Untitled - Laboratory of Neurophysics and Physiology

[1] Donoho et al. (2009), 15 Years of Reproducible Research in Computational Harmonic Analysis,
Computing in Science and Engineering 11:8–18, doi:10.1109/MCSE.2009.15
[2] http://git-scm.com
[3] http://mercurial.selenic.com
[4] http://neuralensemble.org/sumatra
[5] http://www.vistrails.org
[6] http://www.g-node.org/projects/odml
[7] http://neuralensemble.org/neo
[8] Pazienti & Grün (2006), Robustness of the significance of spike correlation with respect to
sorting errors, Journal of Computational Neuroscience 21:329–342
[9] Louis et al. (2010), Generation and selection of surrogate methods for correlation analysis,
In: Analysis of parallel spike trains, Grün & Rotter (eds.), Springer Series in Computational
Neuroscience
[10] Louis et al. (2010) Surrogate spike train generation through dithering in operational time, Front
Comput Neurosci 4:127, doi:10.3389/fncom.2010.00127
T9
Massively parallel time encoding and channel identification machines
Space Grignard, (9.00-12.20; 13.30-16.50)
Aurel A. Lazar, Columbia University, New York, US
This two part tutorial focusses on Time Encoding Machines (part I) and Channel Identification Machines
(part II). The tutorial will give an overview of (i) nonlinear decoding of stimuli encoded with
neural circuits with biophysical neuron models, (ii) functional identification of biophysical neural circuits,
and (iii) the duality between the two. Scaling to massively parallel neural circuits for both
encoding and functional identification will be discussed throughout. The tutorial will provide numerous
examples of neural decoding and functional identification using the Time Encoding Machines
Toolbox and the Channel Identification Machines Toolbox. Tutorial material, programming code and
demonstrations will be provided.
Part I: Time Encoding Machines
The nature of the neural code is fundamental to theoretical and systems neuroscience [1]. Can information
about the sensory world be faithfully represented by a population of sensory neurons
What features of the stimulus are encoded by a multidimensional spike train How can these features
be decoded Why does the cochlear nerve carry some 30,000 fibers and the optic nerve
some 1,000,000 We will discuss these questions using a class of neural encoding circuits called
Time Encoding Machines (TEMs) [2]. TEMs model the encoding of stimuli in early sensory systems
with neural circuits with arbitrary connectivity and feedback. These circuits are realized with
temporal, spectro-temporal and/or spatio-temporal receptive fields, and biophysical neuron models
with stochastic conductances (Hodgkin-Huxley, Morris-Lecar, etc.) [3,4,5,6]. The tutorial will
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