Event Abstract

Evaluating neuronal codes for inference using Fisher information

  • 1 Max Planck Institute for Biological Cybernetics, NWG Bethge, Germany

Many studies have explored the impact of response variability on the quality of sensory codes. The source of this variability is almost always assumed to be intrinsic to the brain. However, when inferring a particular stimulus property, variability associated with other stimulus attributes also effectively acts as noise. Here we explore the impact of such stimulus-induced response variability for two model cases: a) binocular disparity inference and b) orientation discrimination. We characterize the response distribution for the energy model in response to random dot stereograms and to displays of oriented random dots.For the case of bincular disparity processing we find the response distribution to be very different from the Poisson-like noise usually assumed. We compute the Fisher information with respect to binocular disparity, present in the monocular inputs to the standard model of early binocular processing, and thereby obtain an upper bound on how much information a model could theoretically extract from them. Then we analyze the information loss incurred by the different ways of combining those inputs to produce a scalar single-neuron response. We find that in the case of depth inference, monocular stimulus variability places a greater limit on the extractable information than intrinsic neuronal noise for typical spike counts. Furthermore, the largest loss of information is incurred by the standard model for position disparity neurons (tuned-excitatory), that are the most ubiquitous in monkey primary visual cortex, while more information from the inputs is preserved in phase-disparity neurons (tuned-near or tuned-far) primarily found in higher cortical regions.

Figure 1: A: Disparity tuning curves for the model using position disparity (even) and phase disparity (odd) in blue and red, respectively. B: Black: Fisher information contained in the monocular inputs. Blue: Fisher information left after combining inputs from left and right eye according to position disparity model. Red: Fisher information after combining inputs using phase disparity model. Note that the black and red curves diverge at zero disparity. C: Fisher information for the final model output/neuronal response. Same color code as before. Solid lines correspond to complex, dashed lines to simple cells. D: Same as C but with added Gaussian noise in the monocular inputs.

Figure 1

Keywords: computational neuroscience

Conference: Bernstein Conference on Computational Neuroscience, Berlin, Germany, 27 Sep - 1 Oct, 2010.

Presentation Type: Presentation

Topic: Bernstein Conference on Computational Neuroscience

Citation: Haefner R and Bethge M (2010). Evaluating neuronal codes for inference using Fisher information. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.fncom.2010.51.00069

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Received: 15 Sep 2010; Published Online: 23 Sep 2010.

* Correspondence: Dr. Ralf Haefner, Max Planck Institute for Biological Cybernetics, NWG Bethge, Tübingen, Germany, ralf.haefner@gmail.com