Modelling SingleNeuron Dynamics and Computations: A Balance of Detail and (2008)
Et Al, Tim Gollisch, Christian K. Machens, Dieter Jaeger
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2Institute for Theoretical Biology (2008)
Christian K. Machens, Tim Gollisch, Olga Kolesnikova, On The Sys, The Time-fre
tem’s level, the hypothesis has helped to elucidate the
LETTER Communicated by Andreas Andreou Energy-Ef�cient Coding with Discrete Stochastic Events (2008)
Susanne Schreiber, Christian K. Machens, Simon B. Laughlin
We investigate the energy ef�ciency of signaling mechanisms that transfer information by means of discrete stochastic events, such as the opening or closing of an ion channel. Using a simple model...
Spectro-temporal receptive fields of subthreshold responses in auditory cortex (2003)
Christian K. Machens, Michael Wehr, Anthony M. Zador
How do cortical neurons represent the acoustic environment? This question is often addressed by probing with simple stimuli such as clicks or tone pips. Such stimuli have the advantage of yielding...
editorial focus Auditory Modeling Gets an Edge (2003)
Christian K. Machens, Anthony Zador
Wiesel 1962), visual neurophysiologists have had a powerful model for understanding responses of neurons in the primary visual cortex. According to this model, simple cells in the visual cortex...
Adaptive sampling by information maximization (2001)
The investigation of input-output systems often requires a sophisticated choice of test inputs to make best use of limited experimental time. Here we present an iterative algorithm that continuously...
Representation of Acoustic Communication Signals by Insect Auditory Receptor Neurons (2001)
Christian K. Machens, Martin B. Stemmler, Petra Prinz, Rüdiger Krahe, Bernhard Ronacher
ns of natural songs do not exceed those obtained from artificial stimuli with the same low-order statistical properties. We conclude that auditory receptor neurons are optimized to extract both the...