, Vol. 94, pp. 719–723, January 1997 Neurobiology.