Abstract
To study sensory neurons, the neuron response is plotted versus stimulus level. The aim of the present contribution is to determine how well two different levels of the incoming stimulation can be distinguished on the basis of their evoked responses. Two generic models of response function are presented and studied under the influence of noise. We show in these noisy cases that the most suitable signal, from the point of view of its identification, is not unique. To obtain the best identification we propose to use measures based on Fisher information. For these measures, we show that the most identifiable signal may differ from that derived when the noise is neglected.
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Lansky, P., Pokora, O., Rospars, JP. (2007). Stimulus-Response Curves in Sensory Neurons: How to Find the Stimulus Measurable with the Highest Precision. In: Mele, F., Ramella, G., Santillo, S., Ventriglia, F. (eds) Advances in Brain, Vision, and Artificial Intelligence. BVAI 2007. Lecture Notes in Computer Science, vol 4729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75555-5_32
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DOI: https://doi.org/10.1007/978-3-540-75555-5_32
Publisher Name: Springer, Berlin, Heidelberg
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